diff --git a/examples/diff-showcase-report.html b/examples/diff-showcase-report.html
index a65a546..0c48472 100644
--- a/examples/diff-showcase-report.html
+++ b/examples/diff-showcase-report.html
@@ -3197,7 +3197,7 @@ <h2>Project definition changed</h2>
 </section>
 </div>
 
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condition_description\n)","conditions_source":"conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n)","deduped":"deduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n)","final":"final as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n)","unknown_member":"unknown_member as (\n    select\n      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last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , 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}}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_patient_count__0.4c1de369ac"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_start_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_start_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_end_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_end_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: dim_conditions\n    description: |\n      Master condition dimension for healthcare analytics.\n      One row per unique ICD/SNOMED condition code, deduplicated from staging.\n      Use for filtering and grouping by condition across fact tables.\n\n      Use cases:\n      - Condition-based cohort analysis\n      - Disease prevalence tracking\n      - Condition frequency and distribution reporting\n      - Join target for condition-related facts\n\n    config:\n      tags: ['analytics', 'marts', 'dimension']\n    meta:\n      grain: condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Condition lookup and filtering\"\n        - \"Cohort analysis by diagnosis\"\n        - \"Disease prevalence reporting\"\n      example_queries:\n        - name: \"Top 10 most common conditions\"\n          description: \"Identify the most prevalent conditions in the patient population\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , patient_count\n                , encounter_count\n            from main_marts.dim_conditions\n            order by patient_count desc\n            limit 10\n\n        - name: \"Conditions by encounter frequency\"\n          description: \"Find conditions that drive the most healthcare utilization\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , encounter_count\n                , patient_count\n                , round(encounter_count * 1.0 / patient_count, 2) as avg_encounters_per_patient\n            from main_marts.dim_conditions\n            where patient_count > 0\n            order by encounter_count desc\n            limit 20\n\n        - name: \"Recently diagnosed conditions\"\n          description: \"Find conditions first diagnosed in the last year\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , first_occurrence_date\n                , last_occurrence_date\n                , patient_count\n            from main_marts.dim_conditions\n            where first_occurrence_date >= current_date - interval '1 year'\n            order by first_occurrence_date desc\n\n    columns:\n      - name: condition_key\n        description: Surrogate key (row_number)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: condition_code\n        description: ICD/SNOMED code (natural key)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: condition_description\n        description: Full condition name/description\n        data_tests:\n          - not_null\n\n      - name: first_occurrence_date\n        description: Earliest date this condition was diagnosed in the dataset\n\n      - name: last_occurrence_date\n        description: Most recent date this condition was diagnosed\n\n      - name: patient_count\n        description: Number of unique patients with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounter_count\n        description: Total number of encounters with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number \u003c= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day \u003c= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number \u003c= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day \u003c= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end 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)\n}}\n\n{#\n    Calendar dimension covering 1909-01-01 to 2025-12-31.\n\n    Note on date_key: Uses YYYYMMDD integer format. Valid range is years 0001-2147483\n    (integer max 2,147,483,647). Safe for all practical date ranges.\n\n    Note on day_of_week: dbt_date uses US convention (Sunday=1, Saturday=7),\n    NOT ISO 8601 (Monday=1). Weekend and holiday logic accounts for this.\n#}\n\nwith date_spine as (\n    {{ dbt_date.get_date_dimension('1909-01-01', '2025-12-31') }}\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_date\n    description: >\n      Calendar dimension covering 1909-01-01 to 2025-12-31.\n      Provides date attributes, day/week/month/quarter/year hierarchies,\n      weekend flags, and US holiday identification.\n    data_tests:\n      # Row count validation - covers 117 years of dates (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 42000\n          config:\n            severity: warn\n    columns:\n      - name: date_key\n        description: Surrogate key in YYYYMMDD integer format (e.g., 20240115)\n        data_tests:\n          - unique\n          - not_null\n          # Date key must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 19090101\n              max_value: 20251231\n              config:\n                severity: warn\n\n      - name: date_actual\n        description: The actual date value\n        data_tests:\n          - unique\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1909-01-01'::date\"\n              max_value: \"'2025-12-31'::date\"\n              config:\n                severity: warn\n\n      - name: day_of_week\n        description: Day of week number (US convention - Sunday=1, Saturday=7)\n        data_tests:\n          - not_null\n          # Day of week must be 1-7 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 7\n              config:\n                severity: warn\n\n      - name: day_name\n        description: Full day name (e.g., Monday, Tuesday)\n        data_tests:\n          - not_null\n\n      - name: day_name_short\n        description: Abbreviated day name (e.g., Mon, Tue)\n        data_tests:\n          - not_null\n\n      - name: day_of_month\n        description: Day of the month (1-31)\n        data_tests:\n          - not_null\n          # Day of month must be 1-31 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 31\n              config:\n                severity: warn\n\n      - name: day_of_year\n        description: Day of the year (1-366)\n        data_tests:\n          - not_null\n          # Day of year must be 1-366 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 366\n              config:\n                severity: warn\n\n      - name: week_of_year\n        description: Week of the year (1-53)\n        data_tests:\n          - not_null\n          # Week of year must be 1-53 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 53\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Month number (1-12)\n        data_tests:\n          - not_null\n          # Month must be 1-12 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 12\n              config:\n                severity: warn\n\n      - name: month_name\n        description: Full month name (e.g., January, February)\n        data_tests:\n          - not_null\n\n      - name: month_name_short\n        description: Abbreviated month name (e.g., Jan, Feb)\n        data_tests:\n          - not_null\n\n      - name: quarter_actual\n        description: Quarter number (1-4)\n        data_tests:\n          - not_null\n          # Quarter must be 1-4 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 4\n              config:\n                severity: warn\n\n      - name: quarter_name\n        description: Quarter name (Q1, Q2, Q3, Q4)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['Q1', 'Q2', 'Q3', 'Q4']\n\n      - name: year_actual\n        description: Year number\n        data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: is_weekend\n        description: Boolean flag indicating if date falls on Saturday or Sunday\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: is_us_holiday\n        description: Boolean flag indicating if date is a recognized US holiday\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: holiday_name\n        description: Name of the US holiday (null if not a holiday)"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_date","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_date_date_key.0869fd48f9"]},"evidence":[{"label":"unique_key","value":"date_key"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_organizations","path":"models/marts/core/dim_organizations.sql","description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_organizations.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":61,"col":1,"byte":1382},"end":{"line":61,"col":20,"byte":1401}},"final":{"start":{"line":55,"col":1,"byte":1293},"end":{"line":59,"col":2,"byte":1380}},"sequenced":{"start":{"line":7,"col":1,"byte":94},"end":{"line":35,"col":2,"byte":648}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":91}},"unknown_member":{"start":{"line":38,"col":1,"byte":708},"end":{"line":53,"col":2,"byte":1290}}},"column_spans":{"(final select)\u001f*":{"start":{"line":61,"col":8,"byte":1389},"end":{"line":61,"col":9,"byte":1390}},"sequenced\u001f_loaded_at":{"start":{"line":32,"col":11,"byte":598},"end":{"line":32,"col":42,"byte":629}},"sequenced\u001faddress":{"start":{"line":17,"col":11,"byte":373},"end":{"line":17,"col":18,"byte":380}},"sequenced\u001fcity":{"start":{"line":18,"col":11,"byte":391},"end":{"line":18,"col":15,"byte":395}},"sequenced\u001flatitude":{"start":{"line":24,"col":11,"byte":478},"end":{"line":24,"col":19,"byte":486}},"sequenced\u001flongitude":{"start":{"line":25,"col":11,"byte":497},"end":{"line":25,"col":20,"byte":506}},"sequenced\u001forganization_id":{"start":{"line":13,"col":11,"byte":296},"end":{"line":13,"col":26,"byte":311}},"sequenced\u001forganization_key":{"start":{"line":10,"col":9,"byte":197},"end":{"line":10,"col":73,"byte":261}},"sequenced\u001forganization_name":{"start":{"line":16,"col":11,"byte":345},"end":{"line":16,"col":28,"byte":362}},"sequenced\u001fphone":{"start":{"line":21,"col":11,"byte":441},"end":{"line":21,"col":16,"byte":446}},"sequenced\u001frevenue":{"start":{"line":28,"col":11,"byte":537},"end":{"line":28,"col":18,"byte":544}},"sequenced\u001fstate":{"start":{"line":19,"col":11,"byte":406},"end":{"line":19,"col":16,"byte":411}},"sequenced\u001futilization":{"start":{"line":29,"col":11,"byte":555},"end":{"line":29,"col":22,"byte":566}},"sequenced\u001fzip_code":{"start":{"line":20,"col":11,"byte":422},"end":{"line":20,"col":19,"byte":430}},"source\u001f*":{"start":{"line":4,"col":12,"byte":30},"end":{"line":4,"col":13,"byte":31}},"unknown_member\u001f_loaded_at":{"start":{"line":52,"col":11,"byte":1257},"end":{"line":52,"col":42,"byte":1288}},"unknown_member\u001faddress":{"start":{"line":43,"col":16,"byte":878},"end":{"line":43,"col":43,"byte":905}},"unknown_member\u001fcity":{"start":{"line":44,"col":16,"byte":921},"end":{"line":44,"col":40,"byte":945}},"unknown_member\u001flatitude":{"start":{"line":48,"col":16,"byte":1087},"end":{"line":48,"col":43,"byte":1114}},"unknown_member\u001flongitude":{"start":{"line":49,"col":16,"byte":1130},"end":{"line":49,"col":44,"byte":1158}},"unknown_member\u001forganization_id":{"start":{"line":41,"col":11,"byte":780},"end":{"line":41,"col":39,"byte":808}},"unknown_member\u001forganization_key":{"start":{"line":40,"col":10,"byte":748},"end":{"line":40,"col":31,"byte":769}},"unknown_member\u001forganization_name":{"start":{"line":42,"col":11,"byte":819},"end":{"line":42,"col":54,"byte":862}},"unknown_member\u001fphone":{"start":{"line":47,"col":16,"byte":1046},"end":{"line":47,"col":41,"byte":1071}},"unknown_member\u001frevenue":{"start":{"line":50,"col":16,"byte":1174},"end":{"line":50,"col":42,"byte":1200}},"unknown_member\u001fstate":{"start":{"line":45,"col":16,"byte":961},"end":{"line":45,"col":41,"byte":986}},"unknown_member\u001futilization":{"start":{"line":51,"col":16,"byte":1216},"end":{"line":51,"col":46,"byte":1246}},"unknown_member\u001fzip_code":{"start":{"line":46,"col":16,"byte":1002},"end":{"line":46,"col":44,"byte":1030}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"city":{"description":"City","documented":true},"latitude":{"description":"Latitude coordinate","documented":true},"longitude":{"description":"Longitude coordinate","documented":true},"organization_id":{"description":"Natural organization identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_id.8ca9bc4237"}],"documented":true},"organization_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_key.a539e5f42a"},{"kind":"unique","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"}],"documented":true},"organization_name":{"description":"Name of the healthcare organization","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_name","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_name.7fb64e1577"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"revenue","min_value":0,"model":"{{ get_where_subquery(ref('dim_organizations')) }}","row_condition":"revenue is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_organizations_revenue__0__revenue_is_not_null.3e63617295"}],"documented":true},"state":{"description":"State (full state 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='organization_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_organizations\n    description: >\n      Organization dimension containing healthcare facilities.\n      Includes an unknown member row (organization_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 800\n          config:\n            severity: warn\n    columns:\n      - name: organization_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: organization_id\n        description: Natural organization identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: organization_name\n        description: Name of the healthcare organization\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State (full state name)\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: latitude\n        description: Latitude coordinate\n\n      - name: longitude\n        description: Longitude coordinate\n\n      - name: revenue\n        description: Total revenue\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: utilization\n        description: Total utilization count\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"utilization is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"]},"evidence":[{"label":"unique_key","value":"organization_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__organizations')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_patients","path":"models/marts/core/dim_patients.sql","description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","dag":{"nodes":[{"id":"snapshot_source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_patients.sql","role":"final"}],"edges":[{"from":"snapshot_source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\n-- depends_on: \"memory\".\"snapshots\".\"snp_patients\"\n-- depends_on: \"memory\".\"main_staging\".\"stg_synthea__patients\"\n\n\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final 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Each row represents a\n    specific version of a patient's record.\n\n    SCD Type 2 Columns:\n    - patient_key: Surrogate key (unique per version)\n    - patient_id: Natural key (same across versions)\n    - valid_from: When this version became active\n    - valid_to: When this version was superseded (null = current)\n    - is_current: True for the active version\n\n    To query current patients only:\n        WHERE is_current = true\n\n    To query patient state at a point in time:\n        WHERE valid_from \u003c= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_patients\n    description: >\n      Patient dimension with SCD Type 2 history tracking.\n      Each row represents a specific version of a patient's record.\n      Includes an unknown member row (patient_key = -1) for handling missing lookups.\n\n      SCD Type 2 columns: patient_key (surrogate), patient_id (natural),\n      valid_from, valid_to, is_current.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 900\n          config:\n            severity: warn\n    columns:\n      - name: patient_key\n        description: Surrogate key (unique per version, -1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_id\n        description: Natural patient identifier (UUID), same across SCD2 versions\n        data_tests:\n          - not_null\n\n      - name: first_name\n        description: Patient's first name\n        data_tests:\n          - not_null\n\n      - name: last_name\n        description: Patient's last name\n        data_tests:\n          - not_null\n\n      - name: full_name\n        description: Patient's full name (first + last)\n        data_tests:\n          - not_null\n\n      - name: name_prefix\n        description: Name prefix (Mr., Mrs., Ms., etc.)\n\n      - name: name_suffix\n        description: Name suffix (Jr., Sr., III, etc.)\n\n      - name: birth_date\n        description: Patient date of birth\n        data_tests:\n          # Date range validation - exclude unknown member (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"birth_date is not null and patient_key != -1\"\n              config:\n                severity: warn\n\n      - name: death_date\n        description: Patient date of death (null if alive)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"death_date is not null\"\n              config:\n                severity: warn\n\n      - name: age_years\n        description: Current age or age at death (in years)\n        data_tests:\n          # Age must be reasonable 0-150 years (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 150\n              row_condition: \"age_years is not null\"\n              config:\n                severity: warn\n\n      - name: is_deceased\n        description: Boolean flag indicating if patient has died\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: gender\n        description: Patient gender (M or F)\n        data_tests:\n          - accepted_values:\n              values: ['M', 'F']\n              config:\n                where: \"patient_key != -1\"\n\n      - name: race\n        description: Patient race\n\n      - name: ethnicity\n        description: Patient ethnicity (hispanic or nonhispanic)\n\n      - name: marital_status\n        description: Marital status (M=Married, S=Single, or blank)\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City of residence\n\n      - name: state\n        description: State of residence (full state name)\n\n      - name: county\n        description: County of residence\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: latitude\n        description: Latitude coordinate of residence\n\n      - name: longitude\n        description: Longitude coordinate of residence\n\n      - name: healthcare_expenses\n        description: Total healthcare expenses incurred\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_expenses is not null\"\n              config:\n                severity: warn\n\n      - name: healthcare_coverage\n        description: Total healthcare coverage received\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: valid_from\n        description: Date when this SCD2 version became active\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: valid_to\n        description: Date when this SCD2 version was superseded (null = current)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"valid_to is not null\"\n              config:\n                severity: warn\n\n      - name: is_current\n        description: Boolean flag indicating if this is the current version\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final 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This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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_member\u001funcovered_encounters":{"start":{"line":71,"col":16,"byte":1815},"end":{"line":71,"col":55,"byte":1854}},"unknown_member\u001funcovered_immunizations":{"start":{"line":77,"col":16,"byte":2144},"end":{"line":77,"col":58,"byte":2186}},"unknown_member\u001funcovered_medications":{"start":{"line":73,"col":16,"byte":1924},"end":{"line":73,"col":56,"byte":1964}},"unknown_member\u001funcovered_procedures":{"start":{"line":75,"col":16,"byte":2033},"end":{"line":75,"col":55,"byte":2072}},"unknown_member\u001funique_customers":{"start":{"line":78,"col":16,"byte":2202},"end":{"line":78,"col":51,"byte":2237}},"unknown_member\u001fzip_code":{"start":{"line":65,"col":16,"byte":1535},"end":{"line":65,"col":44,"byte":1563}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total amount)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_rate","max_value":1,"min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"coverage_rate is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_coverage_rate__1__0__coverage_rate_is_not_null.8999cddc49"}],"documented":true},"covered_encounters":{"description":"Number of encounters covered","documented":true},"covered_immunizations":{"description":"Number of immunizations covered","documented":true},"covered_medications":{"description":"Number of medications covered","documented":true},"covered_procedures":{"description":"Number of procedures covered","documented":true},"member_months":{"description":"Total member months","documented":true},"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_id","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue collected","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"revenue","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"revenue is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_revenue__0__revenue_is_not_null.0bff623461"}],"documented":true},"state":{"description":"State","documented":true},"uncovered_encounters":{"description":"Number of encounters not covered","documented":true},"uncovered_immunizations":{"description":"Number of immunizations not covered","documented":true},"uncovered_medications":{"description":"Number of medications not covered","documented":true},"uncovered_procedures":{"description":"Number of procedures not covered","documented":true},"unique_customers":{"description":"Number of unique customers","documented":true},"zip_code":{"description":"ZIP 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_payers\n    description: >\n      Payer dimension containing insurance payers.\n      Includes an unknown member row (payer_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 5\n          config:\n            severity: warn\n    columns:\n      - name: payer_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: payer_id\n        description: Natural payer identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: payer_name\n        description: Name of the insurance payer\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: amount_covered\n        description: Total amount covered by payer\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"amount_covered is not null\"\n              config:\n                severity: warn\n\n      - name: amount_uncovered\n        description: Total amount not covered by payer\n\n      - name: revenue\n        description: Total revenue collected\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: covered_encounters\n        description: Number of encounters covered\n\n      - name: uncovered_encounters\n        description: Number of encounters not covered\n\n      - name: covered_medications\n        description: Number of medications covered\n\n      - name: uncovered_medications\n        description: Number of medications not covered\n\n      - name: covered_procedures\n        description: Number of procedures covered\n\n      - name: uncovered_procedures\n        description: Number of procedures not covered\n\n      - name: covered_immunizations\n        description: Number of immunizations covered\n\n      - name: uncovered_immunizations\n        description: Number of immunizations not covered\n\n      - name: unique_customers\n        description: Number of unique customers\n\n      - name: average_quality_of_life_score\n        description: Average quality of life score for members\n\n      - name: member_months\n        description: Total member months\n\n      - name: coverage_rate\n        description: Derived coverage rate (amount_covered / total amount)\n        data_tests:\n          # Coverage rate must be 0-1 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 1\n              row_condition: \"coverage_rate is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt's authoring-YAML drawer (since #69) attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test to the test's authored slice. This block demonstrates the\n  # LEADING bracket — useful for stating the test's intent before the\n  # reader sees the assertion (cute-dbt#73).\n  - name: test_dim_payers_injects_unknown_sentinel\n    description: |\n      dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\n      with the sequenced source rows. This test verifies the sentinel\n      survives the union and the sequenced rows are surrogate-keyed\n      starting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\n      dict format only compares listed columns, so non-deterministic\n      outputs (`_loaded_at` via `current_timestamp`) are excluded from\n      assertion automatically.\n    model: dim_payers\n    # The INSIDE bracket — comments at deeper indent than `- name:` ride\n    # along with the body slice as raw text. Cute-dbt preserves them\n    # verbatim in the Authoring YAML drawer (cute-dbt#73).\n    given:\n      - input: ref('stg_synthea__payers')\n        format: dict\n        rows:\n          - payer_id: 'a00000aa-0000-0000-0000-000000000001'\n            payer_name: 'Acme Health'\n            address: '1 Acme Way'\n            city: 'Boston'\n            state: 'MA'\n            zip_code: '02101'\n            phone: '555-0100'\n            amount_covered: 1000.0\n            amount_uncovered: 250.0\n            revenue: 1250.0\n            covered_encounters: 10\n            uncovered_encounters: 2\n            covered_medications: 5\n            uncovered_medications: 1\n            covered_procedures: 3\n            uncovered_procedures: 0\n            covered_immunizations: 4\n            uncovered_immunizations: 0\n            unique_customers: 8\n            average_quality_of_life_score: 0.85\n            member_months: 96\n    expect:\n      format: dict\n      rows:\n        - payer_key: -1\n          payer_id: 'UNKNOWN'\n          payer_name: 'Unknown Payer'\n        - payer_key: 1\n          payer_id: 'a00000aa-0000-0000-0000-000000000001'\n          payer_name: 'Acme Health'\n  # The TRAILING bracket — contiguous `#`-comment lines at the SAME\n  # indent as `- name:`, immediately following the test body and\n  # terminated by EOF or a blank line, are attached to this test's\n  # slice. Useful for cross-references and follow-up notes (cute-dbt#73).","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme Way"}},{"display":"1000.0","key":{"t":"number","v":"1000"}},{"display":"250.0","key":{"t":"number","v":"250"}},{"display":"0.85","key":{"t":"number","v":"0.85"}},{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"10","key":{"t":"number","v":"10"}},{"display":"4","key":{"t":"number","v":"4"}},{"display":"5","key":{"t":"number","v":"5"}},{"display":"3","key":{"t":"number","v":"3"}},{"display":"96","key":{"t":"number","v":"96"}},{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}},{"display":"555-0100","key":{"t":"str","v":"555-0100"}},{"display":"1250.0","key":{"t":"number","v":"1250"}},{"display":"MA","key":{"t":"str","v":"MA"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"8","key":{"t":"number","v":"8"}},{"display":"02101","key":{"t":"str","v":"02101"}}]}]},"rows":[{"address":"1 Acme Way","amount_covered":1000.0,"amount_uncovered":250.0,"average_quality_of_life_score":0.85,"city":"Boston","covered_encounters":10,"covered_immunizations":4,"covered_medications":5,"covered_procedures":3,"member_months":96,"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_name":"Acme Health","phone":"555-0100","revenue":1250.0,"state":"MA","uncovered_encounters":2,"uncovered_immunizations":0,"uncovered_medications":1,"uncovered_procedures":0,"unique_customers":8,"zip_code":"02101"}],"format":"dict","column_meta":{"address":{"description":"Street address"},"amount_covered":{"description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"amount_uncovered":{"description":"Total amount not covered by payer"},"average_quality_of_life_score":{"description":"Average quality of life score for members"},"city":{"description":"City"},"covered_encounters":{"description":"Number of encounters covered"},"covered_immunizations":{"description":"Number of immunizations covered"},"covered_medications":{"description":"Number of medications covered"},"covered_procedures":{"description":"Number of procedures covered"},"member_months":{"description":"Total member months"},"payer_id":{"description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]},"phone":{"description":"Phone number"},"revenue":{"description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"state":{"description":"State where headquartered"},"uncovered_encounters":{"description":"Number of encounters not covered"},"uncovered_immunizations":{"description":"Number of immunizations not covered"},"uncovered_medications":{"description":"Number of medications not covered"},"uncovered_procedures":{"description":"Number of procedures not covered"},"unique_customers":{"description":"Number of unique customers"},"zip_code":{"description":"ZIP code (stored as numeric, 5 digits)"}}}],"expected":{"table":{"columns":["payer_id","payer_key","payer_name"],"rows":[{"cells":[{"display":"UNKNOWN","key":{"t":"str","v":"UNKNOWN"}},{"display":"-1","key":{"t":"number","v":"-1"}},{"display":"Unknown Payer","key":{"t":"str","v":"Unknown Payer"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}}]}]},"rows":[{"payer_id":"UNKNOWN","payer_key":-1,"payer_name":"Unknown Payer"},{"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_key":1,"payer_name":"Acme Health"}],"format":"dict","column_meta":{"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_payers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"]},"evidence":[{"label":"unique_key","value":"payer_key"}],"degraded":[{"by":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2","causes":["severity: warn — a failing run warns instead of failing, so this backing is advisory, not gating"]}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_payers","construct":"union[final]","verdict":{"status":"unknown"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"suppressed":{"source":"pragma","reason":"unknown_member is a constant sentinel arm, exercised by design"},"pin_node":"final"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_providers","path":"models/marts/core/dim_providers.sql","description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). Includes an unknown member row (provider_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_providers.sql","role":"final"}],"edges":[{"from":"providers","to":"sequenced","edge_type":"from"},{"from":"organizations","to":"sequenced","edge_type":"left"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","providers":"providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n)","unknown_member":"unknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":76,"col":1,"byte":1766},"end":{"line":76,"col":20,"byte":1785}},"final":{"start":{"line":70,"col":1,"byte":1677},"end":{"line":74,"col":2,"byte":1764}},"organizations":{"start":{"line":7,"col":1,"byte":93},"end":{"line":12,"col":2,"byte":226}},"providers":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":90}},"sequenced":{"start":{"line":14,"col":1,"byte":229},"end":{"line":48,"col":2,"byte":972}},"unknown_member":{"start":{"line":51,"col":1,"byte":1032},"end":{"line":68,"col":2,"byte":1674}}},"column_spans":{"(final select)\u001f*":{"start":{"line":76,"col":8,"byte":1773},"end":{"line":76,"col":9,"byte":1774}},"organizations\u001forganization_id":{"start":{"line":10,"col":11,"byte":158},"end":{"line":10,"col":26,"byte":173}},"organizations\u001forganization_key":{"start":{"line":9,"col":9,"byte":131},"end":{"line":9,"col":25,"byte":147}},"providers\u001f*":{"start":{"line":4,"col":12,"byte":33},"end":{"line":4,"col":13,"byte":34}},"sequenced\u001f_loaded_at":{"start":{"line":43,"col":11,"byte":838},"end":{"line":43,"col":42,"byte":869}},"sequenced\u001faddress":{"start":{"line":32,"col":11,"byte":654},"end":{"line":32,"col":20,"byte":663}},"sequenced\u001fcity":{"start":{"line":33,"col":11,"byte":674},"end":{"line":33,"col":17,"byte":680}},"sequenced\u001fgender":{"start":{"line":28,"col":11,"byte":592},"end":{"line":28,"col":19,"byte":600}},"sequenced\u001flatitude":{"start":{"line":36,"col":11,"byte":730},"end":{"line":36,"col":21,"byte":740}},"sequenced\u001flongitude":{"start":{"line":37,"col":11,"byte":751},"end":{"line":37,"col":22,"byte":762}},"sequenced\u001forganization_id":{"start":{"line":24,"col":11,"byte":515},"end":{"line":24,"col":28,"byte":532}},"sequenced\u001forganization_key":{"start":{"line":23,"col":11,"byte":486},"end":{"line":23,"col":29,"byte":504}},"sequenced\u001fprovider_id":{"start":{"line":20,"col":11,"byte":425},"end":{"line":20,"col":24,"byte":438}},"sequenced\u001fprovider_key":{"start":{"line":17,"col":9,"byte":332},"end":{"line":17,"col":67,"byte":390}},"sequenced\u001fprovider_name":{"start":{"line":27,"col":11,"byte":566},"end":{"line":27,"col":26,"byte":581}},"sequenced\u001fspecialty":{"start":{"line":29,"col":11,"byte":611},"end":{"line":29,"col":22,"byte":622}},"sequenced\u001fstate":{"start":{"line":34,"col":11,"byte":691},"end":{"line":34,"col":18,"byte":698}},"sequenced\u001futilization":{"start":{"line":40,"col":11,"byte":793},"end":{"line":40,"col":24,"byte":806}},"sequenced\u001fzip_code":{"start":{"line":35,"col":11,"byte":709},"end":{"line":35,"col":21,"byte":719}},"unknown_member\u001f_loaded_at":{"start":{"line":67,"col":11,"byte":1641},"end":{"line":67,"col":42,"byte":1672}},"unknown_member\u001faddress":{"start":{"line":60,"col":16,"byte":1345},"end":{"line":60,"col":43,"byte":1372}},"unknown_member\u001fcity":{"start":{"line":61,"col":16,"byte":1388},"end":{"line":61,"col":40,"byte":1412}},"unknown_member\u001fgender":{"start":{"line":58,"col":16,"byte":1258},"end":{"line":58,"col":42,"byte":1284}},"unknown_member\u001flatitude":{"start":{"line":64,"col":16,"byte":1513},"end":{"line":64,"col":43,"byte":1540}},"unknown_member\u001flongitude":{"start":{"line":65,"col":16,"byte":1556},"end":{"line":65,"col":44,"byte":1584}},"unknown_member\u001forganization_id":{"start":{"line":56,"col":11,"byte":1168},"end":{"line":56,"col":39,"byte":1196}},"unknown_member\u001forganization_key":{"start":{"line":55,"col":12,"byte":1136},"end":{"line":55,"col":33,"byte":1157}},"unknown_member\u001fprovider_id":{"start":{"line":54,"col":11,"byte":1100},"end":{"line":54,"col":35,"byte":1124}},"unknown_member\u001fprovider_key":{"start":{"line":53,"col":10,"byte":1072},"end":{"line":53,"col":27,"byte":1089}},"unknown_member\u001fprovider_name":{"start":{"line":57,"col":11,"byte":1207},"end":{"line":57,"col":46,"byte":1242}},"unknown_member\u001fspecialty":{"start":{"line":59,"col":16,"byte":1300},"end":{"line":59,"col":45,"byte":1329}},"unknown_member\u001fstate":{"start":{"line":62,"col":16,"byte":1428},"end":{"line":62,"col":41,"byte":1453}},"unknown_member\u001futilization":{"start":{"line":66,"col":16,"byte":1600},"end":{"line":66,"col":46,"byte":1630}},"unknown_member\u001fzip_code":{"start":{"line":63,"col":16,"byte":1469},"end":{"line":63,"col":44,"byte":1497}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"city":{"description":"City","documented":true},"gender":{"description":"Provider gender (M or F)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"gender","model":"{{ get_where_subquery(ref('dim_providers')) }}","values":["M","F"]},"node_id":"test.healthcare_analytics.accepted_values_dim_providers_gender__M__F.248d04b44f"}],"documented":true},"latitude":{"description":"Latitude coordinate","documented":true},"longitude":{"description":"Longitude coordinate","documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_organization_id.c650c07852"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_organization_key.d5b2be228b"},{"kind":"relationships","kwargs":{"column_name":"organization_key","field":"organization_key","model":"{{ get_where_subquery(ref('dim_providers')) }}","to":"ref('dim_organizations')"},"node_id":"test.healthcare_analytics.relationships_dim_providers_organization_key__organization_key__ref_dim_organizations_.905f82d6ac"}],"documented":true},"provider_id":{"description":"Natural provider identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"provider_id","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_provider_id.2f5a85b6ff"}],"documented":true},"provider_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"provider_key","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_provider_key.9a2c91be87"},{"kind":"unique","kwargs":{"column_name":"provider_key","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"}],"documented":true},"provider_name":{"description":"Full name of the provider","tests":[{"kind":"not_null","kwargs":{"column_name":"provider_name","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_provider_name.e2289fc3be"}],"documented":true},"specialty":{"description":"Medical specialty","documented":true},"state":{"description":"State (full state name)","documented":true},"utilization":{"description":"Total utilization count","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"utilization","min_value":0,"model":"{{ get_where_subquery(ref('dim_providers')) }}","row_condition":"utilization is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_providers_utilization__0__utilization_is_not_null.175a27ff73"}],"documented":true},"zip_code":{"description":"ZIP code","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__providers"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"providers"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"dim_organizations"}},"column":"organization_key"},"to_col":{"scope":{"intra":{"node_id":"organizations"}},"column":"organization_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_organizations"}},"column":"organization_id"},"to_col":{"scope":{"intra":{"node_id":"organizations"}},"column":"organization_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"provider_id"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"provider_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"organizations"}},"column":"organization_key"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"organization_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"organization_id"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"organization_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"provider_name"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"provider_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"gender"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"gender"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"specialty"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"specialty"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"address"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"address"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"zip_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"zip_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"latitude"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"latitude"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"longitude"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"longitude"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"utilization"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"utilization"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='provider_key'\n    )\n}}\n\nwith providers as (\n    select * from {{ ref('stg_synthea__providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from {{ ref('dim_organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_providers\n    description: >\n      Provider dimension containing healthcare providers (doctors, nurses, etc.).\n      Includes an unknown member row (provider_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 4000\n          config:\n            severity: warn\n    columns:\n      - name: provider_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_id\n        description: Natural provider identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_organizations')\n              field: organization_key\n\n      - name: organization_id\n        description: Natural organization identifier\n        data_tests:\n          - not_null\n\n      - name: provider_name\n        description: Full name of the provider\n        data_tests:\n          - not_null\n\n      - name: gender\n        description: Provider gender (M or F)\n        data_tests:\n          - accepted_values:\n              values: ['M', 'F']\n              config:\n                where: \"provider_key != -1\"\n\n      - name: specialty\n        description: Medical specialty\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State (full state name)\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: latitude\n        description: Latitude coordinate\n\n      - name: longitude\n        description: Longitude coordinate\n\n      - name: utilization\n        description: Total utilization count\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"utilization is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_providers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"]},"evidence":[{"label":"unique_key","value":"provider_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_providers","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('dim_organizations'), ref('stg_synthea__providers')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","dag":{"nodes":[{"id":"valid_encounters","role":"import"},{"id":"conditions","role":"import"},{"id":"medications","role":"import"},{"id":"procedures","role":"import"},{"id":"all_events","role":"transform"},{"id":"patients","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_clinical_events.sql","role":"final"}],"edges":[{"from":"conditions","to":"all_events","edge_type":"union_all"},{"from":"medications","to":"all_events","edge_type":"union_all"},{"from":"procedures","to":"all_events","edge_type":"union_all"},{"from":"all_events","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"encounters","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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unique_key='clinical_event_key'\n    )\n}}\n\nwith valid_encounters as (\n    select encounter_id\n    from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from {{ ref('stg_synthea__conditions') }} as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from {{ ref('stg_synthea__medications') }} as med\n    {{ quarantine_filter() }}\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from {{ ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_clinical_events\n    description: >\n      Unified clinical events fact table combining conditions, medications, and procedures.\n      Each row represents a single clinical event with standardized structure.\n      Links to patient and encounter dimensions.\n    data_tests:\n      # Row count validation - should be sum of conditions + medications + procedures (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 60000\n          config:\n            severity: warn\n    columns:\n      - name: clinical_event_key\n        description: Surrogate key for the clinical event\n        data_tests:\n          - unique\n          - not_null\n\n      - name: event_type\n        description: Type of clinical event (CONDITION, MEDICATION, PROCEDURE)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['CONDITION', 'MEDICATION', 'PROCEDURE']\n\n      - name: event_id\n        description: Natural event identifier (condition_id, medication_id, or procedure_id)\n        data_tests:\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('fct_encounters')\n              field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - not_null\n\n      - name: event_date_key\n        description: Foreign key to dim_date for event start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"event_date_key != -1\"\n\n      - name: event_start_date\n        description: Start date of the clinical event\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: event_end_date\n        description: End date of the clinical event (null if ongoing or one-time)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"event_end_date is not null\"\n              config:\n                severity: warn\n\n      - name: code\n        description: Clinical code (SNOMED-CT or RxNorm)\n        data_tests:\n          - not_null\n\n      - name: code_system\n        description: Code system (SNOMED-CT or RXNORM)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['SNOMED-CT', 'RXNORM']\n\n      - name: description\n        description: Description of the clinical event\n        data_tests:\n          - not_null\n\n      - name: reason_code\n        description: Clinical code for the reason of the event\n\n      - name: reason_description\n        description: Description of the reason for the event\n\n      - name: event_cost\n        description: Cost of the clinical event\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"event_cost is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_condition_cohorts","path":"models/marts/analytics/fct_condition_cohorts.sql","description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"patients","role":"import"},{"id":"dim_conditions","role":"import"},{"id":"condition_windows","role":"transform"},{"id":"with_dimensions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_condition_cohorts.sql","role":"final"}],"edges":[{"from":"conditions_source","to":"condition_windows","edge_type":"from"},{"from":"condition_windows","to":"with_dimensions","edge_type":"from"},{"from":"patients","to":"with_dimensions","edge_type":"left"},{"from":"dim_conditions","to":"with_dimensions","edge_type":"left"},{"from":"with_dimensions","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_windows":"condition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n)","conditions_source":"conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n)","dim_conditions":"dim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)","patients":"patients as (\n    select\n        patient_key\n        , 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condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","node_spans":{"(final 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unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_condition_cohorts\n    description: |\n      Patient-condition cohort analysis for disease management and population health.\n      One row per unique patient-condition combination with timeline and encounter details.\n      Grain: (patient_id, condition_code)\n\n      Use cases:\n      - Disease management program enrollment\n      - Cohort identification for clinical trials\n      - Chronic condition tracking\n      - Care gap analysis\n      - Risk stratification by condition\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: patient_id + condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Disease management programs\"\n        - \"Cohort identification\"\n        - \"Chronic condition tracking\"\n        - \"Care gap analysis\"\n      example_queries:\n        - name: \"Largest condition cohorts\"\n          description: \"Find conditions with the most patients for population health programs\"\n          sql: |\n            select\n                fcc.condition_code\n                , fcc.condition_description\n                , count(distinct fcc.patient_id) as patient_count\n                , sum(fcc.encounter_count) as total_encounters\n                , round(avg(fcc.months_with_condition), 1) as avg_months_with_condition\n                , sum(case when fcc.is_active then 1 else 0 end) as active_patients\n            from main_marts.fct_condition_cohorts fcc\n            group by 1, 2\n            order by patient_count desc\n            limit 15\n\n        - name: \"Chronic condition patients (long duration)\"\n          description: \"Identify patients with long-standing conditions for care management\"\n          sql: |\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , fcc.condition_description\n                , fcc.first_diagnosis_date\n                , fcc.months_with_condition\n                , fcc.encounter_count\n                , fcc.is_active\n            from main_marts.fct_condition_cohorts fcc\n            join main_marts.dim_patients p\n                on fcc.patient_key = p.patient_key\n            where fcc.months_with_condition >= 24\n                and fcc.is_active = true\n            order by fcc.months_with_condition desc\n            limit 50\n\n        - name: \"Patients with multiple conditions\"\n          description: \"Find patients with comorbidities for complex care programs\"\n          sql: |\n            with patient_condition_counts as (\n                select\n                    patient_id\n                    , patient_key\n                    , count(distinct condition_code) as condition_count\n                    , sum(case when is_active then 1 else 0 end) as active_condition_count\n                from main_marts.fct_condition_cohorts\n                group by 1, 2\n            )\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , pcc.condition_count\n                , pcc.active_condition_count\n            from patient_condition_counts pcc\n            join main_marts.dim_patients p\n                on pcc.patient_key = p.patient_key\n            where pcc.condition_count >= 3\n            order by pcc.condition_count desc\n            limit 25\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - patient_id\n              - condition_code\n          config:\n            severity: error\n    columns:\n      - name: condition_cohort_key\n        description: Surrogate key (MD5 hash of patient_id + condition_code)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_patients')\n                field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: condition_key\n        description: Foreign key to dim_conditions\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_conditions')\n                field: condition_key\n\n      - name: condition_code\n        description: ICD/SNOMED condition code\n        data_tests:\n          - not_null\n\n      - name: condition_description\n        description: Full condition name\n\n      - name: first_diagnosis_date\n        description: Date of first diagnosis\n        data_tests:\n          - not_null\n\n      - name: last_diagnosis_date\n        description: Date of most recent diagnosis (null if first occurrence only)\n\n      - name: first_diagnosis_year\n        description: Year of first diagnosis\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: months_with_condition\n        description: Months between first and last diagnosis (0 if single occurrence)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounter_count\n        description: Number of encounters with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: is_active\n        description: True if condition has no end date (currently active)\n        data_tests:\n          - not_null\n          - accepted_values:\n              arguments:\n                values: [true, false]\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from 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select)"}},"column":"coverage_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_cost_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_cost_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"procedure_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"procedure_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"cost_per_procedure"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"cost_per_procedure"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"_loaded_at"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"_loaded_at"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_cost_analysis\n    description: |\n      Detailed cost breakdown for financial analysis and revenue cycle management.\n      One row per encounter with cost allocation details across payer and patient.\n      Grain: encounter_id\n\n      Use cases:\n      - Financial performance reporting\n      - Payer mix analysis\n      - Patient out-of-pocket tracking\n      - Cost driver identification\n      - Revenue cycle optimization\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: encounter_id\n      owner: healthcare_analytics\n      uses:\n        - \"Financial reporting\"\n        - \"Payer mix analysis\"\n        - \"Cost allocation tracking\"\n        - \"Revenue cycle management\"\n      example_queries:\n        - name: \"Cost breakdown by payer\"\n          description: \"Analyze cost distribution across different insurance payers\"\n          sql: |\n            select\n                py.payer_name\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(sum(fca.payer_coverage), 2) as total_payer_coverage\n                , round(sum(fca.patient_responsibility), 2) as total_patient_responsibility\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            join main_marts.dim_payers py\n                on fca.payer_key = py.payer_key\n            group by py.payer_name\n            order by total_cost desc\n\n        - name: \"Monthly cost trends\"\n          description: \"Track cost trends over time for financial forecasting\"\n          sql: |\n            select\n                strftime(fca.encounter_date, '%Y-%m') as year_month\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(avg(fca.total_cost), 2) as avg_cost_per_encounter\n                , round(sum(fca.payer_coverage), 2) as total_payer_coverage\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            where fca.year_actual >= 2023\n            group by 1\n            order by 1 desc\n\n        - name: \"Cost by encounter type\"\n          description: \"Compare costs across different encounter classes\"\n          sql: |\n            select\n                fca.encounter_class\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(avg(fca.total_cost), 2) as avg_cost\n                , round(avg(fca.procedure_count), 2) as avg_procedures\n                , round(avg(fca.cost_per_procedure), 2) as avg_cost_per_procedure\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            group by fca.encounter_class\n            order by total_cost desc\n\n        - name: \"High-cost encounters\"\n          description: \"Identify individual high-cost encounters for review\"\n          sql: |\n            select\n                fca.encounter_id\n                , fca.encounter_date\n                , fca.encounter_class\n                , p.first_name || ' ' || p.last_name as patient_name\n                , py.payer_name\n                , fca.total_cost\n                , fca.payer_coverage\n                , fca.patient_responsibility\n                , fca.procedure_count\n            from main_marts.fct_cost_analysis fca\n            join main_marts.dim_patients p\n                on fca.patient_key = p.patient_key\n            join main_marts.dim_payers py\n                on fca.payer_key = py.payer_key\n            order by fca.total_cost desc\n            limit 25\n\n    columns:\n      - name: cost_analysis_key\n        description: Surrogate key (MD5 hash of encounter_id)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('fct_encounters')\n                field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n\n      - name: patient_id\n        description: Natural patient identifier\n\n      - name: payer_key\n        description: Foreign key to dim_payers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_payers')\n                field: payer_key\n\n      - name: payer_id\n        description: Natural payer identifier\n\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_providers')\n                field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n\n      - name: encounter_date_key\n        description: Foreign key to dim_date\n\n      - name: encounter_date\n        description: Date of the encounter\n\n      - name: year_actual\n        description: Calendar year of encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter (ambulatory, wellness, etc.)\n\n      - name: total_cost\n        description: Total claim cost\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: patient_responsibility\n        description: Amount owed by patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: coverage_pct\n        description: Percentage covered by insurance (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: patient_cost_pct\n        description: Percentage owed by patient (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: procedure_count\n        description: Number of procedures in the encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: cost_per_procedure\n        description: Average cost per procedure (null if no procedures)\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_cost_analysis_cost_analysis_key.2aecf62159"]},"evidence":[{"label":"unique_key","value":"cost_analysis_key"}],"pin_node":"(final select)"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:dim_date]","verdict":{"status":"unknown"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN dim_date ON encounters.encounter_start_date_key = dim_date.date_key"},{"label":"unattributable join","value":"encounters_enriched — join key or side binding not statically recoverable"}],"pin_node":"encounters_enriched"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:procedures]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN procedures ON encounters.encounter_id = procedures.encounter_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"encounters_enriched","sketches":["- input: ref('fct_encounters')\n  rows:\n    - {encounter_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__procedures')\n  rows:\n    - {encounter_id: 1}\n# expect: the no-match row with NULL stg_synthea__procedures columns (or the intended fallback)"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"patients","role":"import"},{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"payers","role":"import"},{"id":"dim_date","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"providers","to":"final","edge_type":"left"},{"from":"organizations","to":"final","edge_type":"left"},{"from":"payers","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final select)\u001f*":{"start":{"line":109,"col":8,"byte":2736},"end":{"line":109,"col":9,"byte":2737}},"dim_date\u001fdate_actual":{"start":{"line":45,"col":11,"byte":721},"end":{"line":45,"col":22,"byte":732}},"dim_date\u001fdate_key":{"start":{"line":44,"col":9,"byte":702},"end":{"line":44,"col":17,"byte":710}},"encounters\u001f*":{"start":{"line":4,"col":12,"byte":34},"end":{"line":4,"col":13,"byte":35}},"final\u001f_loaded_at":{"start":{"line":98,"col":11,"byte":2250},"end":{"line":98,"col":42,"byte":2281}},"final\u001fbase_encounter_cost":{"start":{"line":86,"col":11,"byte":1836},"end":{"line":86,"col":32,"byte":1857}},"final\u001fduration_minutes":{"start":{"line":83,"col":11,"byte":1726},"end":{"line":83,"col":92,"byte":1807}},"final\u001fencounter_class":{"start":{"line":72,"col":11,"byte":1470},"end":{"line":72,"col":28,"byte":1487}},"final\u001fencounter_code":{"start":{"line":73,"col":11,"byte":1498},"end":{"line":73,"col":27,"byte":1514}},"final\u001fencounter_description":{"start":{"line":74,"col":11,"byte":1525},"end":{"line":74,"col":34,"byte":1548}},"final\u001fencounter_end_at":{"start":{"line":80,"col":11,"byte":1668},"end":{"line":80,"col":29,"byte":1686}},"final\u001fencounter_end_date_key":{"start":{"line":69,"col":11,"byte":1385},"end":{"line":69,"col":62,"byte":1436}},"final\u001fencounter_id":{"start":{"line":55,"col":11,"byte":929},"end":{"line":55,"col":25,"byte":943}},"final\u001fencounter_key":{"start":{"line":52,"col":9,"byte":834},"end":{"line":52,"col":69,"byte":894}},"final\u001fencounter_start_at":{"start":{"line":79,"col":11,"byte":1637},"end":{"line":79,"col":31,"byte":1657}},"final\u001fencounter_start_date_key":{"start":{"line":68,"col":11,"byte":1321},"end":{"line":68,"col":64,"byte":1374}},"final\u001forganization_id":{"start":{"line":63,"col":11,"byte":1200},"end":{"line":63,"col":28,"byte":1217}},"final\u001forganization_key":{"start":{"line":62,"col":11,"byte":1137},"end":{"line":62,"col":63,"byte":1189}},"final\u001fpatient_age_at_encounter":{"start":{"line":92,"col":11,"byte":2047},"end":{"line":95,"col":40,"byte":2218}},"final\u001fpatient_id":{"start":{"line":59,"col":11,"byte":1034},"end":{"line":59,"col":23,"byte":1046}},"final\u001fpatient_key":{"start":{"line":58,"col":11,"byte":981},"end":{"line":58,"col":53,"byte":1023}},"final\u001fpatient_responsibility":{"start":{"line":89,"col":11,"byte":1924},"end":{"line":89,"col":87,"byte":2000}},"final\u001fpayer_coverage":{"start":{"line":88,"col":11,"byte":1897},"end":{"line":88,"col":27,"byte":1913}},"final\u001fpayer_id":{"start":{"line":65,"col":11,"byte":1278},"end":{"line":65,"col":21,"byte":1288}},"final\u001fpayer_key":{"start":{"line":64,"col":11,"byte":1228},"end":{"line":64,"col":50,"byte":1267}},"final\u001fprovider_id":{"start":{"line":61,"col":11,"byte":1113},"end":{"line":61,"col":24,"byte":1126}},"final\u001fprovider_key":{"start":{"line":60,"col":11,"byte":1057},"end":{"line":60,"col":56,"byte":1102}},"final\u001freason_code":{"start":{"line":75,"col":11,"byte":1559},"end":{"line":75,"col":24,"byte":1572}},"final\u001freason_description":{"start":{"line":76,"col":11,"byte":1583},"end":{"line":76,"col":31,"byte":1603}},"final\u001ftotal_claim_cost":{"start":{"line":87,"col":11,"byte":1868},"end":{"line":87,"col":29,"byte":1886}},"organizations\u001forganization_id":{"start":{"line":31,"col":11,"byte":490},"end":{"line":31,"col":26,"byte":505}},"organizations\u001forganization_key":{"start":{"line":30,"col":9,"byte":463},"end":{"line":30,"col":25,"byte":479}},"patients\u001fbirth_date":{"start":{"line":16,"col":11,"byte":216},"end":{"line":16,"col":21,"byte":226}},"patients\u001fpatient_id":{"start":{"line":15,"col":11,"byte":195},"end":{"line":15,"col":21,"byte":205}},"patients\u001fpatient_key":{"start":{"line":14,"col":9,"byte":173},"end":{"line":14,"col":20,"byte":184}},"payers\u001fpayer_id":{"start":{"line":38,"col":11,"byte":612},"end":{"line":38,"col":19,"byte":620}},"payers\u001fpayer_key":{"start":{"line":37,"col":9,"byte":592},"end":{"line":37,"col":18,"byte":601}},"providers\u001fprovider_id":{"start":{"line":24,"col":11,"byte":362},"end":{"line":24,"col":22,"byte":373}},"providers\u001fprovider_key":{"start":{"line":23,"col":9,"byte":339},"end":{"line":23,"col":21,"byte":351}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"base_encounter_cost":{"description":"Base cost of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"base_encounter_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"base_encounter_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_base_encounter_cost__0__base_encounter_cost_is_not_null.a096f214be"}],"documented":true},"duration_minutes":{"description":"Duration of encounter in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_duration_minutes__0__duration_minutes_is_not_null.ce51123f28"}],"documented":true},"encounter_class":{"description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.59f26de6a5"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_class.9419876296"}],"documented":true},"encounter_code":{"description":"SNOMED CT code for the encounter type","documented":true},"encounter_description":{"description":"Description of the encounter type","documented":true},"encounter_end_at":{"description":"End timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_end_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"encounter_end_at is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_end_at__current_timestamp___1900_01_01_timestamp__encounter_end_at_is_not_null.393dec12f4"}],"documented":true},"encounter_end_date_key":{"description":"Foreign key to dim_date for encounter end","documented":true},"encounter_id":{"description":"Natural encounter identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_id.f7111b4eea"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_id.ce88bae928"}],"documented":true},"encounter_key":{"description":"Surrogate key for the encounter","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_key.4fa2f03d5e"},{"kind":"unique","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"}],"documented":true},"encounter_start_at":{"description":"Start timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_start_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_start_at__current_timestamp___1900_01_01_timestamp.f32b839e27"},{"kind":"not_null","kwargs":{"column_name":"encounter_start_at","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_at.c47cc214ac"}],"documented":true},"encounter_start_date_key":{"description":"Foreign key to dim_date for encounter start","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_start_date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_date_key.73ce57ba43"},{"kind":"relationships","kwargs":{"column_name":"encounter_start_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_encounter_start_date_key__date_key__ref_dim_date_.4f1d88383c"}],"documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_id.acfb633ecb"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_key.815fa1363c"},{"kind":"relationships","kwargs":{"column_name":"organization_key","field":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_organizations')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_organization_key__organization_key__ref_dim_organizations_.5feb1b3ad6"}],"documented":true},"patient_age_at_encounter":{"description":"Patient's age at the time of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_age_at_encounter","max_value":150,"min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_age_at_encounter is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_age_at_encounter__150__0__patient_age_at_encounter_is_not_null.bbd237256e"}],"documented":true},"patient_id":{"description":"Natural patient identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_id.9e325e7ac6"}],"documented":true},"patient_key":{"description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_key.e5abbfbe29"},{"kind":"relationships","kwargs":{"column_name":"patient_key","field":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_patients')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_patient_key__patient_key__ref_dim_patients_.ad1c292e28"}],"documented":true},"patient_responsibility":{"description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_responsibility","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_responsibility is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_responsibility__0__patient_responsibility_is_not_null.7a61f00fd0"}],"documented":true},"payer_coverage":{"description":"Amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"payer_coverage","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"payer_coverage is not 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from {{ ref('dim_payers') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_encounters\n    description: >\n      Encounter fact table containing healthcare visits/encounters.\n      Links to patient, provider, organization, and payer dimensions.\n      Includes date keys, costs, and derived duration.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 40000\n          config:\n            severity: warn\n    columns:\n      - name: encounter_key\n        description: Surrogate key for the encounter\n        data_tests:\n          - unique\n          - not_null\n\n      - name: encounter_id\n        description: Natural encounter identifier (UUID)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: provider_key\n        description: Foreign key to dim_providers (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_providers')\n              field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_organizations')\n              field: organization_key\n\n      - name: organization_id\n        description: Natural organization identifier\n        data_tests:\n          - not_null\n\n      - name: payer_key\n        description: Foreign key to dim_payers (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_payers')\n              field: payer_key\n\n      - name: payer_id\n        description: Natural payer identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_start_date_key\n        description: Foreign key to dim_date for encounter start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"encounter_start_date_key != -1\"\n\n      - name: encounter_end_date_key\n        description: Foreign key to dim_date for encounter end\n\n      - name: encounter_class\n        description: Type of encounter (ambulatory, wellness, emergency, etc.)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_code\n        description: SNOMED CT code for the encounter type\n\n      - name: encounter_description\n        description: Description of the encounter type\n\n      - name: reason_code\n        description: SNOMED CT code for the reason of visit\n\n      - name: reason_description\n        description: Description of the reason for visit\n\n      - name: encounter_start_at\n        description: Start timestamp of the encounter\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::timestamp\"\n              max_value: \"current_timestamp\"\n              config:\n                severity: warn\n\n      - name: encounter_end_at\n        description: End timestamp of the encounter\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::timestamp\"\n              max_value: \"current_timestamp\"\n              row_condition: \"encounter_end_at is not null\"\n              config:\n                severity: warn\n\n      - name: duration_minutes\n        description: Duration of encounter in minutes\n        data_tests:\n          # Duration must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"duration_minutes is not null\"\n              config:\n                severity: warn\n\n      - name: base_encounter_cost\n        description: Base cost of the encounter\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"base_encounter_cost is not null\"\n              config:\n                severity: warn\n\n      - name: total_claim_cost\n        description: Total claim cost\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_claim_cost is not null\"\n              config:\n                severity: warn\n\n      - name: payer_coverage\n        description: Amount covered by payer\n        data_tests:\n          # Coverage must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"payer_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: patient_responsibility\n        description: Amount patient is responsible for (total_claim_cost - payer_coverage)\n        data_tests:\n          # Patient responsibility must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"patient_responsibility is not null\"\n              config:\n                severity: warn\n\n      - name: patient_age_at_encounter\n        description: Patient's age at the time of the encounter\n        data_tests:\n          # Age must be reasonable 0-150 years (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 150\n              row_condition: \"patient_age_at_encounter is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was 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Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","dag":{"nodes":[{"id":"source","role":"import"},{"id":"(final select)","label":"fct_encounters_incremental.sql","role":"final"}],"edges":[{"from":"source","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","node_spans":{"(final select)":{"start":{"line":7,"col":1,"byte":90},"end":{"line":16,"col":1,"byte":299}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":88}}},"column_spans":{"(final select)\u001fencounter_id":{"start":{"line":8,"col":5,"byte":101},"end":{"line":8,"col":17,"byte":113}},"(final select)\u001fencounter_type":{"start":{"line":10,"col":7,"byte":137},"end":{"line":10,"col":21,"byte":151}},"(final select)\u001fpatient_id":{"start":{"line":9,"col":7,"byte":120},"end":{"line":9,"col":17,"byte":130}},"(final select)\u001fstart_time":{"start":{"line":11,"col":7,"byte":158},"end":{"line":11,"col":17,"byte":168}},"(final select)\u001ftotal_cost":{"start":{"line":12,"col":7,"byte":175},"end":{"line":12,"col":17,"byte":185}},"source\u001f*":{"start":{"line":4,"col":12,"byte":30},"end":{"line":4,"col":13,"byte":31}}}},"column_lineage":{"context":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_incremental_encounter_id.b165c01d02"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"}],"documented":true},"encounter_type":{"description":"Visit class (wellness, emergency, ...)","documented":true},"patient_id":{"description":"Patient the encounter belongs to","documented":true},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark","documented":true},"total_cost":{"description":"Total claim cost for the encounter","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"start_time"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"start_time"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"total_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_cost"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='incremental',\n        unique_key='encounter_id',\n        incremental_strategy='merge',\n        on_schema_change='append_new_columns'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__encounters') }}\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost\nfrom source\n{% if is_incremental() %}\nwhere start_time > (select max(start_time) from {{ this }})\n{% endif %}","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  # cute-dbt#165 — column-header tooltip dogfood on the incremental model:\n  # authored descriptions + unique/not_null on the merge key, so the\n  # playground golden surfaces the th tooltips on this model's `this`\n  # given, ref given, and expect tables.\n  - name: fct_encounters_incremental\n    description: >\n      Incremental encounter accumulator. On incremental runs it only merges\n      encounters newer than the loaded start_time high-water mark.\n    columns:\n      - name: encounter_id\n        description: Natural key of the encounter (unique per visit)\n        data_tests:\n          - unique\n          - not_null\n      - name: patient_id\n        description: Patient the encounter belongs to\n      - name: encounter_type\n        description: Visit class (wellness, emergency, ...)\n      - name: start_time\n        description: Encounter start timestamp - the incremental high-water mark\n      - name: total_cost\n        description: Total claim cost for the encounter"},"tests":[{"id":"unit_test.healthcare_analytics.fct_encounters_incremental.test_fct_encounters_incremental_appends_new_encounters","name":"test_fct_encounters_incremental_appends_new_encounters","target_model":"fct_encounters_incremental","changed":false,"description":"fct_encounters_incremental appends encounters newer than the latest loaded start_time. 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With is_incremental() true, only the new\n      encounter (e2) is merged; the prior row (e1, supplied via `this`) is\n      untouched, so Expected is the single inserted row — the materialization\n      delta, not the resulting table.\n    model: fct_encounters_incremental\n    overrides:\n      macros:\n        is_incremental: true\n    given:\n      - input: this\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n      - input: ref('stg_synthea__encounters')\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n          - encounter_id: 'e2'\n            patient_id: 'p2'\n            encounter_type: 'emergency'\n            start_time: '2025-02-01 14:30:00'\n            total_cost: 500.0\n    expect:\n      format: dict\n      rows:\n        - encounter_id: 'e2'\n          patient_id: 'p2'\n          encounter_type: 'emergency'\n          start_time: '2025-02-01 14:30:00'\n          total_cost: 500.0","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - 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the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_encounters_monthly","path":"models/marts/core/fct_encounters_monthly.sql","description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters_monthly.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"encounters_with_date","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":53,"col":1,"byte":1374},"end":{"line":53,"col":20,"byte":1393}},"dim_date":{"start":{"line":16,"col":1,"byte":301},"end":{"line":22,"col":2,"byte":431}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":298}},"encounters_with_date":{"start":{"line":24,"col":1,"byte":434},"end":{"line":34,"col":2,"byte":790}},"final":{"start":{"line":36,"col":1,"byte":793},"end":{"line":51,"col":2,"byte":1372}}},"column_spans":{"(final select)\u001f*":{"start":{"line":53,"col":8,"byte":1381},"end":{"line":53,"col":9,"byte":1382}},"dim_date\u001fdate_key":{"start":{"line":18,"col":9,"byte":334},"end":{"line":18,"col":17,"byte":342}},"dim_date\u001fmonth_actual":{"start":{"line":20,"col":11,"byte":375},"end":{"line":20,"col":23,"byte":387}},"dim_date\u001fyear_actual":{"start":{"line":19,"col":11,"byte":353},"end":{"line":19,"col":22,"byte":364}},"encounters\u001fduration_minutes":{"start":{"line":12,"col":11,"byte":232},"end":{"line":12,"col":27,"byte":248}},"encounters\u001fencounter_class":{"start":{"line":8,"col":11,"byte":121},"end":{"line":8,"col":26,"byte":136}},"encounters\u001fencounter_id":{"start":{"line":5,"col":9,"byte":42},"end":{"line":5,"col":21,"byte":54}},"encounters\u001fencounter_start_date_key":{"start":{"line":7,"col":11,"byte":86},"end":{"line":7,"col":35,"byte":110}},"encounters\u001fpatient_id":{"start":{"line":6,"col":11,"byte":65},"end":{"line":6,"col":21,"byte":75}},"encounters\u001fpatient_responsibility":{"start":{"line":11,"col":11,"byte":199},"end":{"line":11,"col":33,"byte":221}},"encounters\u001fpayer_coverage":{"start":{"line":10,"col":11,"byte":174},"end":{"line":10,"col":25,"byte":188}},"encounters\u001ftotal_claim_cost":{"start":{"line":9,"col":11,"byte":147},"end":{"line":9,"col":27,"byte":163}},"encounters_with_date\u001f*":{"start":{"line":26,"col":9,"byte":479},"end":{"line":26,"col":12,"byte":482}},"encounters_with_date\u001fmonth_actual":{"start":{"line":28,"col":11,"byte":517},"end":{"line":28,"col":25,"byte":531}},"encounters_with_date\u001fyear_actual":{"start":{"line":27,"col":11,"byte":493},"end":{"line":27,"col":24,"byte":506}},"encounters_with_date\u001fyear_month":{"start":{"line":29,"col":16,"byte":547},"end":{"line":30,"col":72,"byte":654}},"final\u001f_loaded_at":{"start":{"line":48,"col":11,"byte":1241},"end":{"line":48,"col":42,"byte":1272}},"final\u001favg_duration_minutes":{"start":{"line":47,"col":11,"byte":1185},"end":{"line":47,"col":56,"byte":1230}},"final\u001fencounter_class":{"start":{"line":41,"col":11,"byte":889},"end":{"line":41,"col":26,"byte":904}},"final\u001fencounter_count":{"start":{"line":42,"col":11,"byte":915},"end":{"line":42,"col":38,"byte":942}},"final\u001fmonth_actual":{"start":{"line":40,"col":11,"byte":866},"end":{"line":40,"col":23,"byte":878}},"final\u001ftotal_claim_cost":{"start":{"line":44,"col":11,"byte":1009},"end":{"line":44,"col":52,"byte":1050}},"final\u001ftotal_patient_responsibility":{"start":{"line":46,"col":11,"byte":1115},"end":{"line":46,"col":70,"byte":1174}},"final\u001ftotal_payer_coverage":{"start":{"line":45,"col":11,"byte":1061},"end":{"line":45,"col":54,"byte":1104}},"final\u001funique_patients":{"start":{"line":43,"col":11,"byte":953},"end":{"line":43,"col":56,"byte":998}},"final\u001fyear_actual":{"start":{"line":39,"col":11,"byte":844},"end":{"line":39,"col":22,"byte":855}},"final\u001fyear_month":{"start":{"line":38,"col":9,"byte":823},"end":{"line":38,"col":19,"byte":833}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"avg_duration_minutes":{"description":"Average encounter duration in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"avg_duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"avg_duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_avg_duration_minutes__0__avg_duration_minutes_is_not_null.0cea70c94f"}],"documented":true},"encounter_class":{"description":"Type of encounter","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) 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  , year_actual\n        , month_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_encounters_monthly\n    description: >\n      Monthly aggregated encounter statistics by encounter class.\n      Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 100\n          config:\n            severity: warn\n    columns:\n      - name: year_month\n        description: Year-month string (YYYY-MM format)\n        data_tests:\n          - not_null\n          # Pattern validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_match_regex:\n              regex: \"^\\\\d{4}-\\\\d{2}$\"\n              config:\n                severity: warn\n\n      - name: year_actual\n        description: Year number\n        data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Month number (1-12)\n        data_tests:\n          - not_null\n          # Month must be 1-12 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 12\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_count\n        description: Number of encounters in the month\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: unique_patients\n        description: Number of unique patients with encounters\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: total_claim_cost\n        description: Sum of total claim costs\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_claim_cost is not null\"\n              config:\n                severity: warn\n\n      - name: total_payer_coverage\n        description: Sum of payer coverage amounts\n        data_tests:\n          # Coverage must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_payer_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: total_patient_responsibility\n        description: Sum of patient responsibility amounts\n        data_tests:\n          # Patient responsibility must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_patient_responsibility is not null\"\n              config:\n                severity: warn\n\n      - name: avg_duration_minutes\n        description: Average encounter duration in minutes\n        data_tests:\n          # Duration must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"avg_duration_minutes is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_monthly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_month, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_encounters_yearly","path":"models/marts/core/fct_encounters_yearly.sql","description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters_yearly.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"encounters_with_date","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , 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sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , cast(count(*) as decimal(18, 2)) / nullif(count(distinct patient_id), 0) as encounters_per_patient\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_actual, encounter_class\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":48,"col":1,"byte":1242},"end":{"line":48,"col":20,"byte":1261}},"dim_date":{"start":{"line":16,"col":1,"byte":301},"end":{"line":21,"col":2,"byte":408}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":298}},"encounters_with_date":{"start":{"line":23,"col":1,"byte":411},"end":{"line":30,"col":2,"byte":619}},"final":{"start":{"line":32,"col":1,"byte":622},"end":{"line":46,"col":2,"byte":1240}}},"column_spans":{"(final 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when record was loaded","documented":true},"avg_duration_minutes":{"description":"Average encounter duration in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"avg_duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_yearly')) }}","row_condition":"avg_duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_yearly_avg_duration_minutes__0__avg_duration_minutes_is_not_null.735639404e"}],"documented":true},"encounter_class":{"description":"Type of encounter","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_yearly')) 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}}","row_condition":"total_payer_coverage is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_yearly_total_payer_coverage__0__total_payer_coverage_is_not_null.7dcf20d7fc"}],"documented":true},"unique_patients":{"description":"Number of unique patients with encounters","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"unique_patients","min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_yearly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_yearly_unique_patients__1.977eab8cda"},{"kind":"not_null","kwargs":{"column_name":"unique_patients","model":"{{ get_where_subquery(ref('fct_encounters_yearly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_yearly_unique_patients.b2b2d4c41b"}],"documented":true},"year_actual":{"description":"Year 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   where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , cast(count(*) as decimal(18, 2)) / nullif(count(distinct patient_id), 0) as encounters_per_patient\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_actual, encounter_class\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_encounters_yearly\n    description: >\n      Yearly aggregated encounter statistics by encounter class.\n      Provides counts, costs, patient metrics, and encounters per patient ratio.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 10\n          config:\n            severity: warn\n    columns:\n      - name: year_actual\n        description: Year number\n        data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_count\n        description: Number of encounters in the year\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: unique_patients\n        description: Number of unique patients with encounters\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: total_claim_cost\n        description: Sum of total claim costs\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_claim_cost is not null\"\n              config:\n                severity: warn\n\n      - name: total_payer_coverage\n        description: Sum of payer coverage amounts\n        data_tests:\n          # Coverage must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_payer_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: total_patient_responsibility\n        description: Sum of patient responsibility amounts\n        data_tests:\n          # Patient responsibility must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_patient_responsibility is not null\"\n              config:\n                severity: warn\n\n      - name: avg_duration_minutes\n        description: Average encounter duration in minutes\n        data_tests:\n          # Duration must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"avg_duration_minutes is not null\"\n              config:\n                severity: warn\n\n      - name: encounters_per_patient\n        description: Average number of encounters per patient\n        data_tests:\n          # Ratio must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"encounters_per_patient is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_yearly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_actual, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_patient_summary","path":"models/marts/analytics/fct_patient_summary.sql","description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"conditions","role":"import"},{"id":"encounters_with_year","role":"transform"},{"id":"conditions_per_patient_year","role":"transform"},{"id":"patient_year_agg","role":"transform"},{"id":"with_conditions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final 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costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)","patient_year_agg":"patient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","with_conditions":"with_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where patient_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\n-- join encounters to date dimension to get year\nencounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n),\n\n-- join condition counts\nwith_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)\n\nselect * from final","node_spans":{"(final 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dimensions for foreign key lookups.\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n    where patient_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from {{ ref('stg_synthea__conditions') }}\n),\n\n-- join encounters to date dimension to get year\nencounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n),\n\n-- join condition counts\nwith_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_patient_summary\n    description: |\n      Annual patient-level aggregations for outcomes tracking and population health analysis.\n      One row per patient per calendar year with comprehensive utilization and cost metrics.\n      Grain: (patient_id, year_actual)\n\n      Use cases:\n      - Patient outcomes tracking over time\n      - Annual cost summaries and trends\n      - Cohort analysis and patient stratification\n      - Health trajectory visualization\n      - High-cost patient identification\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: patient_id + year_actual\n      owner: healthcare_analytics\n      uses:\n        - \"Population health reporting\"\n        - \"Cost and utilization analysis\"\n        - \"Patient stratification\"\n        - \"Year-over-year trending\"\n      example_queries:\n        - name: \"Top 10 costliest patients by year\"\n          description: \"Identify high-cost patients for care management programs\"\n          sql: |\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , fps.year_actual\n                , fps.total_cost\n                , fps.encounter_count\n                , fps.condition_count\n                , fps.payer_coverage_pct\n            from main_marts.fct_patient_summary fps\n            join main_marts.dim_patients p\n                on fps.patient_key = p.patient_key\n            where fps.year_actual = 2024\n            order by fps.total_cost desc\n            limit 10\n\n        - name: \"Patient count and cost by age group\"\n          description: \"Analyze patient population distribution and costs by age cohort\"\n          sql: |\n            select\n                case\n                    when fps.patient_age_at_year_end < 18 then '0-17'\n                    when fps.patient_age_at_year_end < 35 then '18-34'\n                    when fps.patient_age_at_year_end < 50 then '35-49'\n                    when fps.patient_age_at_year_end < 65 then '50-64'\n                    else '65+'\n                end as age_group\n                , count(distinct fps.patient_id) as patient_count\n                , round(avg(fps.total_cost), 2) as avg_annual_cost\n                , round(sum(fps.total_cost), 2) as total_cost\n                , round(avg(fps.encounter_count), 2) as avg_encounters\n            from main_marts.fct_patient_summary fps\n            where fps.year_actual = 2024\n            group by 1\n            order by 1\n\n        - name: \"Cost and utilization trends over time\"\n          description: \"Year-over-year comparison of costs and utilization metrics\"\n          sql: |\n            select\n                fps.year_actual\n                , count(distinct fps.patient_id) as patient_count\n                , round(avg(fps.encounter_count), 2) as avg_encounters_per_patient\n                , round(avg(fps.condition_count), 2) as avg_conditions_per_patient\n                , round(avg(fps.total_cost), 2) as avg_annual_cost\n                , round(sum(fps.total_cost), 2) as total_population_cost\n                , round(avg(fps.payer_coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_patient_summary fps\n            group by fps.year_actual\n            order by fps.year_actual desc\n\n        - name: \"High utilization patients\"\n          description: \"Find patients with above-average encounters and conditions\"\n          sql: |\n            with averages as (\n                select\n                    avg(encounter_count) as avg_encounters\n                    , avg(condition_count) as avg_conditions\n                from main_marts.fct_patient_summary\n                where year_actual = 2024\n            )\n            select\n                fps.patient_id\n                , fps.encounter_count\n                , fps.condition_count\n                , fps.total_cost\n                , fps.unique_providers\n            from main_marts.fct_patient_summary fps\n            cross join averages a\n            where fps.year_actual = 2024\n                and fps.encounter_count > a.avg_encounters * 2\n                and fps.condition_count > a.avg_conditions\n            order by fps.total_cost desc\n            limit 25\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - patient_id\n              - year_actual\n          config:\n            severity: error\n    columns:\n      - name: patient_summary_key\n        description: Surrogate key (MD5 hash of patient_id + year_actual)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_patients')\n                field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: Calendar year of the summary\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: patient_age_at_year_end\n        description: Patient age as of December 31 of the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 150\n              config:\n                severity: warn\n\n      - name: encounter_count\n        description: Number of encounters in the year\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 10000\n              config:\n                severity: error\n\n      - name: procedure_count\n        description: Number of procedures in the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: condition_count\n        description: Number of distinct conditions diagnosed in the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_cost\n        description: Total claim cost for the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 10000000\n              config:\n                severity: error\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: patient_responsibility\n        description: Amount owed by patient (copay + coinsurance)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: payer_coverage_pct\n        description: Percentage of costs covered by insurance (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: unique_providers\n        description: Number of distinct providers seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1\n                max_value: 1000\n              config:\n                severity: error\n\n      - name: unique_organizations\n        description: Number of distinct organizations visited\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_patient_summary","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_patient_summary_patient_summary_key.e532e1ff45"]},"evidence":[{"label":"unique_key","value":"patient_summary_key"}],"pin_node":"(final select)"},{"check":"enforcement.constraint-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_patient_summary","construct":"constraint.primary_key[(patient_id, year_actual)]","verdict":{"status":"covered","by":["test.healthcare_analytics.dbt_utils_unique_combination_of_columns_fct_patient_summary_patient_id__year_actual.cdb3418166"]},"evidence":[{"label":"constraint","value":"primary_key on (patient_id, year_actual)"},{"label":"adapter","value":"duckdb (metadata-only)"},{"label":"backing-test","value":"present"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_provider_metrics","path":"models/marts/analytics/fct_provider_metrics.sql","description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"provider_month_agg","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_provider_metrics.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"procedures","to":"encounters_with_date","edge_type":"left"},{"from":"encounters_with_date","to":"provider_month_agg","edge_type":"from"},{"from":"provider_month_agg","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":143,"col":1,"byte":3614},"end":{"line":143,"col":20,"byte":3633}},"dim_date":{"start":{"line":24,"col":1,"byte":452},"end":{"line":30,"col":2,"byte":582}},"encounters":{"start":{"line":5,"col":6,"byte":9},"end":{"line":22,"col":2,"byte":449}},"encounters_with_date":{"start":{"line":41,"col":1,"byte":815},"end":{"line":54,"col":2,"byte":1321}},"final":{"start":{"line":95,"col":1,"byte":2331},"end":{"line":141,"col":2,"byte":3612}},"procedures":{"start":{"line":32,"col":1,"byte":585},"end":{"line":38,"col":2,"byte":757}},"provider_month_agg":{"start":{"line":57,"col":1,"byte":1364},"end":{"line":93,"col":2,"byte":2328}}},"column_spans":{"(final 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select)"}},"column":"organization_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"year_month_str"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_month_str"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"unique_patient_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"unique_patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_procedures"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_procedures"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"avg_patient_age"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"avg_patient_age"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"avg_encounter_duration_minutes"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"avg_encounter_duration_minutes"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_payer_coverage"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_payer_coverage"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"avg_cost_per_encounter"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"avg_cost_per_encounter"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"encounters_per_patient_avg"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounters_per_patient_avg"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"_loaded_at"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"_loaded_at"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='provider_metrics_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Monthly Provider Metrics Fact\n\n    Purpose: Provider-level utilization and performance metrics\n    Grain: One row per provider per calendar month\n    Use cases: Provider performance dashboards, utilization trending, quality measures\n\n    Aggregates encounters, patients, procedures, and costs by provider and month.\n    Calculates derived metrics like avg cost per encounter and encounters per patient.\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","sql_diff":{"lines":[{"kind":"context","text":"{{","emphasis":null},{"kind":"context","text":"    config(","emphasis":null},{"kind":"context","text":"        materialized='table',","emphasis":null},{"kind":"context","text":"        unique_key='provider_metrics_key',","emphasis":null},{"kind":"context","text":"        tags=['analytics', 'marts', 'fact']","emphasis":null},{"kind":"context","text":"    )","emphasis":null},{"kind":"context","text":"}}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"{#","emphasis":null},{"kind":"context","text":"    Monthly Provider Metrics Fact","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    Purpose: Provider-level utilization and performance metrics","emphasis":null},{"kind":"context","text":"    Grain: One row per provider per calendar month","emphasis":null},{"kind":"context","text":"    Use cases: Provider performance dashboards, utilization trending, quality measures","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    Aggregates encounters, patients, procedures, and costs by provider and month.","emphasis":null},{"kind":"context","text":"    Calculates derived metrics like avg cost per encounter and encounters per patient.","emphasis":null},{"kind":"context","text":"#}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"with encounters as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        encounter_key","emphasis":null},{"kind":"context","text":"        , encounter_id","emphasis":null},{"kind":"context","text":"        , patient_key","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , provider_key","emphasis":null},{"kind":"context","text":"        , provider_id","emphasis":null},{"kind":"context","text":"        , organization_key","emphasis":null},{"kind":"context","text":"        , organization_id","emphasis":null},{"kind":"context","text":"        , encounter_start_date_key","emphasis":null},{"kind":"context","text":"        , total_claim_cost","emphasis":null},{"kind":"context","text":"        , payer_coverage","emphasis":null},{"kind":"context","text":"        , duration_minutes","emphasis":null},{"kind":"context","text":"        , patient_age_at_encounter","emphasis":null},{"kind":"context","text":"    from {{ ref('fct_encounters') }}","emphasis":null},{"kind":"context","text":"    where provider_key != -1","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"dim_date as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        date_key","emphasis":null},{"kind":"context","text":"        , year_actual","emphasis":null},{"kind":"context","text":"        , month_actual","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_date') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"procedures as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        encounter_id","emphasis":null},{"kind":"removed","text":"        , count(distinct procedure_code) as procedure_count","emphasis":[16,39]},{"kind":"added","text":"        , count(*) as procedure_count","emphasis":[16,17]},{"kind":"context","text":"    from {{ ref('stg_synthea__procedures') }}","emphasis":null},{"kind":"context","text":"    group by encounter_id","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"-- join encounters to date dimension to get year/month","emphasis":null},{"kind":"context","text":"encounters_with_date as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        e.*","emphasis":null},{"kind":"context","text":"        , d.year_actual","emphasis":null},{"kind":"context","text":"        , d.month_actual","emphasis":null},{"kind":"context","text":"        , cast(d.year_actual as varchar)","emphasis":null},{"kind":"context","text":"            || '-'","emphasis":null},{"kind":"context","text":"            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str","emphasis":null},{"kind":"removed","text":"        , pr.procedure_count as procedure_count","emphasis":[10,28]},{"kind":"added","text":"        , coalesce(pr.procedure_count, 0) as procedure_count","emphasis":[10,41]},{"kind":"context","text":"    from encounters as e","emphasis":null},{"kind":"context","text":"    inner join dim_date as d on e.encounter_start_date_key = d.date_key","emphasis":null},{"kind":"context","text":"    left join procedures as pr on e.encounter_id = pr.encounter_id","emphasis":null},{"kind":"context","text":"    where e.encounter_start_date_key != -1","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"-- aggregate by provider and year-month","emphasis":null},{"kind":"context","text":"provider_month_agg as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        e.provider_id","emphasis":null},{"kind":"context","text":"        , e.provider_key","emphasis":null},{"kind":"context","text":"        , e.organization_id","emphasis":null},{"kind":"context","text":"        , e.organization_key","emphasis":null},{"kind":"context","text":"        , e.year_actual","emphasis":null},{"kind":"context","text":"        , e.month_actual","emphasis":null},{"kind":"context","text":"        , e.year_month_str","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- counts","emphasis":null},{"kind":"context","text":"        , count(*) as encounter_count","emphasis":null},{"kind":"context","text":"        , count(distinct e.patient_id) as unique_patient_count","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- patient metrics","emphasis":null},{"kind":"context","text":"        , avg(e.patient_age_at_encounter) as avg_patient_age","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- encounter metrics","emphasis":null},{"kind":"context","text":"        , avg(e.duration_minutes) as avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- procedure metrics","emphasis":null},{"kind":"removed","text":"        , sum(e.procedure_count) as procedure_total","emphasis":[36,51]},{"kind":"added","text":"        , sum(e.procedure_count) as total_procedures","emphasis":[36,52]},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- cost metrics","emphasis":null},{"kind":"context","text":"        , sum(e.total_claim_cost) as total_cost","emphasis":null},{"kind":"context","text":"        , sum(e.payer_coverage) as total_payer_coverage","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    from encounters_with_date as e","emphasis":null},{"kind":"context","text":"    group by","emphasis":null},{"kind":"context","text":"        e.provider_id","emphasis":null},{"kind":"context","text":"        , e.provider_key","emphasis":null},{"kind":"context","text":"        , e.organization_id","emphasis":null},{"kind":"context","text":"        , e.organization_key","emphasis":null},{"kind":"context","text":"        , e.year_actual","emphasis":null},{"kind":"context","text":"        , e.month_actual","emphasis":null},{"kind":"context","text":"        , e.year_month_str","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"final as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        -- surrogate key","emphasis":null},{"kind":"context","text":"        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- dimension keys","emphasis":null},{"kind":"context","text":"        , provider_key","emphasis":null},{"kind":"context","text":"        , provider_id","emphasis":null},{"kind":"context","text":"        , organization_key","emphasis":null},{"kind":"context","text":"        , organization_id","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- time grain","emphasis":null},{"kind":"context","text":"        , year_actual","emphasis":null},{"kind":"context","text":"        , month_actual","emphasis":null},{"kind":"context","text":"        , year_month_str","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: counts","emphasis":null},{"kind":"context","text":"        , encounter_count","emphasis":null},{"kind":"context","text":"        , unique_patient_count","emphasis":null},{"kind":"context","text":"        , total_procedures","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: averages","emphasis":null},{"kind":"context","text":"        , round(avg_patient_age, 2) as avg_patient_age","emphasis":null},{"kind":"context","text":"        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: costs","emphasis":null},{"kind":"context","text":"        , coalesce(total_cost, 0) as total_cost","emphasis":null},{"kind":"context","text":"        , coalesce(total_payer_coverage, 0) as total_payer_coverage","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: derived ratios","emphasis":null},{"kind":"context","text":"        , case","emphasis":null},{"kind":"context","text":"            when encounter_count > 0","emphasis":null},{"kind":"context","text":"            then round(total_cost / encounter_count, 2)","emphasis":null},{"kind":"context","text":"            else 0","emphasis":null},{"kind":"context","text":"        end as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        , case","emphasis":null},{"kind":"context","text":"            when unique_patient_count > 0","emphasis":null},{"kind":"context","text":"            then round(cast(encounter_count as decimal) / unique_patient_count, 2)","emphasis":null},{"kind":"context","text":"            else 0","emphasis":null},{"kind":"context","text":"        end as encounters_per_patient_avg","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- metadata","emphasis":null},{"kind":"context","text":"        , current_timestamp as _loaded_at","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    from provider_month_agg","emphasis":null},{"kind":"context","text":")","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"select * from final","emphasis":null}]},"model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_provider_metrics\n    description: |\n      Monthly provider-level utilization and performance metrics.\n      One row per provider per calendar month with patient volumes, costs, and efficiency metrics.\n      Grain: (provider_id, year_actual, month_actual)\n\n      Use cases:\n      - Provider performance tracking\n      - Workload distribution analysis\n      - Cost efficiency comparisons\n      - Capacity planning and staffing\n      - Quality improvement initiatives\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: provider_id + year_month_str\n      owner: healthcare_analytics\n      uses:\n        - \"Provider productivity reporting\"\n        - \"Workload balancing\"\n        - \"Cost efficiency analysis\"\n        - \"Capacity planning\"\n      example_queries:\n        - name: \"Top providers by patient volume\"\n          description: \"Identify highest-volume providers for capacity planning\"\n          sql: |\n            select\n                p.provider_id\n                , p.name as provider_name\n                , p.specialty\n                , sum(fpm.encounter_count) as total_encounters\n                , sum(fpm.unique_patient_count) as total_patients\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n            from main_marts.fct_provider_metrics fpm\n            join main_marts.dim_providers p\n                on fpm.provider_key = p.provider_key\n            where fpm.year_actual = 2024\n            group by 1, 2, 3\n            order by total_encounters desc\n            limit 15\n\n        - name: \"Provider metrics trend by month\"\n          description: \"Track provider productivity over time\"\n          sql: |\n            select\n                fpm.year_month_str\n                , count(distinct fpm.provider_id) as active_providers\n                , sum(fpm.encounter_count) as total_encounters\n                , sum(fpm.unique_patient_count) as total_patients\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n                , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\n            from main_marts.fct_provider_metrics fpm\n            where fpm.year_actual >= 2023\n            group by fpm.year_month_str\n            order by fpm.year_month_str desc\n\n        - name: \"Provider efficiency comparison\"\n          description: \"Compare providers by cost and volume efficiency\"\n          sql: |\n            select\n                p.provider_id\n                , p.name as provider_name\n                , p.specialty\n                , sum(fpm.encounter_count) as total_encounters\n                , round(sum(fpm.total_cost), 2) as total_cost\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n                , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\n                , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\n            from main_marts.fct_provider_metrics fpm\n            join main_marts.dim_providers p\n                on fpm.provider_key = p.provider_key\n            where fpm.year_actual = 2024\n            group by 1, 2, 3\n            having sum(fpm.encounter_count) >= 10\n            order by avg_cost_per_encounter\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - provider_id\n              - year_actual\n              - month_actual\n          config:\n            severity: error\n    columns:\n      - name: provider_metrics_key\n        description: Surrogate key (MD5 hash of provider_id + year_month_str)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_providers')\n                field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations\n\n      - name: organization_id\n        description: Natural organization identifier\n\n      - name: year_actual\n        description: Calendar year\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Calendar month (1-12)\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1\n                max_value: 12\n              config:\n                severity: error\n\n      - name: year_month_str\n        description: Year-month string (YYYY-MM)\n\n      - name: encounter_count\n        description: Number of encounters in the month\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: unique_patient_count\n        description: Number of distinct patients seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_patient_age\n        description: Average age of patients seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 150\n              config:\n                severity: warn\n\n      - name: avg_encounter_duration_minutes\n        description: Average encounter duration in minutes\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_procedures\n        description: Total procedures performed\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_cost\n        description: Total claim cost for the month\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_cost_per_encounter\n        description: Average cost per encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounters_per_patient_avg\n        description: Average encounters per patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded","diff":{"lines":[{"kind":"context","text":"  - name: fct_provider_metrics","emphasis":null},{"kind":"context","text":"    description: |","emphasis":null},{"kind":"context","text":"      Monthly provider-level utilization and performance metrics.","emphasis":null},{"kind":"removed","text":"      One row per provider per month.","emphasis":[31,36]},{"kind":"added","text":"      One row per provider per calendar month with patient volumes, costs, and efficiency metrics.","emphasis":[31,97]},{"kind":"context","text":"      Grain: (provider_id, year_actual, month_actual)","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      Use cases:","emphasis":null},{"kind":"context","text":"      - Provider performance tracking","emphasis":null},{"kind":"context","text":"      - Workload distribution analysis","emphasis":null},{"kind":"context","text":"      - Cost efficiency comparisons","emphasis":null},{"kind":"context","text":"      - Capacity planning and staffing","emphasis":null},{"kind":"context","text":"      - Quality improvement initiatives","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    config:","emphasis":null},{"kind":"context","text":"      tags: ['analytics', 'marts', 'fact']","emphasis":null},{"kind":"context","text":"    meta:","emphasis":null},{"kind":"context","text":"      grain: provider_id + year_month_str","emphasis":null},{"kind":"context","text":"      owner: healthcare_analytics","emphasis":null},{"kind":"context","text":"      uses:","emphasis":null},{"kind":"context","text":"        - \"Provider productivity reporting\"","emphasis":null},{"kind":"context","text":"        - \"Workload balancing\"","emphasis":null},{"kind":"context","text":"        - \"Cost efficiency analysis\"","emphasis":null},{"kind":"context","text":"        - \"Capacity planning\"","emphasis":null},{"kind":"context","text":"      example_queries:","emphasis":null},{"kind":"context","text":"        - name: \"Top providers by patient volume\"","emphasis":null},{"kind":"context","text":"          description: \"Identify highest-volume providers for capacity planning\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                p.provider_id","emphasis":null},{"kind":"context","text":"                , p.name as provider_name","emphasis":null},{"kind":"context","text":"                , p.specialty","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , sum(fpm.unique_patient_count) as total_patients","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            join main_marts.dim_providers p","emphasis":null},{"kind":"context","text":"                on fpm.provider_key = p.provider_key","emphasis":null},{"kind":"context","text":"            where fpm.year_actual = 2024","emphasis":null},{"kind":"context","text":"            group by 1, 2, 3","emphasis":null},{"kind":"context","text":"            order by total_encounters desc","emphasis":null},{"kind":"context","text":"            limit 15","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        - name: \"Provider metrics trend by month\"","emphasis":null},{"kind":"context","text":"          description: \"Track provider productivity over time\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                fpm.year_month_str","emphasis":null},{"kind":"context","text":"                , count(distinct fpm.provider_id) as active_providers","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , sum(fpm.unique_patient_count) as total_patients","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            where fpm.year_actual >= 2023","emphasis":null},{"kind":"context","text":"            group by fpm.year_month_str","emphasis":null},{"kind":"context","text":"            order by fpm.year_month_str desc","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        - name: \"Provider efficiency comparison\"","emphasis":null},{"kind":"context","text":"          description: \"Compare providers by cost and volume efficiency\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                p.provider_id","emphasis":null},{"kind":"context","text":"                , p.name as provider_name","emphasis":null},{"kind":"context","text":"                , p.specialty","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , round(sum(fpm.total_cost), 2) as total_cost","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient","emphasis":null},{"kind":"context","text":"                , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            join main_marts.dim_providers p","emphasis":null},{"kind":"context","text":"                on fpm.provider_key = p.provider_key","emphasis":null},{"kind":"context","text":"            where fpm.year_actual = 2024","emphasis":null},{"kind":"context","text":"            group by 1, 2, 3","emphasis":null},{"kind":"context","text":"            having sum(fpm.encounter_count) >= 10","emphasis":null},{"kind":"context","text":"            order by avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    data_tests:","emphasis":null},{"kind":"context","text":"      # Grain test: enforce unique combination","emphasis":null},{"kind":"context","text":"      - dbt_utils.unique_combination_of_columns:","emphasis":null},{"kind":"context","text":"          arguments:","emphasis":null},{"kind":"context","text":"            combination_of_columns:","emphasis":null},{"kind":"context","text":"              - provider_id","emphasis":null},{"kind":"context","text":"              - year_actual","emphasis":null},{"kind":"context","text":"              - month_actual","emphasis":null},{"kind":"context","text":"          config:","emphasis":null},{"kind":"context","text":"            severity: error","emphasis":null},{"kind":"context","text":"    columns:","emphasis":null},{"kind":"context","text":"      - name: provider_metrics_key","emphasis":null},{"kind":"context","text":"        description: Surrogate key (MD5 hash of provider_id + year_month_str)","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - unique","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: provider_key","emphasis":null},{"kind":"context","text":"        description: Foreign key to dim_providers","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - relationships:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                to: ref('dim_providers')","emphasis":null},{"kind":"context","text":"                field: provider_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: provider_id","emphasis":null},{"kind":"context","text":"        description: Natural provider identifier","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: organization_key","emphasis":null},{"kind":"context","text":"        description: Foreign key to dim_organizations","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: organization_id","emphasis":null},{"kind":"context","text":"        description: Natural organization identifier","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: year_actual","emphasis":null},{"kind":"context","text":"        description: Calendar year","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 1900","emphasis":null},{"kind":"context","text":"                max_value: 2100","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: warn","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: month_actual","emphasis":null},{"kind":"context","text":"        description: Calendar month (1-12)","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 1","emphasis":null},{"kind":"context","text":"                max_value: 12","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: year_month_str","emphasis":null},{"kind":"context","text":"        description: Year-month string (YYYY-MM)","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: encounter_count","emphasis":null},{"kind":"context","text":"        description: Number of encounters in the month","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: unique_patient_count","emphasis":null},{"kind":"context","text":"        description: Number of distinct patients seen","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_patient_age","emphasis":null},{"kind":"context","text":"        description: Average age of patients seen","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"                max_value: 150","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: warn","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"        description: Average encounter duration in minutes","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_procedures","emphasis":null},{"kind":"context","text":"        description: Total procedures performed","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_cost","emphasis":null},{"kind":"context","text":"        description: Total claim cost for the month","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_payer_coverage","emphasis":null},{"kind":"context","text":"        description: Amount covered by insurance","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"        description: Average cost per encounter","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: encounters_per_patient_avg","emphasis":null},{"kind":"context","text":"        description: Average encounters per patient","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: _loaded_at","emphasis":null},{"kind":"context","text":"        description: Timestamp when record was loaded","emphasis":null}]}},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_provider_metrics","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_provider_metrics_provider_metrics_key.d58939d602"]},"evidence":[{"label":"unique_key","value":"provider_metrics_key"}],"degraded":[{"by":"test.healthcare_analytics.unique_fct_provider_metrics_provider_metrics_key.d58939d602","causes":["where-filtered — only rows matching `year_actual >= 2024` are checked; the grain is unverified outside the filter","limit: 100 — failing-row reporting caps at 100 rows; the test still trips on the first failure, but the failure surface is truncated"]}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":true,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_provider_scorecard","path":"models/marts/analytics/fct_provider_scorecard.sql","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).\nselect\n    provider_id\n    , avg(quality_score) as avg_quality_score\n    , count(*) as encounter_count\nfrom \"memory\".\"main_marts\".\"fct_provider_metrics\"\ngroup by provider_id"},"code_map":{"compiled":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).\nselect\n    provider_id\n    , avg(quality_score) as avg_quality_score\n    , count(*) as encounter_count\nfrom \"memory\".\"main_marts\".\"fct_provider_metrics\"\ngroup by provider_id","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":7,"col":21,"byte":250}}},"column_spans":{"(final select)\u001favg_quality_score":{"start":{"line":4,"col":7,"byte":106},"end":{"line":4,"col":46,"byte":145}},"(final select)\u001fencounter_count":{"start":{"line":5,"col":7,"byte":152},"end":{"line":5,"col":34,"byte":179}},"(final select)\u001fprovider_id":{"start":{"line":3,"col":5,"byte":88},"end":{"line":3,"col":16,"byte":99}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"fct_provider_metrics"}},"column":"provider_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"provider_id"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'scorecard']\n    )\n}}\n\n-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).\nselect\n    provider_id\n    , avg(quality_score) as avg_quality_score\n    , count(*) as encounter_count\nfrom {{ ref('fct_provider_metrics') }}\ngroup by provider_id","sql_diff":{"lines":[{"kind":"added","text":"{{","emphasis":null},{"kind":"added","text":"    config(","emphasis":null},{"kind":"added","text":"        materialized='table',","emphasis":null},{"kind":"added","text":"        tags=['analytics', 'scorecard']","emphasis":null},{"kind":"added","text":"    )","emphasis":null},{"kind":"added","text":"}}","emphasis":null},{"kind":"added","text":"","emphasis":null},{"kind":"added","text":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).","emphasis":null},{"kind":"added","text":"select","emphasis":null},{"kind":"added","text":"    provider_id","emphasis":null},{"kind":"added","text":"    , avg(quality_score) as avg_quality_score","emphasis":null},{"kind":"added","text":"    , count(*) as encounter_count","emphasis":null},{"kind":"added","text":"from {{ ref('fct_provider_metrics') }}","emphasis":null},{"kind":"added","text":"group by provider_id","emphasis":null}]},"model_yaml":{"missing":"No schema file declares this model in the manifest — there is no authored models: entry to show."},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_provider_scorecard","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"provider_metrics_key"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns."}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":true,"config":false,"unit_test":false},"state":"new"},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final select)\u001f*":{"start":{"line":30,"col":8,"byte":542},"end":{"line":30,"col":9,"byte":543}},"grid\u001fmonth_actual":{"start":{"line":23,"col":11,"byte":423},"end":{"line":23,"col":30,"byte":442}},"grid\u001fmonth_key":{"start":{"line":21,"col":14,"byte":312},"end":{"line":21,"col":85,"byte":383}},"grid\u001fmonth_name":{"start":{"line":24,"col":11,"byte":453},"end":{"line":24,"col":28,"byte":470}},"grid\u001fstate":{"start":{"line":25,"col":11,"byte":481},"end":{"line":25,"col":23,"byte":493}},"grid\u001fyear_actual":{"start":{"line":22,"col":11,"byte":394},"end":{"line":22,"col":29,"byte":412}},"months\u001fmonth_actual":{"start":{"line":8,"col":11,"byte":71},"end":{"line":8,"col":23,"byte":83}},"months\u001fmonth_name":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":21,"byte":104}},"months\u001fyear_actual":{"start":{"line":7,"col":9,"byte":49},"end":{"line":7,"col":20,"byte":60}},"states\u001fstate":{"start":{"line":14,"col":21,"byte":183},"end":{"line":14,"col":26,"byte":188}}}},"column_lineage":{"context":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_date_state_grid\n    description: |\n      Dense month x state reporting scaffold.\n      One row per (calendar month x patient state) combination — the full\n      cartesian product of dim_date months and the non-null states observed\n      in patient staging. Left-join sparse activity facts against this grid\n      so zero-activity cells render as explicit zeros.\n      Grain: month_key x state\n\n      Use cases:\n      - Dense time-series dashboards (no missing month/state cells)\n      - Zero-activity gap detection\n      - cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n\n    data_tests:\n      # New-style `arguments:` nesting (fusion-clean; dbt-core 1.10+).\n      # The legacy top-level-args form elsewhere in this file predates\n      # the planned dbt-autofix sweep.\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - month_key\n              - state\n\n    config:\n      tags: ['analytics', 'reporting', 'marts']\n      meta:\n        grain: month_key x state\n        owner: healthcare_analytics\n        uses:\n          - \"Dense reporting grids\"\n          - \"Zero-activity gap detection\"\n\n    columns:\n      - name: month_key\n        description: \"Month surrogate key in YYYYMM integer form (e.g., 202401)\"\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: \"Calendar year of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_actual\n        description: \"Calendar month number (1-12) of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_name\n        description: \"Full month name (e.g., January)\"\n\n      - name: state\n        description: \"Patient state, distinct and non-null from staging\"\n        data_tests:\n          - not_null"},"tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # Cross-join demo (cute-dbt#64): mart_date_state_grid's CROSS JOIN\n  # CTE gives cute-dbt's renderer a real cross-join edge to draw. This\n  # test also mixes given formats (dict + csv) in a single unit_test.\n  - name: test_mart_date_state_grid_cross_joins_months_and_states\n    description: |\n      mart_date_state_grid CROSS JOINs the distinct calendar months from\n      dim_date with the distinct non-null patient states from staging.\n      Three daily dim_date rows collapse to two months; four patient\n      rows collapse to two states (one NULL filtered out, one duplicate\n      deduplicated). The result is the full 2 x 2 cartesian product —\n      four grid rows. Mixes a dict given (dim_date) with a csv given\n      (patients) in one test.\n    model: mart_date_state_grid\n    given:\n      - input: ref('dim_date')\n        format: dict\n        rows:\n          - date_actual: 2024-01-15\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-01-16\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-02-01\n            year_actual: 2024\n            month_actual: 2\n            month_name: 'February'\n      - input: ref('stg_synthea__patients')\n        format: csv\n        fixture: mart_date_state_grid_patients\n    expect:\n      format: csv\n      rows: |\n        month_key,year_actual,month_actual,month_name,state\n        202401,2024,1,January,Massachusetts\n        202401,2024,1,January,Connecticut\n        202402,2024,2,February,Massachusetts\n        202402,2024,2,February,Connecticut","data_diff":{"given":[{"ordinal":1,"input":"ref('stg_synthea__patients')","diff":{"columns":[{"name":"patient_id","status":"present"},{"name":"state","status":"present"}],"rows":[{"kind":"unchanged","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},"new":{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},"changed":false},{"old":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"new":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"changed":false}]},{"kind":"modified","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},"new":{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},"changed":false},{"old":{"display":"Connecticutt","key":{"t":"str","v":"Connecticutt"}},"new":{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},"changed":true}]},{"kind":"modified","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},"new":{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},"changed":false},{"old":{"display":"Vermont","key":{"t":"str","v":"Vermont"}},"new":{"display":"","key":{"t":"null"}},"changed":true}]},{"kind":"unchanged","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},"new":{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},"changed":false},{"old":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"new":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"changed":false}]}]}}],"expect":null},"given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","table":{"columns":["patient_id","state"],"rows":[{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},{"display":"","key":{"t":"null"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":"patient_id,state\na00000aa-0000-0000-0000-000000000001,Massachusetts\na00000aa-0000-0000-0000-000000000002,Connecticut\na00000aa-0000-0000-0000-000000000003,\na00000aa-0000-0000-0000-000000000004,Massachusetts\n","format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv","column_meta":{"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"var_references":[{"name":"default_state_grid_density","tier":"config"}],"axes":{"body":false,"config":true,"unit_test":true},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_dq_summary\n    description: |\n      Data quality summary metrics for monitoring quarantine rates and validation failures.\n      One row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\n      Grain: entity_type\n\n      Use cases:\n      - Data quality monitoring dashboards\n      - Quarantine rate trending\n      - Validation failure root cause analysis\n      - Data quality SLA tracking\n\n    config:\n      tags: ['analytics', 'data_quality', 'marts']\n    meta:\n      grain: entity_type\n      owner: healthcare_analytics\n      uses:\n        - \"Data quality dashboards\"\n        - \"Quarantine monitoring\"\n        - \"Data quality alerting\"\n\n    columns:\n      - name: entity_type\n        description: \"Type of entity being measured (encounters, medications)\"\n        tests:\n          - not_null\n          - accepted_values:\n              values: ['encounters', 'medications']\n\n      - name: quarantined_count\n        description: \"Number of records failing data quality validations\"\n        tests:\n          - not_null\n\n      - name: total_count\n        description: \"Total number of records (valid + quarantined)\"\n        tests:\n          - not_null\n\n      - name: quarantine_rate_pct\n        description: \"Percentage of records quarantined (quarantined_count / total_count * 100)\"\n        tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 100\n\n      - name: failed_timestamp_validations\n        description: \"Count of records failing timestamp/date sequence validations\"\n\n      - name: failed_future_date_validations\n        description: \"Count of records with future dates\"\n\n      - name: failed_historical_end_validations\n        description: \"Count of records with end dates before 1900\"\n\n      - name: failed_historical_start_validations\n        description: \"Count of records with start dates before 1900\"\n\n      - name: _generated_at\n        description: \"Timestamp when summary was generated\""},"tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 1 — the slicer attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test (no blank-line gap below) to this test's authored slice\n  # (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_combines_encounter_and_medication_metrics\n    description: |\n      mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\n      and stg_synthea__medications. This test mocks two encounter rows\n      (one invalid) and two medication rows (both invalid), then asserts\n      the per-entity counts + quarantine rate. Uses csv format for both\n      GIVEN and EXPECT — compact tabular form across many boolean\n      dq-flag columns.\n    model: mart_dq_summary\n    # INSIDE bracket for test 1 — comments at deeper indent than\n    # `- name:` ride along with the body slice as raw text (cute-dbt#69\n    # / #73).\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: csv\n        rows: |\n          is_dq_valid,valid_encounter_timestamps,no_future_encounter_dates,end_after_1900,start_after_1900\n          false,false,true,true,true\n          true,true,true,true,true\n      - input: ref(\"stg_synthea__medications\")\n        format: csv\n        rows: |\n          is_dq_valid,valid_medication_dates,no_future_medication_dates,start_after_1900,end_after_1900_if_present\n          false,false,true,true,true\n          false,true,false,true,true\n    expect:\n      format: csv\n      rows: |\n        entity_type,quarantined_count,total_count,quarantine_rate_pct,failed_timestamp_validations\n        encounters,1,2,50.00,1\n        medications,2,2,100.00,1\n  # TRAILING bracket for test 1 — the comment block butts against the\n  # body above (no blank-line gap) and is terminated below by the blank\n  # line separating the two tests (cute-dbt#69 / #73).","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":true,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. Uses sql format for the GIVEN mocks and dict format\nfor the EXPECT — dict expects only compare listed columns, so\nnon-deterministic outputs (`_generated_at` from\n`current_timestamp`) are excluded from comparison.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 2 — sits between the blank-line\n  # terminator above and `- name:` below with no further gap, so the\n  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_zero_quarantined_when_all_valid\n    description: |\n      mart_dq_summary should report zero quarantined_count and 0.00\n      quarantine_rate_pct when every upstream row passes data-quality\n      validations. Uses sql format for the GIVEN mocks and dict format\n      for the EXPECT — dict expects only compare listed columns, so\n      non-deterministic outputs (`_generated_at` from\n      `current_timestamp`) are excluded from comparison.\n    model: mart_dq_summary\n    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /\n    # #73). Demonstrates that the slicer ride-along applies to every\n    # unit_test, not just the first in the file.\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_encounter_timestamps\n            , true as no_future_encounter_dates\n            , true as end_after_1900\n            , true as start_after_1900\n      - input: ref(\"stg_synthea__medications\")\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_medication_dates\n            , true as no_future_medication_dates\n            , true as start_after_1900\n            , true as end_after_1900_if_present\n    expect:\n      format: dict\n      rows:\n        - entity_type: 'encounters'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n        - entity_type: 'medications'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n    overrides:\n      macros:\n        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"\n      vars:\n        dq_quarantine_threshold: 5\n      env_vars:\n        DBT_DQ_REGION: 'us-east-1'\n  # TRAILING bracket for test 2 — terminated by the blank line\n  # separating it from test 3 (cute-dbt#69 / #73).","yaml_diff":{"lines":[{"kind":"context","text":"  # LEADING bracket for test 2 — sits between the blank-line","emphasis":null},{"kind":"context","text":"  # terminator above and `- name:` below with no further gap, so the","emphasis":null},{"kind":"context","text":"  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).","emphasis":null},{"kind":"context","text":"  - name: test_mart_dq_summary_zero_quarantined_when_all_valid","emphasis":null},{"kind":"context","text":"    description: |","emphasis":null},{"kind":"context","text":"      mart_dq_summary should report zero quarantined_count and 0.00","emphasis":null},{"kind":"context","text":"      quarantine_rate_pct when every upstream row passes data-quality","emphasis":null},{"kind":"context","text":"      validations. Uses sql format for the GIVEN mocks and dict format","emphasis":null},{"kind":"context","text":"      for the EXPECT — dict expects only compare listed columns, so","emphasis":null},{"kind":"context","text":"      non-deterministic outputs (`_generated_at` from","emphasis":null},{"kind":"context","text":"      `current_timestamp`) are excluded from comparison.","emphasis":null},{"kind":"context","text":"    model: mart_dq_summary","emphasis":null},{"kind":"context","text":"    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /","emphasis":null},{"kind":"context","text":"    # #73). Demonstrates that the slicer ride-along applies to every","emphasis":null},{"kind":"context","text":"    # unit_test, not just the first in the file.","emphasis":null},{"kind":"context","text":"    given:","emphasis":null},{"kind":"context","text":"      - input: ref('stg_synthea__encounters')","emphasis":null},{"kind":"context","text":"        format: sql","emphasis":null},{"kind":"context","text":"        rows: |","emphasis":null},{"kind":"context","text":"          select","emphasis":null},{"kind":"context","text":"            true as is_dq_valid","emphasis":null},{"kind":"context","text":"            , true as valid_encounter_timestamps","emphasis":null},{"kind":"context","text":"            , true as no_future_encounter_dates","emphasis":null},{"kind":"context","text":"            , true as end_after_1900","emphasis":null},{"kind":"context","text":"            , true as start_after_1900","emphasis":null},{"kind":"context","text":"      - input: ref(\"stg_synthea__medications\")","emphasis":null},{"kind":"context","text":"        format: sql","emphasis":null},{"kind":"context","text":"        rows: |","emphasis":null},{"kind":"context","text":"          select","emphasis":null},{"kind":"context","text":"            true as is_dq_valid","emphasis":null},{"kind":"context","text":"            , true as valid_medication_dates","emphasis":null},{"kind":"context","text":"            , true as no_future_medication_dates","emphasis":null},{"kind":"context","text":"            , true as start_after_1900","emphasis":null},{"kind":"context","text":"            , true as end_after_1900_if_present","emphasis":null},{"kind":"context","text":"    expect:","emphasis":null},{"kind":"context","text":"      format: dict","emphasis":null},{"kind":"context","text":"      rows:","emphasis":null},{"kind":"context","text":"        - entity_type: 'encounters'","emphasis":null},{"kind":"removed","text":"          quarantined_count: null","emphasis":[29,33]},{"kind":"removed","text":"          total_count: 2","emphasis":[23,24]},{"kind":"added","text":"          quarantined_count: 0","emphasis":[29,30]},{"kind":"added","text":"          total_count: 1","emphasis":[23,24]},{"kind":"context","text":"          quarantine_rate_pct: 0.00","emphasis":null},{"kind":"context","text":"        - entity_type: 'medications'","emphasis":null},{"kind":"removed","text":"          quarantined_count: 1","emphasis":[29,30]},{"kind":"removed","text":"          total_count: 2","emphasis":[23,24]},{"kind":"added","text":"          quarantined_count: 0","emphasis":[29,30]},{"kind":"added","text":"          total_count: 1","emphasis":[23,24]},{"kind":"context","text":"          quarantine_rate_pct: 0.00","emphasis":null},{"kind":"context","text":"    overrides:","emphasis":null},{"kind":"context","text":"      macros:","emphasis":null},{"kind":"context","text":"        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"","emphasis":null},{"kind":"context","text":"      vars:","emphasis":null},{"kind":"removed","text":"        dq_quarantine_threshold: 10","emphasis":[33,35]},{"kind":"added","text":"        dq_quarantine_threshold: 5","emphasis":[33,34]},{"kind":"context","text":"      env_vars:","emphasis":null},{"kind":"context","text":"        DBT_DQ_REGION: 'us-east-1'","emphasis":null},{"kind":"context","text":"  # TRAILING bracket for test 2 — terminated by the blank line","emphasis":null},{"kind":"context","text":"  # separating it from test 3 (cute-dbt#69 / #73).","emphasis":null}]},"data_diff":{"given":[],"expect":{"columns":[{"name":"entity_type","status":"present"},{"name":"quarantine_rate_pct","status":"present"},{"name":"quarantined_count","status":"present"},{"name":"total_count","status":"present"}],"rows":[{"kind":"modified","cells":[{"old":{"display":"encounters","key":{"t":"str","v":"encounters"}},"new":{"display":"encounters","key":{"t":"str","v":"encounters"}},"changed":false},{"old":{"display":"0.00","key":{"t":"number","v":"0"}},"new":{"display":"0.0","key":{"t":"number","v":"0"}},"changed":false},{"old":{"display":"","key":{"t":"null"}},"new":{"display":"0","key":{"t":"number","v":"0"}},"changed":true},{"old":{"display":"2","key":{"t":"number","v":"2"}},"new":{"display":"1","key":{"t":"number","v":"1"}},"changed":true}]},{"kind":"modified","cells":[{"old":{"display":"medications","key":{"t":"str","v":"medications"}},"new":{"display":"medications","key":{"t":"str","v":"medications"}},"changed":false},{"old":{"display":"0.00","key":{"t":"number","v":"0"}},"new":{"display":"0.0","key":{"t":"number","v":"0"}},"changed":false},{"old":{"display":"1","key":{"t":"number","v":"1"}},"new":{"display":"0","key":{"t":"number","v":"0"}},"changed":true},{"old":{"display":"2","key":{"t":"number","v":"2"}},"new":{"display":"1","key":{"t":"number","v":"1"}},"changed":true}]}]}},"overrides":{"env_vars":{"DBT_DQ_REGION":"us-east-1"},"macros":{"dbt.current_timestamp":"'2024-01-15 00:00:00'"},"vars":{"dq_quarantine_threshold":5}},"given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["is_dq_valid","valid_encounter_timestamps","no_future_encounter_dates","end_after_1900","start_after_1900"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_encounter_timestamps\n  , true as no_future_encounter_dates\n  , true as end_after_1900\n  , true as start_after_1900","format":"sql"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["is_dq_valid","valid_medication_dates","no_future_medication_dates","start_after_1900","end_after_1900_if_present"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_medication_dates\n  , true as no_future_medication_dates\n  , true as start_after_1900\n  , true as end_after_1900_if_present","format":"sql"}],"expected":{"table":{"columns":["entity_type","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]}]},"rows":[{"entity_type":"encounters","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1},{"entity_type":"medications","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1}],"format":"dict","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"entity_type"},{"label":"exists but disabled","value":"test.healthcare_analytics.unique_mart_dq_summary_entity_type.4f2a9c7d10 — a uniqueness test on (entity_type) is declared but disabled (config.enabled: false); it asserts nothing and never counts as coverage"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"var_references":[{"name":"dq_quarantine_threshold","tier":"direct"}],"axes":{"body":false,"config":true,"unit_test":true},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_encounter_summary","path":"models/marts/analytics/views/v_encounter_summary.sql","description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"patients","role":"import"},{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"payers","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_encounter_summary.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"providers","to":"final","edge_type":"left"},{"from":"organizations","to":"final","edge_type":"left"},{"from":"payers","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_at\n        , encounter_class\n        , encounter_description\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_name\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , full_name as patient_name\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_name\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_name\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_at\n        , encounter_class\n        , encounter_description\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\npatients as (\n    select\n        patient_key\n        , full_name as patient_name\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_name\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_name\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_name\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\nfinal as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":95,"col":1,"byte":2262},"end":{"line":95,"col":20,"byte":2281}},"encounters":{"start":{"line":5,"col":6,"byte":9},"end":{"line":25,"col":2,"byte":509}},"final":{"start":{"line":56,"col":1,"byte":1041},"end":{"line":93,"col":2,"byte":2260}},"organizations":{"start":{"line":42,"col":1,"byte":793},"end":{"line":47,"col":2,"byte":928}},"patients":{"start":{"line":27,"col":1,"byte":512},"end":{"line":33,"col":2,"byte":668}},"payers":{"start":{"line":49,"col":1,"byte":931},"end":{"line":54,"col":2,"byte":1038}},"providers":{"start":{"line":35,"col":1,"byte":671},"end":{"line":40,"col":2,"byte":790}}},"column_spans":{"(final 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of encounter","documented":true},"encounter_date":{"description":"Date of the encounter","documented":true},"encounter_id":{"description":"Unique encounter identifier","documented":true},"organization_id":{"description":"Organization identifier","documented":true},"organization_name":{"description":"Organization name","documented":true},"patient_id":{"description":"Patient identifier","documented":true},"patient_name":{"description":"Full patient name","documented":true},"patient_responsibility":{"description":"Amount owed by patient","documented":true},"payer_coverage":{"description":"Amount covered by insurance","documented":true},"payer_id":{"description":"Payer identifier","documented":true},"payer_name":{"description":"Payer name","documented":true},"provider_id":{"description":"Provider identifier","documented":true},"provider_name":{"description":"Full provider name","documented":true},"total_cost":{"description":"Total claim 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provider_name\n    from {{ ref('dim_providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_name\n    from {{ ref('dim_organizations') }}\n),\n\npayers as (\n    select\n        payer_key\n        , payer_name\n    from {{ ref('dim_payers') }}\n),\n\nfinal as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_encounter_summary\n    description: |\n      Enriched encounter view with dimension attributes for ad-hoc analysis.\n      Ephemeral (not persisted) - used as a building block for other queries.\n      Joins encounters with patient, provider, organization, and payer details.\n\n      Use cases:\n      - Ad-hoc encounter analysis\n      - Building block for custom reports\n      - Quick encounter lookups with full context\n\n    config:\n      materialized: ephemeral\n      tags: ['analytics', 'marts', 'view']\n    columns:\n      - name: encounter_id\n        description: Unique encounter identifier\n\n      - name: encounter_date\n        description: Date of the encounter\n\n      - name: patient_id\n        description: Patient identifier\n\n      - name: patient_name\n        description: Full patient name\n\n      - name: provider_id\n        description: Provider identifier\n\n      - name: provider_name\n        description: Full provider name\n\n      - name: organization_id\n        description: Organization identifier\n\n      - name: organization_name\n        description: Organization name\n\n      - name: payer_id\n        description: Payer identifier\n\n      - name: payer_name\n        description: Payer name\n\n      - name: encounter_class\n        description: Type of encounter\n\n      - name: total_cost\n        description: Total claim cost\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n\n      - name: patient_responsibility\n        description: Amount owed by patient"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_patient_current_conditions","path":"models/marts/analytics/views/v_patient_current_conditions.sql","description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition 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patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_patient_current_conditions\n    description: |\n      Current (active) conditions for each patient, filtered from fct_condition_cohorts.\n      Use for patient panels, disease management dashboards, and care gap identification.\n      Grain: (patient_id, condition_code) - only active conditions\n\n      Use cases:\n      - Patient panel management\n      - Active disease registries\n      - Care gap identification\n      - Real-time patient condition views\n\n    config:\n      materialized: table\n      tags: ['analytics', 'marts', 'view']\n    meta:\n      grain: patient_id + condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Patient panel displays\"\n        - \"Active condition registries\"\n        - \"Care gap dashboards\"\n      example_queries:\n        - name: \"Patient active conditions lookup\"\n          description: \"Get all active conditions for a specific patient\"\n          sql: |\n            select\n                vpcc.patient_id\n                , vpcc.condition_code\n                , vpcc.condition_description\n                , vpcc.first_diagnosis_date\n                , vpcc.months_with_condition\n                , vpcc.encounter_count\n            from main_marts.v_patient_current_conditions vpcc\n            where vpcc.patient_id = 'patient-uuid-here'\n            order by vpcc.first_diagnosis_date\n\n        - name: \"Active condition summary\"\n          description: \"Count of patients by active condition for disease registries\"\n          sql: |\n            select\n                vpcc.condition_code\n                , vpcc.condition_description\n                , count(distinct vpcc.patient_id) as active_patient_count\n                , round(avg(vpcc.months_with_condition), 1) as avg_duration_months\n                , round(avg(vpcc.encounter_count), 1) as avg_encounters\n            from main_marts.v_patient_current_conditions vpcc\n            group by 1, 2\n            order by active_patient_count desc\n            limit 20\n\n    columns:\n      - name: patient_key\n        description: Foreign key to dim_patients\n\n      - name: patient_id\n        description: Natural patient identifier\n\n      - name: condition_key\n        description: Foreign key to dim_conditions\n        data_tests:\n          - relationships:\n              arguments:\n                to: ref('dim_conditions')\n                field: condition_key\n\n      - name: condition_code\n        description: ICD/SNOMED condition code\n\n      - name: condition_description\n        description: Full condition name\n\n      - name: first_diagnosis_date\n        description: Date of first diagnosis\n\n      - name: months_with_condition\n        description: Duration of condition in months\n\n      - name: encounter_count\n        description: Number of encounters with this condition"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_provider_active_patients","path":"models/marts/analytics/views/v_provider_active_patients.sql","description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","dag":{"nodes":[{"id":"providers","role":"import"},{"id":"current_conditions","role":"import"},{"id":"patient_recent_encounters","role":"import"},{"id":"patient_provider_assignment","role":"transform"},{"id":"active_conditions_by_provider","role":"transform"},{"id":"provider_summary","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_provider_active_patients.sql","role":"final"}],"edges":[{"from":"patient_recent_encounters","to":"patient_provider_assignment","edge_type":"from"},{"from":"current_conditions","to":"active_conditions_by_provider","edge_type":"from"},{"from":"patient_provider_assignment","to":"active_conditions_by_provider","edge_type":"inner"},{"from":"active_conditions_by_provider","to":"provider_summary","edge_type":"from"},{"from":"providers","to":"final","edge_type":"from"},{"from":"provider_summary","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","active_conditions_by_provider":"active_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":96,"col":1,"byte":2429},"end":{"line":96,"col":20,"byte":2448}},"active_conditions_by_provider":{"start":{"line":52,"col":1,"byte":1176},"end":{"line":61,"col":2,"byte":1451}},"current_conditions":{"start":{"line":17,"col":1,"byte":294},"end":{"line":25,"col":2,"byte":523}},"final":{"start":{"line":74,"col":1,"byte":1744},"end":{"line":94,"col":2,"byte":2427}},"patient_provider_assignment":{"start":{"line":42,"col":1,"byte":962},"end":{"line":49,"col":2,"byte":1123}},"patient_recent_encounters":{"start":{"line":28,"col":1,"byte":599},"end":{"line":39,"col":2,"byte":910}},"provider_summary":{"start":{"line":64,"col":1,"byte":1479},"end":{"line":72,"col":2,"byte":1741}},"providers":{"start":{"line":5,"col":6,"byte":9},"end":{"line":14,"col":2,"byte":232}}},"column_spans":{"(final 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Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_provider_active_patients\n    description: |\n      Active patient count per provider for panel management and workload analysis.\n      Aggregates patients with active conditions by their treating provider.\n      Grain: provider_id\n\n      Use cases:\n      - Provider panel size management\n      - Workload distribution analysis\n      - Capacity planning\n      - Provider assignment optimization\n\n    config:\n      materialized: table\n      tags: ['analytics', 'marts', 'view']\n    meta:\n      grain: provider_id\n      owner: healthcare_analytics\n      uses:\n        - \"Provider panel management\"\n        - \"Workload balancing\"\n        - \"Capacity planning\"\n      example_queries:\n        - name: \"Provider panel sizes\"\n          description: \"View provider workload by active patient and condition counts\"\n          sql: |\n            select\n                vpap.provider_id\n                , vpap.provider_name\n                , vpap.active_patient_count\n                , vpap.total_active_conditions\n                , vpap.avg_conditions_per_patient\n            from main_marts.v_provider_active_patients vpap\n            where vpap.active_patient_count > 0\n            order by vpap.active_patient_count desc\n            limit 25\n\n        - name: \"Provider workload distribution\"\n          description: \"Analyze distribution of patient panels across providers\"\n          sql: |\n            select\n                case\n                    when vpap.active_patient_count = 0 then '0'\n                    when vpap.active_patient_count \u003c= 10 then '1-10'\n                    when vpap.active_patient_count \u003c= 50 then '11-50'\n                    when vpap.active_patient_count \u003c= 100 then '51-100'\n                    else '100+'\n                end as panel_size_bucket\n                , count(*) as provider_count\n                , sum(vpap.active_patient_count) as total_patients\n            from main_marts.v_provider_active_patients vpap\n            group by 1\n            order by 1\n\n    columns:\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_name\n        description: Full name of the provider\n\n      - name: organization_key\n        description: Foreign key to dim_organizations\n\n      - name: organization_id\n        description: Natural organization identifier\n\n      - name: active_patient_count\n        description: Number of patients with active conditions\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_active_conditions\n        description: Total number of active conditions managed\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_conditions_per_patient\n        description: Average number of active conditions per patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). Includes an unknown member row (provider_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"provider_id","description":"Natural provider identifier (UUID)","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"enforcement.constraint-unbacked":{"name":"Declared constraint without a backing test","group":"enforcement","tier":"total","instrument":"data-test","conditions":["the model declares a primary_key or unique constraint (model-level constraints[] or a column-level constraint) whose enforcement on the manifest's adapter is metadata-only (NotEnforced) — the warehouse accepts the declaration but does not enforce uniqueness at write time","no enabled generic uniqueness data test (unique, attached to the model) backs the constrained column — the constraint→test edge is INFERRED by column + test-name match, since the manifest never links a constraint to its test","the verdict is UNCOVERED only when the constraint is declared, metadata-only, AND has no inferred backing test; a backing test (even one cute-dbt could miss) keeps it silent"],"exclusions":["a constraint the adapter ENFORCES at write time (e.g. not_null / foreign_key on Postgres/DuckDB) is never a gap — the warehouse guarantees it","a constraint kind with no column-level generic-test backing (check / custom / foreign_key) is out of this inference and never reported here","the inferred edge can MISS a renamed test or a singular/custom test asserting the same uniqueness — the copy says \"backing test\" (an authoring-discipline cue), never that the warehouse lacks an index; columns are authored-YAML-only, so this is never a warehouse-truth claim","the whole `enforcement` group is gated behind the governance experiment — off by default, it never fires on a non-governance report"],"rationale":"A primary-key / unique constraint that the warehouse treats as metadata-only is a DECLARED guarantee with nothing checking it: duplicate rows load silently, and any downstream join or incremental merge that trusts the declared grain corrupts. A backing data test is the only thing that actually verifies the declared uniqueness on this adapter.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/enforcement.constraint-unbacked.html"},"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}},"project_definition":{"name":"healthcare_analytics","version":"1.0.0","require_dbt_version":">=1.8.0","vars":{"default_state_grid_density":"monthly","dq_quarantine_threshold":5},"vars_spans":{"default_state_grid_density":{"line":12,"column":3},"dq_quarantine_threshold":{"line":11,"column":3}},"config_trees":{"models":{"children":{"healthcare_analytics":{"configs":{"materialized":"view"},"children":{"marts":{"configs":{"materialized":"table"}},"staging":{"configs":{"materialized":"view"}}}}}}},"dispatch":[{"macro_namespace":"dbt_utils","search_order":["healthcare_analytics","dbt_utils"]}],"on_run_start":["grant select on all tables in schema analytics to role reporting"],"paths":{"clean-targets":["target","dbt_packages"],"model-paths":["models"],"test-paths":["tests"]},"other":{"profile":"healthcare_analytics"}},"project_change_panel":{"kind":"categorized","changes":[{"category":"vars","label":"default_state_grid_density","old":"weekly","new":"monthly","vars":{"entries":[{"name":"default_state_grid_density","old":"weekly","new":"monthly","config":["model.healthcare_analytics.mart_date_state_grid"]}],"footprint":{"models_scanned":35,"macros_scanned":89,"python_models":0}}},{"category":"vars","label":"dq_quarantine_threshold","old":10,"new":5,"vars":{"entries":[{"name":"dq_quarantine_threshold","old":10,"new":5,"direct":["model.healthcare_analytics.mart_dq_summary"],"via_macros":[{"model":"model.healthcare_analytics.stg_synthea__encounters","via":"macro.healthcare_analytics.add_dq_flags"},{"model":"model.healthcare_analytics.stg_synthea__medications","via":"macro.healthcare_analytics.add_dq_flags"}],"insulated_tests":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]}],"footprint":{"models_scanned":35,"macros_scanned":89,"python_models":0}}},{"category":"config_tree","label":"models.healthcare_analytics.marts: +materialized","old":"view","new":"table","tree":{"section":"models","segments":["healthcare_analytics","marts"],"key":"materialized"}},{"category":"dispatch","label":"dispatch","old":[{"macro_namespace":"dbt_utils","search_order":["dbt_utils","healthcare_analytics"]}],"new":[{"macro_namespace":"dbt_utils","search_order":["healthcare_analytics","dbt_utils"]}]},{"category":"hooks","label":"on-run-start","old":["grant usage on schema analytics to role reporting"],"new":["grant select on all tables in schema analytics to role reporting"],"hook":{"sql_diff":{"lines":[{"kind":"removed","text":"grant usage on schema analytics to role reporting","emphasis":[6,13]},{"kind":"added","text":"grant select on all tables in schema analytics to role reporting","emphasis":[6,28]}]},"operation_ids":["operation.healthcare_analytics.healthcare_analytics-on-run-start-0"],"manifest":"matched"}}]},"governance":{"group_chips":[{"group":"clinical_quality","owner_name":"Clinical Quality Team","owner_email":"clinical-quality@example.com"}],"blast_radius":[{"exposure_label":"provider_quality_dashboard","exposure_type":"dashboard","owner_name":"Clinical Quality Team","owner_email":"clinical-quality@example.com","in_scope_model_count":7}],"lifecycle_chips":[{"kind":"group-owner-touch","label":"Touches group clinical_quality (owner: clinical-quality@example.com)"}],"meta_tags":[{"model":"dim_conditions","tags":["analytics","dimension","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify the most prevalent conditions in the patient population\",\"name\":\"Top 10 most common conditions\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , patient_count\\n    , encounter_count\\nfrom main_marts.dim_conditions\\norder by patient_count desc\\nlimit 10\"},{\"description\":\"Find conditions that drive the most healthcare utilization\",\"name\":\"Conditions by encounter frequency\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , encounter_count\\n    , patient_count\\n    , round(encounter_count * 1.0 / patient_count, 2) as avg_encounters_per_patient\\nfrom main_marts.dim_conditions\\nwhere patient_count > 0\\norder by encounter_count desc\\nlimit 20\"},{\"description\":\"Find conditions first diagnosed in the last year\",\"name\":\"Recently diagnosed conditions\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , first_occurrence_date\\n    , last_occurrence_date\\n    , patient_count\\nfrom main_marts.dim_conditions\\nwhere first_occurrence_date >= current_date - interval '1 year'\\norder by first_occurrence_date desc\"}]"},{"key":"grain","value":"condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Condition lookup and filtering\",\"Cohort analysis by diagnosis\",\"Disease prevalence reporting\"]"}],"columns":[]},{"model":"dim_payers","tags":[],"meta":[],"columns":[{"column":"payer_key","tags":["dimension_key"],"meta":[{"key":"owner","value":"clinical-quality"},{"key":"pii","value":"false"}]}]},{"model":"fct_condition_cohorts","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Find conditions with the most patients for population health programs\",\"name\":\"Largest condition cohorts\",\"sql\":\"select\\n    fcc.condition_code\\n    , fcc.condition_description\\n    , count(distinct fcc.patient_id) as patient_count\\n    , sum(fcc.encounter_count) as total_encounters\\n    , round(avg(fcc.months_with_condition), 1) as avg_months_with_condition\\n    , sum(case when fcc.is_active then 1 else 0 end) as active_patients\\nfrom main_marts.fct_condition_cohorts fcc\\ngroup by 1, 2\\norder by patient_count desc\\nlimit 15\"},{\"description\":\"Identify patients with long-standing conditions for care management\",\"name\":\"Chronic condition patients (long duration)\",\"sql\":\"select\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , fcc.condition_description\\n    , fcc.first_diagnosis_date\\n    , fcc.months_with_condition\\n    , fcc.encounter_count\\n    , fcc.is_active\\nfrom main_marts.fct_condition_cohorts fcc\\njoin main_marts.dim_patients p\\n    on fcc.patient_key = p.patient_key\\nwhere fcc.months_with_condition >= 24\\n    and fcc.is_active = true\\norder by fcc.months_with_condition desc\\nlimit 50\"},{\"description\":\"Find patients with comorbidities for complex care programs\",\"name\":\"Patients with multiple conditions\",\"sql\":\"with patient_condition_counts as (\\n    select\\n        patient_id\\n        , patient_key\\n        , count(distinct condition_code) as condition_count\\n        , sum(case when is_active then 1 else 0 end) as active_condition_count\\n    from main_marts.fct_condition_cohorts\\n    group by 1, 2\\n)\\nselect\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , pcc.condition_count\\n    , pcc.active_condition_count\\nfrom patient_condition_counts pcc\\njoin main_marts.dim_patients p\\n    on pcc.patient_key = p.patient_key\\nwhere pcc.condition_count >= 3\\norder by pcc.condition_count desc\\nlimit 25\"}]"},{"key":"grain","value":"patient_id + condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Disease management programs\",\"Cohort identification\",\"Chronic condition tracking\",\"Care gap analysis\"]"}],"columns":[]},{"model":"fct_cost_analysis","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Analyze cost distribution across different insurance payers\",\"name\":\"Cost breakdown by payer\",\"sql\":\"select\\n    py.payer_name\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(sum(fca.payer_coverage), 2) as total_payer_coverage\\n    , round(sum(fca.patient_responsibility), 2) as total_patient_responsibility\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\njoin main_marts.dim_payers py\\n    on fca.payer_key = py.payer_key\\ngroup by py.payer_name\\norder by total_cost desc\"},{\"description\":\"Track cost trends over time for financial forecasting\",\"name\":\"Monthly cost trends\",\"sql\":\"select\\n    strftime(fca.encounter_date, '%Y-%m') as year_month\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(avg(fca.total_cost), 2) as avg_cost_per_encounter\\n    , round(sum(fca.payer_coverage), 2) as total_payer_coverage\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\nwhere fca.year_actual >= 2023\\ngroup by 1\\norder by 1 desc\"},{\"description\":\"Compare costs across different encounter classes\",\"name\":\"Cost by encounter type\",\"sql\":\"select\\n    fca.encounter_class\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(avg(fca.total_cost), 2) as avg_cost\\n    , round(avg(fca.procedure_count), 2) as avg_procedures\\n    , round(avg(fca.cost_per_procedure), 2) as avg_cost_per_procedure\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\ngroup by fca.encounter_class\\norder by total_cost desc\"},{\"description\":\"Identify individual high-cost encounters for review\",\"name\":\"High-cost encounters\",\"sql\":\"select\\n    fca.encounter_id\\n    , fca.encounter_date\\n    , fca.encounter_class\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , py.payer_name\\n    , fca.total_cost\\n    , fca.payer_coverage\\n    , fca.patient_responsibility\\n    , fca.procedure_count\\nfrom main_marts.fct_cost_analysis fca\\njoin main_marts.dim_patients p\\n    on fca.patient_key = p.patient_key\\njoin main_marts.dim_payers py\\n    on fca.payer_key = py.payer_key\\norder by fca.total_cost desc\\nlimit 25\"}]"},{"key":"grain","value":"encounter_id"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Financial reporting\",\"Payer mix analysis\",\"Cost allocation tracking\",\"Revenue cycle management\"]"}],"columns":[]},{"model":"fct_patient_summary","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify high-cost patients for care management programs\",\"name\":\"Top 10 costliest patients by year\",\"sql\":\"select\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , fps.year_actual\\n    , fps.total_cost\\n    , fps.encounter_count\\n    , fps.condition_count\\n    , fps.payer_coverage_pct\\nfrom main_marts.fct_patient_summary fps\\njoin main_marts.dim_patients p\\n    on fps.patient_key = p.patient_key\\nwhere fps.year_actual = 2024\\norder by fps.total_cost desc\\nlimit 10\"},{\"description\":\"Analyze patient population distribution and costs by age cohort\",\"name\":\"Patient count and cost by age group\",\"sql\":\"select\\n    case\\n        when fps.patient_age_at_year_end < 18 then '0-17'\\n        when fps.patient_age_at_year_end < 35 then '18-34'\\n        when fps.patient_age_at_year_end < 50 then '35-49'\\n        when fps.patient_age_at_year_end < 65 then '50-64'\\n        else '65+'\\n    end as age_group\\n    , count(distinct fps.patient_id) as patient_count\\n    , round(avg(fps.total_cost), 2) as avg_annual_cost\\n    , round(sum(fps.total_cost), 2) as total_cost\\n    , round(avg(fps.encounter_count), 2) as avg_encounters\\nfrom main_marts.fct_patient_summary fps\\nwhere fps.year_actual = 2024\\ngroup by 1\\norder by 1\"},{\"description\":\"Year-over-year comparison of costs and utilization metrics\",\"name\":\"Cost and utilization trends over time\",\"sql\":\"select\\n    fps.year_actual\\n    , count(distinct fps.patient_id) as patient_count\\n    , round(avg(fps.encounter_count), 2) as avg_encounters_per_patient\\n    , round(avg(fps.condition_count), 2) as avg_conditions_per_patient\\n    , round(avg(fps.total_cost), 2) as avg_annual_cost\\n    , round(sum(fps.total_cost), 2) as total_population_cost\\n    , round(avg(fps.payer_coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_patient_summary fps\\ngroup by fps.year_actual\\norder by fps.year_actual desc\"},{\"description\":\"Find patients with above-average encounters and conditions\",\"name\":\"High utilization patients\",\"sql\":\"with averages as (\\n    select\\n        avg(encounter_count) as avg_encounters\\n        , avg(condition_count) as avg_conditions\\n    from main_marts.fct_patient_summary\\n    where year_actual = 2024\\n)\\nselect\\n    fps.patient_id\\n    , fps.encounter_count\\n    , fps.condition_count\\n    , fps.total_cost\\n    , fps.unique_providers\\nfrom main_marts.fct_patient_summary fps\\ncross join averages a\\nwhere fps.year_actual = 2024\\n    and fps.encounter_count > a.avg_encounters * 2\\n    and fps.condition_count > a.avg_conditions\\norder by fps.total_cost desc\\nlimit 25\"}]"},{"key":"grain","value":"patient_id + year_actual"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Population health reporting\",\"Cost and utilization analysis\",\"Patient stratification\",\"Year-over-year trending\"]"}],"columns":[]},{"model":"fct_provider_metrics","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify highest-volume providers for capacity planning\",\"name\":\"Top providers by patient volume\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\norder by total_encounters desc\\nlimit 15\"},{\"description\":\"Track provider productivity over time\",\"name\":\"Provider metrics trend by month\",\"sql\":\"select\\n    fpm.year_month_str\\n    , count(distinct fpm.provider_id) as active_providers\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\\nfrom main_marts.fct_provider_metrics fpm\\nwhere fpm.year_actual >= 2023\\ngroup by fpm.year_month_str\\norder by fpm.year_month_str desc\"},{\"description\":\"Compare providers by cost and volume efficiency\",\"name\":\"Provider efficiency comparison\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , round(sum(fpm.total_cost), 2) as total_cost\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\\n    , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\nhaving sum(fpm.encounter_count) >= 10\\norder by avg_cost_per_encounter\"}]"},{"key":"grain","value":"provider_id + year_month_str"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider productivity reporting\",\"Workload balancing\",\"Cost efficiency analysis\",\"Capacity planning\"]"}],"columns":[]},{"model":"fct_provider_scorecard","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify highest-volume providers for capacity planning\",\"name\":\"Top providers by patient volume\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\norder by total_encounters desc\\nlimit 15\"},{\"description\":\"Track provider productivity over time\",\"name\":\"Provider metrics trend by month\",\"sql\":\"select\\n    fpm.year_month_str\\n    , count(distinct fpm.provider_id) as active_providers\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\\nfrom main_marts.fct_provider_metrics fpm\\nwhere fpm.year_actual >= 2023\\ngroup by fpm.year_month_str\\norder by fpm.year_month_str desc\"},{\"description\":\"Compare providers by cost and volume efficiency\",\"name\":\"Provider efficiency comparison\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , round(sum(fpm.total_cost), 2) as total_cost\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\\n    , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\nhaving sum(fpm.encounter_count) >= 10\\norder by avg_cost_per_encounter\"}]"},{"key":"grain","value":"provider_id + year_month_str"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider productivity reporting\",\"Workload balancing\",\"Cost efficiency analysis\",\"Capacity planning\"]"}],"columns":[]},{"model":"mart_date_state_grid","tags":["analytics","marts","reporting"],"meta":[{"key":"grain","value":"month_key x state"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Dense reporting grids\",\"Zero-activity gap detection\"]"}],"columns":[]},{"model":"mart_dq_summary","tags":["analytics","data_quality","marts"],"meta":[{"key":"grain","value":"entity_type"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Data quality dashboards\",\"Quarantine monitoring\",\"Data quality alerting\"]"}],"columns":[]},{"model":"v_encounter_summary","tags":["analytics","marts","view"],"meta":[],"columns":[]},{"model":"v_patient_current_conditions","tags":["analytics","marts","view"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Get all active conditions for a specific patient\",\"name\":\"Patient active conditions lookup\",\"sql\":\"select\\n    vpcc.patient_id\\n    , vpcc.condition_code\\n    , vpcc.condition_description\\n    , vpcc.first_diagnosis_date\\n    , vpcc.months_with_condition\\n    , vpcc.encounter_count\\nfrom main_marts.v_patient_current_conditions vpcc\\nwhere vpcc.patient_id = 'patient-uuid-here'\\norder by vpcc.first_diagnosis_date\"},{\"description\":\"Count of patients by active condition for disease registries\",\"name\":\"Active condition summary\",\"sql\":\"select\\n    vpcc.condition_code\\n    , vpcc.condition_description\\n    , count(distinct vpcc.patient_id) as active_patient_count\\n    , round(avg(vpcc.months_with_condition), 1) as avg_duration_months\\n    , round(avg(vpcc.encounter_count), 1) as avg_encounters\\nfrom main_marts.v_patient_current_conditions vpcc\\ngroup by 1, 2\\norder by active_patient_count desc\\nlimit 20\"}]"},{"key":"grain","value":"patient_id + condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Patient panel displays\",\"Active condition registries\",\"Care gap dashboards\"]"}],"columns":[]},{"model":"v_provider_active_patients","tags":["analytics","marts","view"],"meta":[{"key":"example_queries","value":"[{\"description\":\"View provider workload by active patient and condition counts\",\"name\":\"Provider panel sizes\",\"sql\":\"select\\n    vpap.provider_id\\n    , vpap.provider_name\\n    , vpap.active_patient_count\\n    , vpap.total_active_conditions\\n    , vpap.avg_conditions_per_patient\\nfrom main_marts.v_provider_active_patients vpap\\nwhere vpap.active_patient_count > 0\\norder by vpap.active_patient_count desc\\nlimit 25\"},{\"description\":\"Analyze distribution of patient panels across providers\",\"name\":\"Provider workload distribution\",\"sql\":\"select\\n    case\\n        when vpap.active_patient_count = 0 then '0'\\n        when vpap.active_patient_count \u003c= 10 then '1-10'\\n        when vpap.active_patient_count \u003c= 50 then '11-50'\\n        when vpap.active_patient_count \u003c= 100 then '51-100'\\n        else '100+'\\n    end as panel_size_bucket\\n    , count(*) as provider_count\\n    , sum(vpap.active_patient_count) as total_patients\\nfrom main_marts.v_provider_active_patients vpap\\ngroup by 1\\norder by 1\"}]"},{"key":"grain","value":"provider_id"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider panel management\",\"Workload balancing\",\"Capacity planning\"]"}],"columns":[]}]},"macro_lens":{"macros":[{"name":"quarantine_filter","package":"healthcare_analytics","path":"macros/data_quality/quarantine_filter.sql","diff":{"lines":[{"kind":"context","text":"{% macro quarantine_filter(enabled=true, field_name='is_dq_valid') %}","emphasis":null},{"kind":"context","text":"    {% if enabled %}","emphasis":null},{"kind":"removed","text":"    where {{ field_name }} is true","emphasis":[27,29]},{"kind":"added","text":"    where {{ field_name }} = true","emphasis":[27,28]},{"kind":"context","text":"    {% endif %}","emphasis":null},{"kind":"context","text":"{% endmacro %}","emphasis":null}]},"body_lines":[{"kind":"context","text":"{% macro quarantine_filter(enabled=true, field_name='is_dq_valid') %}","emphasis":null},{"kind":"context","text":"    {% if enabled %}","emphasis":null},{"kind":"context","text":"    where {{ field_name }} = true","emphasis":null},{"kind":"context","text":"    {% endif %}","emphasis":null},{"kind":"context","text":"{% endmacro %}","emphasis":null}],"impacted_count":3,"tree":[{"kind":"dir","label":"models","depth":0,"model_id":""},{"kind":"dir","label":"intermediate","depth":1,"model_id":""},{"kind":"dir","label":"healthcare","depth":2,"model_id":""},{"kind":"model","label":"int_encounters__enriched","depth":3,"model_id":"model.healthcare_analytics.int_encounters__enriched"},{"kind":"dir","label":"marts","depth":1,"model_id":""},{"kind":"dir","label":"core","depth":2,"model_id":""},{"kind":"model","label":"fct_clinical_events","depth":3,"model_id":"model.healthcare_analytics.fct_clinical_events"},{"kind":"model","label":"fct_encounters","depth":3,"model_id":"model.healthcare_analytics.fct_encounters"}],"impacted_models":[{"model_id":"model.healthcare_analytics.fct_clinical_events","name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","sql_lines":[{"kind":"context","text":"{{","emphasis":null},{"kind":"context","text":"    config(","emphasis":null},{"kind":"context","text":"        materialized='table',","emphasis":null},{"kind":"context","text":"        unique_key='clinical_event_key'","emphasis":null},{"kind":"context","text":"    )","emphasis":null},{"kind":"context","text":"}}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"with valid_encounters as (","emphasis":null},{"kind":"context","text":"    select encounter_id","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__encounters') }}","emphasis":null},{"kind":"context","text":"    {{ quarantine_filter() }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"conditions as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        condition_id as event_id","emphasis":null},{"kind":"context","text":"        , 'CONDITION' as event_type","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , cond.encounter_id","emphasis":null},{"kind":"context","text":"        , condition_start_date as event_start_date","emphasis":null},{"kind":"context","text":"        , condition_end_date as event_end_date","emphasis":null},{"kind":"context","text":"        , condition_code as code","emphasis":null},{"kind":"context","text":"        , 'SNOMED-CT' as code_system","emphasis":null},{"kind":"context","text":"        , condition_description as description","emphasis":null},{"kind":"context","text":"        , cast(null as varchar) as reason_code","emphasis":null},{"kind":"context","text":"        , cast(null as varchar) as reason_description","emphasis":null},{"kind":"context","text":"        , cast(null as decimal(18, 2)) as event_cost","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__conditions') }} as 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, first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n)","unknown_member":"unknown_member as (\n    select\n      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last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , 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where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: dim_conditions\n    description: |\n      Master condition dimension for healthcare analytics.\n      One row per unique ICD/SNOMED condition code, deduplicated from staging.\n      Use for filtering and grouping by condition across fact tables.\n\n      Use cases:\n      - Condition-based cohort analysis\n      - Disease prevalence tracking\n      - Condition frequency and distribution reporting\n      - Join target for condition-related facts\n\n    config:\n      tags: ['analytics', 'marts', 'dimension']\n    meta:\n      grain: condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Condition lookup and filtering\"\n        - \"Cohort analysis by diagnosis\"\n        - \"Disease prevalence reporting\"\n      example_queries:\n        - name: \"Top 10 most common conditions\"\n          description: \"Identify the most prevalent conditions in the patient population\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , patient_count\n                , encounter_count\n            from main_marts.dim_conditions\n            order by patient_count desc\n            limit 10\n\n        - name: \"Conditions by encounter frequency\"\n          description: \"Find conditions that drive the most healthcare utilization\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , encounter_count\n                , patient_count\n                , round(encounter_count * 1.0 / patient_count, 2) as avg_encounters_per_patient\n            from main_marts.dim_conditions\n            where patient_count > 0\n            order by encounter_count desc\n            limit 20\n\n        - name: \"Recently diagnosed conditions\"\n          description: \"Find conditions first diagnosed in the last year\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , first_occurrence_date\n                , last_occurrence_date\n                , patient_count\n            from main_marts.dim_conditions\n            where first_occurrence_date >= current_date - interval '1 year'\n            order by first_occurrence_date desc\n\n    columns:\n      - name: condition_key\n        description: Surrogate key (row_number)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: condition_code\n        description: ICD/SNOMED code (natural key)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: condition_description\n        description: Full condition name/description\n        data_tests:\n          - not_null\n\n      - name: first_occurrence_date\n        description: Earliest date this condition was diagnosed in the dataset\n\n      - name: last_occurrence_date\n        description: Most recent date this condition was diagnosed\n\n      - name: patient_count\n        description: Number of unique patients with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounter_count\n        description: Total number of encounters with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number \u003c= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day \u003c= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number \u003c= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day \u003c= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end 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)\n}}\n\n{#\n    Calendar dimension covering 1909-01-01 to 2025-12-31.\n\n    Note on date_key: Uses YYYYMMDD integer format. Valid range is years 0001-2147483\n    (integer max 2,147,483,647). Safe for all practical date ranges.\n\n    Note on day_of_week: dbt_date uses US convention (Sunday=1, Saturday=7),\n    NOT ISO 8601 (Monday=1). Weekend and holiday logic accounts for this.\n#}\n\nwith date_spine as (\n    {{ dbt_date.get_date_dimension('1909-01-01', '2025-12-31') }}\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_date\n    description: >\n      Calendar dimension covering 1909-01-01 to 2025-12-31.\n      Provides date attributes, day/week/month/quarter/year hierarchies,\n      weekend flags, and US holiday identification.\n    data_tests:\n      # Row count validation - covers 117 years of dates (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 42000\n          config:\n            severity: warn\n    columns:\n      - name: date_key\n        description: Surrogate key in YYYYMMDD integer format (e.g., 20240115)\n        data_tests:\n          - unique\n          - not_null\n          # Date key must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 19090101\n              max_value: 20251231\n              config:\n                severity: warn\n\n      - name: date_actual\n        description: The actual date value\n        data_tests:\n          - unique\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1909-01-01'::date\"\n              max_value: \"'2025-12-31'::date\"\n              config:\n                severity: warn\n\n      - name: day_of_week\n        description: Day of week number (US convention - Sunday=1, Saturday=7)\n        data_tests:\n          - not_null\n          # Day of week must be 1-7 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 7\n              config:\n                severity: warn\n\n      - name: day_name\n        description: Full day name (e.g., Monday, Tuesday)\n        data_tests:\n          - not_null\n\n      - name: day_name_short\n        description: Abbreviated day name (e.g., Mon, Tue)\n        data_tests:\n          - not_null\n\n      - name: day_of_month\n        description: Day of the month (1-31)\n        data_tests:\n          - not_null\n          # Day of month must be 1-31 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 31\n              config:\n                severity: warn\n\n      - name: day_of_year\n        description: Day of the year (1-366)\n        data_tests:\n          - not_null\n          # Day of year must be 1-366 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 366\n              config:\n                severity: warn\n\n      - name: week_of_year\n        description: Week of the year (1-53)\n        data_tests:\n          - not_null\n          # Week of year must be 1-53 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 53\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Month number (1-12)\n        data_tests:\n          - not_null\n          # Month must be 1-12 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 12\n              config:\n                severity: warn\n\n      - name: month_name\n        description: Full month name (e.g., January, February)\n        data_tests:\n          - not_null\n\n      - name: month_name_short\n        description: Abbreviated month name (e.g., Jan, Feb)\n        data_tests:\n          - not_null\n\n      - name: quarter_actual\n        description: Quarter number (1-4)\n        data_tests:\n          - not_null\n          # Quarter must be 1-4 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 4\n              config:\n                severity: warn\n\n      - name: quarter_name\n        description: Quarter name (Q1, Q2, Q3, Q4)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['Q1', 'Q2', 'Q3', 'Q4']\n\n      - name: year_actual\n        description: Year number\n        data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: is_weekend\n        description: Boolean flag indicating if date falls on Saturday or Sunday\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: is_us_holiday\n        description: Boolean flag indicating if date is a recognized US holiday\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: holiday_name\n        description: Name of the US holiday (null if not a holiday)"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_date","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_date_date_key.0869fd48f9"]},"evidence":[{"label":"unique_key","value":"date_key"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_organizations","path":"models/marts/core/dim_organizations.sql","description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_organizations.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":61,"col":1,"byte":1382},"end":{"line":61,"col":20,"byte":1401}},"final":{"start":{"line":55,"col":1,"byte":1293},"end":{"line":59,"col":2,"byte":1380}},"sequenced":{"start":{"line":7,"col":1,"byte":94},"end":{"line":35,"col":2,"byte":648}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":91}},"unknown_member":{"start":{"line":38,"col":1,"byte":708},"end":{"line":53,"col":2,"byte":1290}}},"column_spans":{"(final select)\u001f*":{"start":{"line":61,"col":8,"byte":1389},"end":{"line":61,"col":9,"byte":1390}},"sequenced\u001f_loaded_at":{"start":{"line":32,"col":11,"byte":598},"end":{"line":32,"col":42,"byte":629}},"sequenced\u001faddress":{"start":{"line":17,"col":11,"byte":373},"end":{"line":17,"col":18,"byte":380}},"sequenced\u001fcity":{"start":{"line":18,"col":11,"byte":391},"end":{"line":18,"col":15,"byte":395}},"sequenced\u001flatitude":{"start":{"line":24,"col":11,"byte":478},"end":{"line":24,"col":19,"byte":486}},"sequenced\u001flongitude":{"start":{"line":25,"col":11,"byte":497},"end":{"line":25,"col":20,"byte":506}},"sequenced\u001forganization_id":{"start":{"line":13,"col":11,"byte":296},"end":{"line":13,"col":26,"byte":311}},"sequenced\u001forganization_key":{"start":{"line":10,"col":9,"byte":197},"end":{"line":10,"col":73,"byte":261}},"sequenced\u001forganization_name":{"start":{"line":16,"col":11,"byte":345},"end":{"line":16,"col":28,"byte":362}},"sequenced\u001fphone":{"start":{"line":21,"col":11,"byte":441},"end":{"line":21,"col":16,"byte":446}},"sequenced\u001frevenue":{"start":{"line":28,"col":11,"byte":537},"end":{"line":28,"col":18,"byte":544}},"sequenced\u001fstate":{"start":{"line":19,"col":11,"byte":406},"end":{"line":19,"col":16,"byte":411}},"sequenced\u001futilization":{"start":{"line":29,"col":11,"byte":555},"end":{"line":29,"col":22,"byte":566}},"sequenced\u001fzip_code":{"start":{"line":20,"col":11,"byte":422},"end":{"line":20,"col":19,"byte":430}},"source\u001f*":{"start":{"line":4,"col":12,"byte":30},"end":{"line":4,"col":13,"byte":31}},"unknown_member\u001f_loaded_at":{"start":{"line":52,"col":11,"byte":1257},"end":{"line":52,"col":42,"byte":1288}},"unknown_member\u001faddress":{"start":{"line":43,"col":16,"byte":878},"end":{"line":43,"col":43,"byte":905}},"unknown_member\u001fcity":{"start":{"line":44,"col":16,"byte":921},"end":{"line":44,"col":40,"byte":945}},"unknown_member\u001flatitude":{"start":{"line":48,"col":16,"byte":1087},"end":{"line":48,"col":43,"byte":1114}},"unknown_member\u001flongitude":{"start":{"line":49,"col":16,"byte":1130},"end":{"line":49,"col":44,"byte":1158}},"unknown_member\u001forganization_id":{"start":{"line":41,"col":11,"byte":780},"end":{"line":41,"col":39,"byte":808}},"unknown_member\u001forganization_key":{"start":{"line":40,"col":10,"byte":748},"end":{"line":40,"col":31,"byte":769}},"unknown_member\u001forganization_name":{"start":{"line":42,"col":11,"byte":819},"end":{"line":42,"col":54,"byte":862}},"unknown_member\u001fphone":{"start":{"line":47,"col":16,"byte":1046},"end":{"line":47,"col":41,"byte":1071}},"unknown_member\u001frevenue":{"start":{"line":50,"col":16,"byte":1174},"end":{"line":50,"col":42,"byte":1200}},"unknown_member\u001fstate":{"start":{"line":45,"col":16,"byte":961},"end":{"line":45,"col":41,"byte":986}},"unknown_member\u001futilization":{"start":{"line":51,"col":16,"byte":1216},"end":{"line":51,"col":46,"byte":1246}},"unknown_member\u001fzip_code":{"start":{"line":46,"col":16,"byte":1002},"end":{"line":46,"col":44,"byte":1030}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"city":{"description":"City","documented":true},"latitude":{"description":"Latitude coordinate","documented":true},"longitude":{"description":"Longitude coordinate","documented":true},"organization_id":{"description":"Natural organization identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_id.8ca9bc4237"}],"documented":true},"organization_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_key.a539e5f42a"},{"kind":"unique","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"}],"documented":true},"organization_name":{"description":"Name of the healthcare organization","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_name","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_name.7fb64e1577"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"revenue","min_value":0,"model":"{{ get_where_subquery(ref('dim_organizations')) }}","row_condition":"revenue is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_organizations_revenue__0__revenue_is_not_null.3e63617295"}],"documented":true},"state":{"description":"State (full state 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='organization_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_organizations\n    description: >\n      Organization dimension containing healthcare facilities.\n      Includes an unknown member row (organization_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 800\n          config:\n            severity: warn\n    columns:\n      - name: organization_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: organization_id\n        description: Natural organization identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: organization_name\n        description: Name of the healthcare organization\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State (full state name)\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: latitude\n        description: Latitude coordinate\n\n      - name: longitude\n        description: Longitude coordinate\n\n      - name: revenue\n        description: Total revenue\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: utilization\n        description: Total utilization count\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"utilization is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"]},"evidence":[{"label":"unique_key","value":"organization_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__organizations')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_patients","path":"models/marts/core/dim_patients.sql","description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","dag":{"nodes":[{"id":"snapshot_source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_patients.sql","role":"final"}],"edges":[{"from":"snapshot_source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\n-- depends_on: \"memory\".\"snapshots\".\"snp_patients\"\n-- depends_on: \"memory\".\"main_staging\".\"stg_synthea__patients\"\n\n\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final 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Each row represents a\n    specific version of a patient's record.\n\n    SCD Type 2 Columns:\n    - patient_key: Surrogate key (unique per version)\n    - patient_id: Natural key (same across versions)\n    - valid_from: When this version became active\n    - valid_to: When this version was superseded (null = current)\n    - is_current: True for the active version\n\n    To query current patients only:\n        WHERE is_current = true\n\n    To query patient state at a point in time:\n        WHERE valid_from \u003c= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_patients\n    description: >\n      Patient dimension with SCD Type 2 history tracking.\n      Each row represents a specific version of a patient's record.\n      Includes an unknown member row (patient_key = -1) for handling missing lookups.\n\n      SCD Type 2 columns: patient_key (surrogate), patient_id (natural),\n      valid_from, valid_to, is_current.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 900\n          config:\n            severity: warn\n    columns:\n      - name: patient_key\n        description: Surrogate key (unique per version, -1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_id\n        description: Natural patient identifier (UUID), same across SCD2 versions\n        data_tests:\n          - not_null\n\n      - name: first_name\n        description: Patient's first name\n        data_tests:\n          - not_null\n\n      - name: last_name\n        description: Patient's last name\n        data_tests:\n          - not_null\n\n      - name: full_name\n        description: Patient's full name (first + last)\n        data_tests:\n          - not_null\n\n      - name: name_prefix\n        description: Name prefix (Mr., Mrs., Ms., etc.)\n\n      - name: name_suffix\n        description: Name suffix (Jr., Sr., III, etc.)\n\n      - name: birth_date\n        description: Patient date of birth\n        data_tests:\n          # Date range validation - exclude unknown member (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"birth_date is not null and patient_key != -1\"\n              config:\n                severity: warn\n\n      - name: death_date\n        description: Patient date of death (null if alive)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"death_date is not null\"\n              config:\n                severity: warn\n\n      - name: age_years\n        description: Current age or age at death (in years)\n        data_tests:\n          # Age must be reasonable 0-150 years (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 150\n              row_condition: \"age_years is not null\"\n              config:\n                severity: warn\n\n      - name: is_deceased\n        description: Boolean flag indicating if patient has died\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: gender\n        description: Patient gender (M or F)\n        data_tests:\n          - accepted_values:\n              values: ['M', 'F']\n              config:\n                where: \"patient_key != -1\"\n\n      - name: race\n        description: Patient race\n\n      - name: ethnicity\n        description: Patient ethnicity (hispanic or nonhispanic)\n\n      - name: marital_status\n        description: Marital status (M=Married, S=Single, or blank)\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City of residence\n\n      - name: state\n        description: State of residence (full state name)\n\n      - name: county\n        description: County of residence\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: latitude\n        description: Latitude coordinate of residence\n\n      - name: longitude\n        description: Longitude coordinate of residence\n\n      - name: healthcare_expenses\n        description: Total healthcare expenses incurred\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_expenses is not null\"\n              config:\n                severity: warn\n\n      - name: healthcare_coverage\n        description: Total healthcare coverage received\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: valid_from\n        description: Date when this SCD2 version became active\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: valid_to\n        description: Date when this SCD2 version was superseded (null = current)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"valid_to is not null\"\n              config:\n                severity: warn\n\n      - name: is_current\n        description: Boolean flag indicating if this is the current version\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final 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This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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_member\u001funcovered_encounters":{"start":{"line":71,"col":16,"byte":1815},"end":{"line":71,"col":55,"byte":1854}},"unknown_member\u001funcovered_immunizations":{"start":{"line":77,"col":16,"byte":2144},"end":{"line":77,"col":58,"byte":2186}},"unknown_member\u001funcovered_medications":{"start":{"line":73,"col":16,"byte":1924},"end":{"line":73,"col":56,"byte":1964}},"unknown_member\u001funcovered_procedures":{"start":{"line":75,"col":16,"byte":2033},"end":{"line":75,"col":55,"byte":2072}},"unknown_member\u001funique_customers":{"start":{"line":78,"col":16,"byte":2202},"end":{"line":78,"col":51,"byte":2237}},"unknown_member\u001fzip_code":{"start":{"line":65,"col":16,"byte":1535},"end":{"line":65,"col":44,"byte":1563}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total amount)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_rate","max_value":1,"min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"coverage_rate is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_coverage_rate__1__0__coverage_rate_is_not_null.8999cddc49"}],"documented":true},"covered_encounters":{"description":"Number of encounters covered","documented":true},"covered_immunizations":{"description":"Number of immunizations covered","documented":true},"covered_medications":{"description":"Number of medications covered","documented":true},"covered_procedures":{"description":"Number of procedures covered","documented":true},"member_months":{"description":"Total member months","documented":true},"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_id","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue collected","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"revenue","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"revenue is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_revenue__0__revenue_is_not_null.0bff623461"}],"documented":true},"state":{"description":"State","documented":true},"uncovered_encounters":{"description":"Number of encounters not covered","documented":true},"uncovered_immunizations":{"description":"Number of immunizations not covered","documented":true},"uncovered_medications":{"description":"Number of medications not covered","documented":true},"uncovered_procedures":{"description":"Number of procedures not covered","documented":true},"unique_customers":{"description":"Number of unique customers","documented":true},"zip_code":{"description":"ZIP 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_payers\n    description: >\n      Payer dimension containing insurance payers.\n      Includes an unknown member row (payer_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 5\n          config:\n            severity: warn\n    columns:\n      - name: payer_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: payer_id\n        description: Natural payer identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: payer_name\n        description: Name of the insurance payer\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: amount_covered\n        description: Total amount covered by payer\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"amount_covered is not null\"\n              config:\n                severity: warn\n\n      - name: amount_uncovered\n        description: Total amount not covered by payer\n\n      - name: revenue\n        description: Total revenue collected\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: covered_encounters\n        description: Number of encounters covered\n\n      - name: uncovered_encounters\n        description: Number of encounters not covered\n\n      - name: covered_medications\n        description: Number of medications covered\n\n      - name: uncovered_medications\n        description: Number of medications not covered\n\n      - name: covered_procedures\n        description: Number of procedures covered\n\n      - name: uncovered_procedures\n        description: Number of procedures not covered\n\n      - name: covered_immunizations\n        description: Number of immunizations covered\n\n      - name: uncovered_immunizations\n        description: Number of immunizations not covered\n\n      - name: unique_customers\n        description: Number of unique customers\n\n      - name: average_quality_of_life_score\n        description: Average quality of life score for members\n\n      - name: member_months\n        description: Total member months\n\n      - name: coverage_rate\n        description: Derived coverage rate (amount_covered / total amount)\n        data_tests:\n          # Coverage rate must be 0-1 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 1\n              row_condition: \"coverage_rate is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt's authoring-YAML drawer (since #69) attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test to the test's authored slice. This block demonstrates the\n  # LEADING bracket — useful for stating the test's intent before the\n  # reader sees the assertion (cute-dbt#73).\n  - name: test_dim_payers_injects_unknown_sentinel\n    description: |\n      dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\n      with the sequenced source rows. This test verifies the sentinel\n      survives the union and the sequenced rows are surrogate-keyed\n      starting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\n      dict format only compares listed columns, so non-deterministic\n      outputs (`_loaded_at` via `current_timestamp`) are excluded from\n      assertion automatically.\n    model: dim_payers\n    # The INSIDE bracket — comments at deeper indent than `- name:` ride\n    # along with the body slice as raw text. Cute-dbt preserves them\n    # verbatim in the Authoring YAML drawer (cute-dbt#73).\n    given:\n      - input: ref('stg_synthea__payers')\n        format: dict\n        rows:\n          - payer_id: 'a00000aa-0000-0000-0000-000000000001'\n            payer_name: 'Acme Health'\n            address: '1 Acme Way'\n            city: 'Boston'\n            state: 'MA'\n            zip_code: '02101'\n            phone: '555-0100'\n            amount_covered: 1000.0\n            amount_uncovered: 250.0\n            revenue: 1250.0\n            covered_encounters: 10\n            uncovered_encounters: 2\n            covered_medications: 5\n            uncovered_medications: 1\n            covered_procedures: 3\n            uncovered_procedures: 0\n            covered_immunizations: 4\n            uncovered_immunizations: 0\n            unique_customers: 8\n            average_quality_of_life_score: 0.85\n            member_months: 96\n    expect:\n      format: dict\n      rows:\n        - payer_key: -1\n          payer_id: 'UNKNOWN'\n          payer_name: 'Unknown Payer'\n        - payer_key: 1\n          payer_id: 'a00000aa-0000-0000-0000-000000000001'\n          payer_name: 'Acme Health'\n  # The TRAILING bracket — contiguous `#`-comment lines at the SAME\n  # indent as `- name:`, immediately following the test body and\n  # terminated by EOF or a blank line, are attached to this test's\n  # slice. Useful for cross-references and follow-up notes (cute-dbt#73).","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme Way"}},{"display":"1000.0","key":{"t":"number","v":"1000"}},{"display":"250.0","key":{"t":"number","v":"250"}},{"display":"0.85","key":{"t":"number","v":"0.85"}},{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"10","key":{"t":"number","v":"10"}},{"display":"4","key":{"t":"number","v":"4"}},{"display":"5","key":{"t":"number","v":"5"}},{"display":"3","key":{"t":"number","v":"3"}},{"display":"96","key":{"t":"number","v":"96"}},{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}},{"display":"555-0100","key":{"t":"str","v":"555-0100"}},{"display":"1250.0","key":{"t":"number","v":"1250"}},{"display":"MA","key":{"t":"str","v":"MA"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"8","key":{"t":"number","v":"8"}},{"display":"02101","key":{"t":"str","v":"02101"}}]}]},"rows":[{"address":"1 Acme Way","amount_covered":1000.0,"amount_uncovered":250.0,"average_quality_of_life_score":0.85,"city":"Boston","covered_encounters":10,"covered_immunizations":4,"covered_medications":5,"covered_procedures":3,"member_months":96,"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_name":"Acme Health","phone":"555-0100","revenue":1250.0,"state":"MA","uncovered_encounters":2,"uncovered_immunizations":0,"uncovered_medications":1,"uncovered_procedures":0,"unique_customers":8,"zip_code":"02101"}],"format":"dict","column_meta":{"address":{"description":"Street address"},"amount_covered":{"description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"amount_uncovered":{"description":"Total amount not covered by payer"},"average_quality_of_life_score":{"description":"Average quality of life score for members"},"city":{"description":"City"},"covered_encounters":{"description":"Number of encounters covered"},"covered_immunizations":{"description":"Number of immunizations covered"},"covered_medications":{"description":"Number of medications covered"},"covered_procedures":{"description":"Number of procedures covered"},"member_months":{"description":"Total member months"},"payer_id":{"description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]},"phone":{"description":"Phone number"},"revenue":{"description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"state":{"description":"State where headquartered"},"uncovered_encounters":{"description":"Number of encounters not covered"},"uncovered_immunizations":{"description":"Number of immunizations not covered"},"uncovered_medications":{"description":"Number of medications not covered"},"uncovered_procedures":{"description":"Number of procedures not covered"},"unique_customers":{"description":"Number of unique customers"},"zip_code":{"description":"ZIP code (stored as numeric, 5 digits)"}}}],"expected":{"table":{"columns":["payer_id","payer_key","payer_name"],"rows":[{"cells":[{"display":"UNKNOWN","key":{"t":"str","v":"UNKNOWN"}},{"display":"-1","key":{"t":"number","v":"-1"}},{"display":"Unknown Payer","key":{"t":"str","v":"Unknown Payer"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}}]}]},"rows":[{"payer_id":"UNKNOWN","payer_key":-1,"payer_name":"Unknown Payer"},{"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_key":1,"payer_name":"Acme Health"}],"format":"dict","column_meta":{"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_payers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"]},"evidence":[{"label":"unique_key","value":"payer_key"}],"degraded":[{"by":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2","causes":["severity: warn — a failing run warns instead of failing, so this backing is advisory, not gating"]}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_payers","construct":"union[final]","verdict":{"status":"unknown"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"suppressed":{"source":"pragma","reason":"unknown_member is a constant sentinel arm, exercised by design"},"pin_node":"final"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_providers","path":"models/marts/core/dim_providers.sql","description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). Includes an unknown member row (provider_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_providers.sql","role":"final"}],"edges":[{"from":"providers","to":"sequenced","edge_type":"from"},{"from":"organizations","to":"sequenced","edge_type":"left"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","providers":"providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n)","unknown_member":"unknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":76,"col":1,"byte":1766},"end":{"line":76,"col":20,"byte":1785}},"final":{"start":{"line":70,"col":1,"byte":1677},"end":{"line":74,"col":2,"byte":1764}},"organizations":{"start":{"line":7,"col":1,"byte":93},"end":{"line":12,"col":2,"byte":226}},"providers":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":90}},"sequenced":{"start":{"line":14,"col":1,"byte":229},"end":{"line":48,"col":2,"byte":972}},"unknown_member":{"start":{"line":51,"col":1,"byte":1032},"end":{"line":68,"col":2,"byte":1674}}},"column_spans":{"(final select)\u001f*":{"start":{"line":76,"col":8,"byte":1773},"end":{"line":76,"col":9,"byte":1774}},"organizations\u001forganization_id":{"start":{"line":10,"col":11,"byte":158},"end":{"line":10,"col":26,"byte":173}},"organizations\u001forganization_key":{"start":{"line":9,"col":9,"byte":131},"end":{"line":9,"col":25,"byte":147}},"providers\u001f*":{"start":{"line":4,"col":12,"byte":33},"end":{"line":4,"col":13,"byte":34}},"sequenced\u001f_loaded_at":{"start":{"line":43,"col":11,"byte":838},"end":{"line":43,"col":42,"byte":869}},"sequenced\u001faddress":{"start":{"line":32,"col":11,"byte":654},"end":{"line":32,"col":20,"byte":663}},"sequenced\u001fcity":{"start":{"line":33,"col":11,"byte":674},"end":{"line":33,"col":17,"byte":680}},"sequenced\u001fgender":{"start":{"line":28,"col":11,"byte":592},"end":{"line":28,"col":19,"byte":600}},"sequenced\u001flatitude":{"start":{"line":36,"col":11,"byte":730},"end":{"line":36,"col":21,"byte":740}},"sequenced\u001flongitude":{"start":{"line":37,"col":11,"byte":751},"end":{"line":37,"col":22,"byte":762}},"sequenced\u001forganization_id":{"start":{"line":24,"col":11,"byte":515},"end":{"line":24,"col":28,"byte":532}},"sequenced\u001forganization_key":{"start":{"line":23,"col":11,"byte":486},"end":{"line":23,"col":29,"byte":504}},"sequenced\u001fprovider_id":{"start":{"line":20,"col":11,"byte":425},"end":{"line":20,"col":24,"byte":438}},"sequenced\u001fprovider_key":{"start":{"line":17,"col":9,"byte":332},"end":{"line":17,"col":67,"byte":390}},"sequenced\u001fprovider_name":{"start":{"line":27,"col":11,"byte":566},"end":{"line":27,"col":26,"byte":581}},"sequenced\u001fspecialty":{"start":{"line":29,"col":11,"byte":611},"end":{"line":29,"col":22,"byte":622}},"sequenced\u001fstate":{"start":{"line":34,"col":11,"byte":691},"end":{"line":34,"col":18,"byte":698}},"sequenced\u001futilization":{"start":{"line":40,"col":11,"byte":793},"end":{"line":40,"col":24,"byte":806}},"sequenced\u001fzip_code":{"start":{"line":35,"col":11,"byte":709},"end":{"line":35,"col":21,"byte":719}},"unknown_member\u001f_loaded_at":{"start":{"line":67,"col":11,"byte":1641},"end":{"line":67,"col":42,"byte":1672}},"unknown_member\u001faddress":{"start":{"line":60,"col":16,"byte":1345},"end":{"line":60,"col":43,"byte":1372}},"unknown_member\u001fcity":{"start":{"line":61,"col":16,"byte":1388},"end":{"line":61,"col":40,"byte":1412}},"unknown_member\u001fgender":{"start":{"line":58,"col":16,"byte":1258},"end":{"line":58,"col":42,"byte":1284}},"unknown_member\u001flatitude":{"start":{"line":64,"col":16,"byte":1513},"end":{"line":64,"col":43,"byte":1540}},"unknown_member\u001flongitude":{"start":{"line":65,"col":16,"byte":1556},"end":{"line":65,"col":44,"byte":1584}},"unknown_member\u001forganization_id":{"start":{"line":56,"col":11,"byte":1168},"end":{"line":56,"col":39,"byte":1196}},"unknown_member\u001forganization_key":{"start":{"line":55,"col":12,"byte":1136},"end":{"line":55,"col":33,"byte":1157}},"unknown_member\u001fprovider_id":{"start":{"line":54,"col":11,"byte":1100},"end":{"line":54,"col":35,"byte":1124}},"unknown_member\u001fprovider_key":{"start":{"line":53,"col":10,"byte":1072},"end":{"line":53,"col":27,"byte":1089}},"unknown_member\u001fprovider_name":{"start":{"line":57,"col":11,"byte":1207},"end":{"line":57,"col":46,"byte":1242}},"unknown_member\u001fspecialty":{"start":{"line":59,"col":16,"byte":1300},"end":{"line":59,"col":45,"byte":1329}},"unknown_member\u001fstate":{"start":{"line":62,"col":16,"byte":1428},"end":{"line":62,"col":41,"byte":1453}},"unknown_member\u001futilization":{"start":{"line":66,"col":16,"byte":1600},"end":{"line":66,"col":46,"byte":1630}},"unknown_member\u001fzip_code":{"start":{"line":63,"col":16,"byte":1469},"end":{"line":63,"col":44,"byte":1497}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"city":{"description":"City","documented":true},"gender":{"description":"Provider gender (M or F)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"gender","model":"{{ get_where_subquery(ref('dim_providers')) }}","values":["M","F"]},"node_id":"test.healthcare_analytics.accepted_values_dim_providers_gender__M__F.248d04b44f"}],"documented":true},"latitude":{"description":"Latitude coordinate","documented":true},"longitude":{"description":"Longitude coordinate","documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_organization_id.c650c07852"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_organization_key.d5b2be228b"},{"kind":"relationships","kwargs":{"column_name":"organization_key","field":"organization_key","model":"{{ get_where_subquery(ref('dim_providers')) }}","to":"ref('dim_organizations')"},"node_id":"test.healthcare_analytics.relationships_dim_providers_organization_key__organization_key__ref_dim_organizations_.905f82d6ac"}],"documented":true},"provider_id":{"description":"Natural provider identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"provider_id","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_provider_id.2f5a85b6ff"}],"documented":true},"provider_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"provider_key","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_provider_key.9a2c91be87"},{"kind":"unique","kwargs":{"column_name":"provider_key","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"}],"documented":true},"provider_name":{"description":"Full name of the provider","tests":[{"kind":"not_null","kwargs":{"column_name":"provider_name","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_provider_name.e2289fc3be"}],"documented":true},"specialty":{"description":"Medical specialty","documented":true},"state":{"description":"State (full state name)","documented":true},"utilization":{"description":"Total utilization count","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"utilization","min_value":0,"model":"{{ get_where_subquery(ref('dim_providers')) }}","row_condition":"utilization is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_providers_utilization__0__utilization_is_not_null.175a27ff73"}],"documented":true},"zip_code":{"description":"ZIP code","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__providers"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"providers"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"dim_organizations"}},"column":"organization_key"},"to_col":{"scope":{"intra":{"node_id":"organizations"}},"column":"organization_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_organizations"}},"column":"organization_id"},"to_col":{"scope":{"intra":{"node_id":"organizations"}},"column":"organization_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"provider_id"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"provider_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"organizations"}},"column":"organization_key"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"organization_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"organization_id"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"organization_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"provider_name"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"provider_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"gender"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"gender"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"specialty"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"specialty"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"address"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"address"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"zip_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"zip_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"latitude"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"latitude"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"longitude"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"longitude"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"utilization"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"utilization"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='provider_key'\n    )\n}}\n\nwith providers as (\n    select * from {{ ref('stg_synthea__providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from {{ ref('dim_organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_providers\n    description: >\n      Provider dimension containing healthcare providers (doctors, nurses, etc.).\n      Includes an unknown member row (provider_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 4000\n          config:\n            severity: warn\n    columns:\n      - name: provider_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_id\n        description: Natural provider identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_organizations')\n              field: organization_key\n\n      - name: organization_id\n        description: Natural organization identifier\n        data_tests:\n          - not_null\n\n      - name: provider_name\n        description: Full name of the provider\n        data_tests:\n          - not_null\n\n      - name: gender\n        description: Provider gender (M or F)\n        data_tests:\n          - accepted_values:\n              values: ['M', 'F']\n              config:\n                where: \"provider_key != -1\"\n\n      - name: specialty\n        description: Medical specialty\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State (full state name)\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: latitude\n        description: Latitude coordinate\n\n      - name: longitude\n        description: Longitude coordinate\n\n      - name: utilization\n        description: Total utilization count\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"utilization is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_providers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"]},"evidence":[{"label":"unique_key","value":"provider_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_providers","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('dim_organizations'), ref('stg_synthea__providers')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","dag":{"nodes":[{"id":"valid_encounters","role":"import"},{"id":"conditions","role":"import"},{"id":"medications","role":"import"},{"id":"procedures","role":"import"},{"id":"all_events","role":"transform"},{"id":"patients","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_clinical_events.sql","role":"final"}],"edges":[{"from":"conditions","to":"all_events","edge_type":"union_all"},{"from":"medications","to":"all_events","edge_type":"union_all"},{"from":"procedures","to":"all_events","edge_type":"union_all"},{"from":"all_events","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"encounters","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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unique_key='clinical_event_key'\n    )\n}}\n\nwith valid_encounters as (\n    select encounter_id\n    from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from {{ ref('stg_synthea__conditions') }} as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from {{ ref('stg_synthea__medications') }} as med\n    {{ quarantine_filter() }}\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from {{ ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_clinical_events\n    description: >\n      Unified clinical events fact table combining conditions, medications, and procedures.\n      Each row represents a single clinical event with standardized structure.\n      Links to patient and encounter dimensions.\n    data_tests:\n      # Row count validation - should be sum of conditions + medications + procedures (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 60000\n          config:\n            severity: warn\n    columns:\n      - name: clinical_event_key\n        description: Surrogate key for the clinical event\n        data_tests:\n          - unique\n          - not_null\n\n      - name: event_type\n        description: Type of clinical event (CONDITION, MEDICATION, PROCEDURE)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['CONDITION', 'MEDICATION', 'PROCEDURE']\n\n      - name: event_id\n        description: Natural event identifier (condition_id, medication_id, or procedure_id)\n        data_tests:\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('fct_encounters')\n              field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - not_null\n\n      - name: event_date_key\n        description: Foreign key to dim_date for event start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"event_date_key != -1\"\n\n      - name: event_start_date\n        description: Start date of the clinical event\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: event_end_date\n        description: End date of the clinical event (null if ongoing or one-time)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"event_end_date is not null\"\n              config:\n                severity: warn\n\n      - name: code\n        description: Clinical code (SNOMED-CT or RxNorm)\n        data_tests:\n          - not_null\n\n      - name: code_system\n        description: Code system (SNOMED-CT or RXNORM)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['SNOMED-CT', 'RXNORM']\n\n      - name: description\n        description: Description of the clinical event\n        data_tests:\n          - not_null\n\n      - name: reason_code\n        description: Clinical code for the reason of the event\n\n      - name: reason_description\n        description: Description of the reason for the event\n\n      - name: event_cost\n        description: Cost of the clinical event\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"event_cost is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_condition_cohorts","path":"models/marts/analytics/fct_condition_cohorts.sql","description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"patients","role":"import"},{"id":"dim_conditions","role":"import"},{"id":"condition_windows","role":"transform"},{"id":"with_dimensions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_condition_cohorts.sql","role":"final"}],"edges":[{"from":"conditions_source","to":"condition_windows","edge_type":"from"},{"from":"condition_windows","to":"with_dimensions","edge_type":"from"},{"from":"patients","to":"with_dimensions","edge_type":"left"},{"from":"dim_conditions","to":"with_dimensions","edge_type":"left"},{"from":"with_dimensions","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_windows":"condition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n)","conditions_source":"conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n)","dim_conditions":"dim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)","patients":"patients as (\n    select\n        patient_key\n        , 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condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","node_spans":{"(final 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unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_condition_cohorts\n    description: |\n      Patient-condition cohort analysis for disease management and population health.\n      One row per unique patient-condition combination with timeline and encounter details.\n      Grain: (patient_id, condition_code)\n\n      Use cases:\n      - Disease management program enrollment\n      - Cohort identification for clinical trials\n      - Chronic condition tracking\n      - Care gap analysis\n      - Risk stratification by condition\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: patient_id + condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Disease management programs\"\n        - \"Cohort identification\"\n        - \"Chronic condition tracking\"\n        - \"Care gap analysis\"\n      example_queries:\n        - name: \"Largest condition cohorts\"\n          description: \"Find conditions with the most patients for population health programs\"\n          sql: |\n            select\n                fcc.condition_code\n                , fcc.condition_description\n                , count(distinct fcc.patient_id) as patient_count\n                , sum(fcc.encounter_count) as total_encounters\n                , round(avg(fcc.months_with_condition), 1) as avg_months_with_condition\n                , sum(case when fcc.is_active then 1 else 0 end) as active_patients\n            from main_marts.fct_condition_cohorts fcc\n            group by 1, 2\n            order by patient_count desc\n            limit 15\n\n        - name: \"Chronic condition patients (long duration)\"\n          description: \"Identify patients with long-standing conditions for care management\"\n          sql: |\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , fcc.condition_description\n                , fcc.first_diagnosis_date\n                , fcc.months_with_condition\n                , fcc.encounter_count\n                , fcc.is_active\n            from main_marts.fct_condition_cohorts fcc\n            join main_marts.dim_patients p\n                on fcc.patient_key = p.patient_key\n            where fcc.months_with_condition >= 24\n                and fcc.is_active = true\n            order by fcc.months_with_condition desc\n            limit 50\n\n        - name: \"Patients with multiple conditions\"\n          description: \"Find patients with comorbidities for complex care programs\"\n          sql: |\n            with patient_condition_counts as (\n                select\n                    patient_id\n                    , patient_key\n                    , count(distinct condition_code) as condition_count\n                    , sum(case when is_active then 1 else 0 end) as active_condition_count\n                from main_marts.fct_condition_cohorts\n                group by 1, 2\n            )\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , pcc.condition_count\n                , pcc.active_condition_count\n            from patient_condition_counts pcc\n            join main_marts.dim_patients p\n                on pcc.patient_key = p.patient_key\n            where pcc.condition_count >= 3\n            order by pcc.condition_count desc\n            limit 25\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - patient_id\n              - condition_code\n          config:\n            severity: error\n    columns:\n      - name: condition_cohort_key\n        description: Surrogate key (MD5 hash of patient_id + condition_code)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_patients')\n                field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: condition_key\n        description: Foreign key to dim_conditions\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_conditions')\n                field: condition_key\n\n      - name: condition_code\n        description: ICD/SNOMED condition code\n        data_tests:\n          - not_null\n\n      - name: condition_description\n        description: Full condition name\n\n      - name: first_diagnosis_date\n        description: Date of first diagnosis\n        data_tests:\n          - not_null\n\n      - name: last_diagnosis_date\n        description: Date of most recent diagnosis (null if first occurrence only)\n\n      - name: first_diagnosis_year\n        description: Year of first diagnosis\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: months_with_condition\n        description: Months between first and last diagnosis (0 if single occurrence)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounter_count\n        description: Number of encounters with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: is_active\n        description: True if condition has no end date (currently active)\n        data_tests:\n          - not_null\n          - accepted_values:\n              arguments:\n                values: [true, false]\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from 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select)"}},"column":"coverage_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_cost_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_cost_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"procedure_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"procedure_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"cost_per_procedure"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"cost_per_procedure"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"_loaded_at"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"_loaded_at"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_cost_analysis\n    description: |\n      Detailed cost breakdown for financial analysis and revenue cycle management.\n      One row per encounter with cost allocation details across payer and patient.\n      Grain: encounter_id\n\n      Use cases:\n      - Financial performance reporting\n      - Payer mix analysis\n      - Patient out-of-pocket tracking\n      - Cost driver identification\n      - Revenue cycle optimization\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: encounter_id\n      owner: healthcare_analytics\n      uses:\n        - \"Financial reporting\"\n        - \"Payer mix analysis\"\n        - \"Cost allocation tracking\"\n        - \"Revenue cycle management\"\n      example_queries:\n        - name: \"Cost breakdown by payer\"\n          description: \"Analyze cost distribution across different insurance payers\"\n          sql: |\n            select\n                py.payer_name\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(sum(fca.payer_coverage), 2) as total_payer_coverage\n                , round(sum(fca.patient_responsibility), 2) as total_patient_responsibility\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            join main_marts.dim_payers py\n                on fca.payer_key = py.payer_key\n            group by py.payer_name\n            order by total_cost desc\n\n        - name: \"Monthly cost trends\"\n          description: \"Track cost trends over time for financial forecasting\"\n          sql: |\n            select\n                strftime(fca.encounter_date, '%Y-%m') as year_month\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(avg(fca.total_cost), 2) as avg_cost_per_encounter\n                , round(sum(fca.payer_coverage), 2) as total_payer_coverage\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            where fca.year_actual >= 2023\n            group by 1\n            order by 1 desc\n\n        - name: \"Cost by encounter type\"\n          description: \"Compare costs across different encounter classes\"\n          sql: |\n            select\n                fca.encounter_class\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(avg(fca.total_cost), 2) as avg_cost\n                , round(avg(fca.procedure_count), 2) as avg_procedures\n                , round(avg(fca.cost_per_procedure), 2) as avg_cost_per_procedure\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            group by fca.encounter_class\n            order by total_cost desc\n\n        - name: \"High-cost encounters\"\n          description: \"Identify individual high-cost encounters for review\"\n          sql: |\n            select\n                fca.encounter_id\n                , fca.encounter_date\n                , fca.encounter_class\n                , p.first_name || ' ' || p.last_name as patient_name\n                , py.payer_name\n                , fca.total_cost\n                , fca.payer_coverage\n                , fca.patient_responsibility\n                , fca.procedure_count\n            from main_marts.fct_cost_analysis fca\n            join main_marts.dim_patients p\n                on fca.patient_key = p.patient_key\n            join main_marts.dim_payers py\n                on fca.payer_key = py.payer_key\n            order by fca.total_cost desc\n            limit 25\n\n    columns:\n      - name: cost_analysis_key\n        description: Surrogate key (MD5 hash of encounter_id)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('fct_encounters')\n                field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n\n      - name: patient_id\n        description: Natural patient identifier\n\n      - name: payer_key\n        description: Foreign key to dim_payers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_payers')\n                field: payer_key\n\n      - name: payer_id\n        description: Natural payer identifier\n\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_providers')\n                field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n\n      - name: encounter_date_key\n        description: Foreign key to dim_date\n\n      - name: encounter_date\n        description: Date of the encounter\n\n      - name: year_actual\n        description: Calendar year of encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter (ambulatory, wellness, etc.)\n\n      - name: total_cost\n        description: Total claim cost\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: patient_responsibility\n        description: Amount owed by patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: coverage_pct\n        description: Percentage covered by insurance (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: patient_cost_pct\n        description: Percentage owed by patient (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: procedure_count\n        description: Number of procedures in the encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: cost_per_procedure\n        description: Average cost per procedure (null if no procedures)\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_cost_analysis_cost_analysis_key.2aecf62159"]},"evidence":[{"label":"unique_key","value":"cost_analysis_key"}],"pin_node":"(final select)"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:dim_date]","verdict":{"status":"unknown"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN dim_date ON encounters.encounter_start_date_key = dim_date.date_key"},{"label":"unattributable join","value":"encounters_enriched — join key or side binding not statically recoverable"}],"pin_node":"encounters_enriched"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:procedures]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN procedures ON encounters.encounter_id = procedures.encounter_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"encounters_enriched","sketches":["- input: ref('fct_encounters')\n  rows:\n    - {encounter_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__procedures')\n  rows:\n    - {encounter_id: 1}\n# expect: the no-match row with NULL stg_synthea__procedures columns (or the intended fallback)"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"patients","role":"import"},{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"payers","role":"import"},{"id":"dim_date","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"providers","to":"final","edge_type":"left"},{"from":"organizations","to":"final","edge_type":"left"},{"from":"payers","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final select)\u001f*":{"start":{"line":109,"col":8,"byte":2736},"end":{"line":109,"col":9,"byte":2737}},"dim_date\u001fdate_actual":{"start":{"line":45,"col":11,"byte":721},"end":{"line":45,"col":22,"byte":732}},"dim_date\u001fdate_key":{"start":{"line":44,"col":9,"byte":702},"end":{"line":44,"col":17,"byte":710}},"encounters\u001f*":{"start":{"line":4,"col":12,"byte":34},"end":{"line":4,"col":13,"byte":35}},"final\u001f_loaded_at":{"start":{"line":98,"col":11,"byte":2250},"end":{"line":98,"col":42,"byte":2281}},"final\u001fbase_encounter_cost":{"start":{"line":86,"col":11,"byte":1836},"end":{"line":86,"col":32,"byte":1857}},"final\u001fduration_minutes":{"start":{"line":83,"col":11,"byte":1726},"end":{"line":83,"col":92,"byte":1807}},"final\u001fencounter_class":{"start":{"line":72,"col":11,"byte":1470},"end":{"line":72,"col":28,"byte":1487}},"final\u001fencounter_code":{"start":{"line":73,"col":11,"byte":1498},"end":{"line":73,"col":27,"byte":1514}},"final\u001fencounter_description":{"start":{"line":74,"col":11,"byte":1525},"end":{"line":74,"col":34,"byte":1548}},"final\u001fencounter_end_at":{"start":{"line":80,"col":11,"byte":1668},"end":{"line":80,"col":29,"byte":1686}},"final\u001fencounter_end_date_key":{"start":{"line":69,"col":11,"byte":1385},"end":{"line":69,"col":62,"byte":1436}},"final\u001fencounter_id":{"start":{"line":55,"col":11,"byte":929},"end":{"line":55,"col":25,"byte":943}},"final\u001fencounter_key":{"start":{"line":52,"col":9,"byte":834},"end":{"line":52,"col":69,"byte":894}},"final\u001fencounter_start_at":{"start":{"line":79,"col":11,"byte":1637},"end":{"line":79,"col":31,"byte":1657}},"final\u001fencounter_start_date_key":{"start":{"line":68,"col":11,"byte":1321},"end":{"line":68,"col":64,"byte":1374}},"final\u001forganization_id":{"start":{"line":63,"col":11,"byte":1200},"end":{"line":63,"col":28,"byte":1217}},"final\u001forganization_key":{"start":{"line":62,"col":11,"byte":1137},"end":{"line":62,"col":63,"byte":1189}},"final\u001fpatient_age_at_encounter":{"start":{"line":92,"col":11,"byte":2047},"end":{"line":95,"col":40,"byte":2218}},"final\u001fpatient_id":{"start":{"line":59,"col":11,"byte":1034},"end":{"line":59,"col":23,"byte":1046}},"final\u001fpatient_key":{"start":{"line":58,"col":11,"byte":981},"end":{"line":58,"col":53,"byte":1023}},"final\u001fpatient_responsibility":{"start":{"line":89,"col":11,"byte":1924},"end":{"line":89,"col":87,"byte":2000}},"final\u001fpayer_coverage":{"start":{"line":88,"col":11,"byte":1897},"end":{"line":88,"col":27,"byte":1913}},"final\u001fpayer_id":{"start":{"line":65,"col":11,"byte":1278},"end":{"line":65,"col":21,"byte":1288}},"final\u001fpayer_key":{"start":{"line":64,"col":11,"byte":1228},"end":{"line":64,"col":50,"byte":1267}},"final\u001fprovider_id":{"start":{"line":61,"col":11,"byte":1113},"end":{"line":61,"col":24,"byte":1126}},"final\u001fprovider_key":{"start":{"line":60,"col":11,"byte":1057},"end":{"line":60,"col":56,"byte":1102}},"final\u001freason_code":{"start":{"line":75,"col":11,"byte":1559},"end":{"line":75,"col":24,"byte":1572}},"final\u001freason_description":{"start":{"line":76,"col":11,"byte":1583},"end":{"line":76,"col":31,"byte":1603}},"final\u001ftotal_claim_cost":{"start":{"line":87,"col":11,"byte":1868},"end":{"line":87,"col":29,"byte":1886}},"organizations\u001forganization_id":{"start":{"line":31,"col":11,"byte":490},"end":{"line":31,"col":26,"byte":505}},"organizations\u001forganization_key":{"start":{"line":30,"col":9,"byte":463},"end":{"line":30,"col":25,"byte":479}},"patients\u001fbirth_date":{"start":{"line":16,"col":11,"byte":216},"end":{"line":16,"col":21,"byte":226}},"patients\u001fpatient_id":{"start":{"line":15,"col":11,"byte":195},"end":{"line":15,"col":21,"byte":205}},"patients\u001fpatient_key":{"start":{"line":14,"col":9,"byte":173},"end":{"line":14,"col":20,"byte":184}},"payers\u001fpayer_id":{"start":{"line":38,"col":11,"byte":612},"end":{"line":38,"col":19,"byte":620}},"payers\u001fpayer_key":{"start":{"line":37,"col":9,"byte":592},"end":{"line":37,"col":18,"byte":601}},"providers\u001fprovider_id":{"start":{"line":24,"col":11,"byte":362},"end":{"line":24,"col":22,"byte":373}},"providers\u001fprovider_key":{"start":{"line":23,"col":9,"byte":339},"end":{"line":23,"col":21,"byte":351}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"base_encounter_cost":{"description":"Base cost of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"base_encounter_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"base_encounter_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_base_encounter_cost__0__base_encounter_cost_is_not_null.a096f214be"}],"documented":true},"duration_minutes":{"description":"Duration of encounter in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_duration_minutes__0__duration_minutes_is_not_null.ce51123f28"}],"documented":true},"encounter_class":{"description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.59f26de6a5"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_class.9419876296"}],"documented":true},"encounter_code":{"description":"SNOMED CT code for the encounter type","documented":true},"encounter_description":{"description":"Description of the encounter type","documented":true},"encounter_end_at":{"description":"End timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_end_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"encounter_end_at is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_end_at__current_timestamp___1900_01_01_timestamp__encounter_end_at_is_not_null.393dec12f4"}],"documented":true},"encounter_end_date_key":{"description":"Foreign key to dim_date for encounter end","documented":true},"encounter_id":{"description":"Natural encounter identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_id.f7111b4eea"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_id.ce88bae928"}],"documented":true},"encounter_key":{"description":"Surrogate key for the encounter","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_key.4fa2f03d5e"},{"kind":"unique","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"}],"documented":true},"encounter_start_at":{"description":"Start timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_start_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_start_at__current_timestamp___1900_01_01_timestamp.f32b839e27"},{"kind":"not_null","kwargs":{"column_name":"encounter_start_at","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_at.c47cc214ac"}],"documented":true},"encounter_start_date_key":{"description":"Foreign key to dim_date for encounter start","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_start_date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_date_key.73ce57ba43"},{"kind":"relationships","kwargs":{"column_name":"encounter_start_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_encounter_start_date_key__date_key__ref_dim_date_.4f1d88383c"}],"documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_id.acfb633ecb"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_key.815fa1363c"},{"kind":"relationships","kwargs":{"column_name":"organization_key","field":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_organizations')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_organization_key__organization_key__ref_dim_organizations_.5feb1b3ad6"}],"documented":true},"patient_age_at_encounter":{"description":"Patient's age at the time of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_age_at_encounter","max_value":150,"min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_age_at_encounter is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_age_at_encounter__150__0__patient_age_at_encounter_is_not_null.bbd237256e"}],"documented":true},"patient_id":{"description":"Natural patient identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_id.9e325e7ac6"}],"documented":true},"patient_key":{"description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_key.e5abbfbe29"},{"kind":"relationships","kwargs":{"column_name":"patient_key","field":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_patients')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_patient_key__patient_key__ref_dim_patients_.ad1c292e28"}],"documented":true},"patient_responsibility":{"description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_responsibility","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_responsibility is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_responsibility__0__patient_responsibility_is_not_null.7a61f00fd0"}],"documented":true},"payer_coverage":{"description":"Amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"payer_coverage","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"payer_coverage is not 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from {{ ref('dim_payers') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_encounters\n    description: >\n      Encounter fact table containing healthcare visits/encounters.\n      Links to patient, provider, organization, and payer dimensions.\n      Includes date keys, costs, and derived duration.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 40000\n          config:\n            severity: warn\n    columns:\n      - name: encounter_key\n        description: Surrogate key for the encounter\n        data_tests:\n          - unique\n          - not_null\n\n      - name: encounter_id\n        description: Natural encounter identifier (UUID)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: provider_key\n        description: Foreign key to dim_providers (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_providers')\n              field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_organizations')\n              field: organization_key\n\n      - name: organization_id\n        description: Natural organization identifier\n        data_tests:\n          - not_null\n\n      - name: payer_key\n        description: Foreign key to dim_payers (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_payers')\n              field: payer_key\n\n      - name: payer_id\n        description: Natural payer identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_start_date_key\n        description: Foreign key to dim_date for encounter start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"encounter_start_date_key != -1\"\n\n      - name: encounter_end_date_key\n        description: Foreign key to dim_date for encounter end\n\n      - name: encounter_class\n        description: Type of encounter (ambulatory, wellness, emergency, etc.)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_code\n        description: SNOMED CT code for the encounter type\n\n      - name: encounter_description\n        description: Description of the encounter type\n\n      - name: reason_code\n        description: SNOMED CT code for the reason of visit\n\n      - name: reason_description\n        description: Description of the reason for visit\n\n      - name: encounter_start_at\n        description: Start timestamp of the encounter\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::timestamp\"\n              max_value: \"current_timestamp\"\n              config:\n                severity: warn\n\n      - name: encounter_end_at\n        description: End timestamp of the encounter\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::timestamp\"\n              max_value: \"current_timestamp\"\n              row_condition: \"encounter_end_at is not null\"\n              config:\n                severity: warn\n\n      - name: duration_minutes\n        description: Duration of encounter in minutes\n        data_tests:\n          # Duration must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"duration_minutes is not null\"\n              config:\n                severity: warn\n\n      - name: base_encounter_cost\n        description: Base cost of the encounter\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"base_encounter_cost is not null\"\n              config:\n                severity: warn\n\n      - name: total_claim_cost\n        description: Total claim cost\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_claim_cost is not null\"\n              config:\n                severity: warn\n\n      - name: payer_coverage\n        description: Amount covered by payer\n        data_tests:\n          # Coverage must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"payer_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: patient_responsibility\n        description: Amount patient is responsible for (total_claim_cost - payer_coverage)\n        data_tests:\n          # Patient responsibility must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"patient_responsibility is not null\"\n              config:\n                severity: warn\n\n      - name: patient_age_at_encounter\n        description: Patient's age at the time of the encounter\n        data_tests:\n          # Age must be reasonable 0-150 years (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 150\n              row_condition: \"patient_age_at_encounter is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was 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name: fct_encounters_incremental\n    description: >\n      Incremental encounter accumulator. On incremental runs it only merges\n      encounters newer than the loaded start_time high-water mark.\n    columns:\n      - name: encounter_id\n        description: Natural key of the encounter (unique per visit)\n        data_tests:\n          - unique\n          - not_null\n      - name: patient_id\n        description: Patient the encounter belongs to\n      - name: encounter_type\n        description: Visit class (wellness, emergency, ...)\n      - name: start_time\n        description: Encounter start timestamp - the incremental high-water mark\n      - name: total_cost\n        description: Total claim cost for the encounter"},"tests":[{"id":"unit_test.healthcare_analytics.fct_encounters_incremental.test_fct_encounters_incremental_appends_new_encounters","name":"test_fct_encounters_incremental_appends_new_encounters","target_model":"fct_encounters_incremental","changed":false,"description":"fct_encounters_incremental appends encounters newer than the latest loaded start_time. 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With is_incremental() true, only the new\n      encounter (e2) is merged; the prior row (e1, supplied via `this`) is\n      untouched, so Expected is the single inserted row — the materialization\n      delta, not the resulting table.\n    model: fct_encounters_incremental\n    overrides:\n      macros:\n        is_incremental: true\n    given:\n      - input: this\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n      - input: ref('stg_synthea__encounters')\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n          - encounter_id: 'e2'\n            patient_id: 'p2'\n            encounter_type: 'emergency'\n            start_time: '2025-02-01 14:30:00'\n            total_cost: 500.0\n    expect:\n      format: dict\n      rows:\n        - encounter_id: 'e2'\n          patient_id: 'p2'\n          encounter_type: 'emergency'\n          start_time: '2025-02-01 14:30:00'\n          total_cost: 500.0","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - 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the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_encounters_monthly","path":"models/marts/core/fct_encounters_monthly.sql","description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters_monthly.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"encounters_with_date","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":53,"col":1,"byte":1374},"end":{"line":53,"col":20,"byte":1393}},"dim_date":{"start":{"line":16,"col":1,"byte":301},"end":{"line":22,"col":2,"byte":431}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":298}},"encounters_with_date":{"start":{"line":24,"col":1,"byte":434},"end":{"line":34,"col":2,"byte":790}},"final":{"start":{"line":36,"col":1,"byte":793},"end":{"line":51,"col":2,"byte":1372}}},"column_spans":{"(final select)\u001f*":{"start":{"line":53,"col":8,"byte":1381},"end":{"line":53,"col":9,"byte":1382}},"dim_date\u001fdate_key":{"start":{"line":18,"col":9,"byte":334},"end":{"line":18,"col":17,"byte":342}},"dim_date\u001fmonth_actual":{"start":{"line":20,"col":11,"byte":375},"end":{"line":20,"col":23,"byte":387}},"dim_date\u001fyear_actual":{"start":{"line":19,"col":11,"byte":353},"end":{"line":19,"col":22,"byte":364}},"encounters\u001fduration_minutes":{"start":{"line":12,"col":11,"byte":232},"end":{"line":12,"col":27,"byte":248}},"encounters\u001fencounter_class":{"start":{"line":8,"col":11,"byte":121},"end":{"line":8,"col":26,"byte":136}},"encounters\u001fencounter_id":{"start":{"line":5,"col":9,"byte":42},"end":{"line":5,"col":21,"byte":54}},"encounters\u001fencounter_start_date_key":{"start":{"line":7,"col":11,"byte":86},"end":{"line":7,"col":35,"byte":110}},"encounters\u001fpatient_id":{"start":{"line":6,"col":11,"byte":65},"end":{"line":6,"col":21,"byte":75}},"encounters\u001fpatient_responsibility":{"start":{"line":11,"col":11,"byte":199},"end":{"line":11,"col":33,"byte":221}},"encounters\u001fpayer_coverage":{"start":{"line":10,"col":11,"byte":174},"end":{"line":10,"col":25,"byte":188}},"encounters\u001ftotal_claim_cost":{"start":{"line":9,"col":11,"byte":147},"end":{"line":9,"col":27,"byte":163}},"encounters_with_date\u001f*":{"start":{"line":26,"col":9,"byte":479},"end":{"line":26,"col":12,"byte":482}},"encounters_with_date\u001fmonth_actual":{"start":{"line":28,"col":11,"byte":517},"end":{"line":28,"col":25,"byte":531}},"encounters_with_date\u001fyear_actual":{"start":{"line":27,"col":11,"byte":493},"end":{"line":27,"col":24,"byte":506}},"encounters_with_date\u001fyear_month":{"start":{"line":29,"col":16,"byte":547},"end":{"line":30,"col":72,"byte":654}},"final\u001f_loaded_at":{"start":{"line":48,"col":11,"byte":1241},"end":{"line":48,"col":42,"byte":1272}},"final\u001favg_duration_minutes":{"start":{"line":47,"col":11,"byte":1185},"end":{"line":47,"col":56,"byte":1230}},"final\u001fencounter_class":{"start":{"line":41,"col":11,"byte":889},"end":{"line":41,"col":26,"byte":904}},"final\u001fencounter_count":{"start":{"line":42,"col":11,"byte":915},"end":{"line":42,"col":38,"byte":942}},"final\u001fmonth_actual":{"start":{"line":40,"col":11,"byte":866},"end":{"line":40,"col":23,"byte":878}},"final\u001ftotal_claim_cost":{"start":{"line":44,"col":11,"byte":1009},"end":{"line":44,"col":52,"byte":1050}},"final\u001ftotal_patient_responsibility":{"start":{"line":46,"col":11,"byte":1115},"end":{"line":46,"col":70,"byte":1174}},"final\u001ftotal_payer_coverage":{"start":{"line":45,"col":11,"byte":1061},"end":{"line":45,"col":54,"byte":1104}},"final\u001funique_patients":{"start":{"line":43,"col":11,"byte":953},"end":{"line":43,"col":56,"byte":998}},"final\u001fyear_actual":{"start":{"line":39,"col":11,"byte":844},"end":{"line":39,"col":22,"byte":855}},"final\u001fyear_month":{"start":{"line":38,"col":9,"byte":823},"end":{"line":38,"col":19,"byte":833}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"avg_duration_minutes":{"description":"Average encounter duration in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"avg_duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"avg_duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_avg_duration_minutes__0__avg_duration_minutes_is_not_null.0cea70c94f"}],"documented":true},"encounter_class":{"description":"Type of encounter","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_monthly_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.274b5b6532"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_encounter_class.d503677b5a"}],"documented":true},"encounter_count":{"description":"Number of encounters in the month","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_encounter_count__1.03132101e0"},{"kind":"not_null","kwargs":{"column_name":"encounter_count","model":"{{ 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data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Month number (1-12)\n        data_tests:\n          - not_null\n          # Month must be 1-12 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 12\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_count\n        description: Number of encounters in the month\n        data_tests:\n          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}}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_yearly_year_actual.f5bf321954"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"encounter_start_date_key"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_start_date_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"encounter_class"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_class"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"total_claim_cost"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"total_claim_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"payer_coverage"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"payer_coverage"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"patient_responsibility"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"patient_responsibility"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"duration_minutes"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"duration_minutes"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"date_key"},"to_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"date_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_start_date_key"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"encounter_start_date_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_class"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"encounter_class"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"total_claim_cost"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"total_claim_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"payer_coverage"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"payer_coverage"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"patient_responsibility"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"patient_responsibility"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"duration_minutes"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"duration_minutes"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"encounter_class"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"encounter_class"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final 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   where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , cast(count(*) as decimal(18, 2)) / nullif(count(distinct patient_id), 0) as encounters_per_patient\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_actual, encounter_class\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_encounters_yearly\n    description: >\n      Yearly aggregated encounter statistics by encounter class.\n      Provides counts, costs, patient metrics, and encounters per patient ratio.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 10\n          config:\n            severity: warn\n    columns:\n      - name: year_actual\n        description: Year number\n        data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_count\n        description: Number of encounters in the year\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: unique_patients\n        description: Number of unique patients with encounters\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: total_claim_cost\n        description: Sum of total claim costs\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_claim_cost is not null\"\n              config:\n                severity: warn\n\n      - name: total_payer_coverage\n        description: Sum of payer coverage amounts\n        data_tests:\n          # Coverage must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_payer_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: total_patient_responsibility\n        description: Sum of patient responsibility amounts\n        data_tests:\n          # Patient responsibility must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_patient_responsibility is not null\"\n              config:\n                severity: warn\n\n      - name: avg_duration_minutes\n        description: Average encounter duration in minutes\n        data_tests:\n          # Duration must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"avg_duration_minutes is not null\"\n              config:\n                severity: warn\n\n      - name: encounters_per_patient\n        description: Average number of encounters per patient\n        data_tests:\n          # Ratio must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"encounters_per_patient is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_yearly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_actual, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_patient_summary","path":"models/marts/analytics/fct_patient_summary.sql","description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"conditions","role":"import"},{"id":"encounters_with_year","role":"transform"},{"id":"conditions_per_patient_year","role":"transform"},{"id":"patient_year_agg","role":"transform"},{"id":"with_conditions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_patient_summary.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_year","edge_type":"from"},{"from":"dim_date","to":"encounters_with_year","edge_type":"inner"},{"from":"procedures","to":"encounters_with_year","edge_type":"left"},{"from":"conditions","to":"conditions_per_patient_year","edge_type":"from"},{"from":"encounters_with_year","to":"patient_year_agg","edge_type":"from"},{"from":"patient_year_agg","to":"with_conditions","edge_type":"from"},{"from":"conditions_per_patient_year","to":"with_conditions","edge_type":"left"},{"from":"with_conditions","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","conditions":"conditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","conditions_per_patient_year":"conditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where patient_key != -1\n)","encounters_with_year":"encounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)","patient_year_agg":"patient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","with_conditions":"with_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where patient_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\n-- join encounters to date dimension to get year\nencounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n),\n\n-- join condition counts\nwith_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":141,"col":1,"byte":3654},"end":{"line":141,"col":20,"byte":3673}},"conditions":{"start":{"line":43,"col":1,"byte":836},"end":{"line":49,"col":2,"byte":1035}},"conditions_per_patient_year":{"start":{"line":64,"col":1,"byte":1482},"end":{"line":71,"col":2,"byte":1706}},"dim_date":{"start":{"line":28,"col":1,"byte":551},"end":{"line":33,"col":2,"byte":658}},"encounters":{"start":{"line":5,"col":6,"byte":9},"end":{"line":26,"col":2,"byte":548}},"encounters_with_year":{"start":{"line":52,"col":1,"byte":1087},"end":{"line":61,"col":2,"byte":1429}},"final":{"start":{"line":102,"col":1,"byte":2695},"end":{"line":139,"col":2,"byte":3652}},"patient_year_agg":{"start":{"line":74,"col":1,"byte":1742},"end":{"line":89,"col":2,"byte":2392}},"procedures":{"start":{"line":35,"col":1,"byte":661},"end":{"line":41,"col":2,"byte":833}},"with_conditions":{"start":{"line":92,"col":1,"byte":2420},"end":{"line":100,"col":2,"byte":2692}}},"column_spans":{"(final select)\u001f*":{"start":{"line":141,"col":8,"byte":3661},"end":{"line":141,"col":9,"byte":3662}},"conditions\u001fcondition_code":{"start":{"line":46,"col":11,"byte":892},"end":{"line":46,"col":25,"byte":906}},"conditions\u001fcondition_year":{"start":{"line":47,"col":29,"byte":935},"end":{"line":47,"col":68,"byte":974}},"conditions\u001fpatient_id":{"start":{"line":45,"col":9,"byte":871},"end":{"line":45,"col":19,"byte":881}},"conditions_per_patient_year\u001fcondition_count":{"start":{"line":68,"col":11,"byte":1595},"end":{"line":68,"col":60,"byte":1644}},"conditions_per_patient_year\u001fpatient_id":{"start":{"line":66,"col":9,"byte":1534},"end":{"line":66,"col":19,"byte":1544}},"conditions_per_patient_year\u001fyear_actual":{"start":{"line":67,"col":11,"byte":1555},"end":{"line":67,"col":40,"byte":1584}},"dim_date\u001fdate_key":{"start":{"line":30,"col":9,"byte":584},"end":{"line":30,"col":17,"byte":592}},"dim_date\u001fyear_actual":{"start":{"line":31,"col":11,"byte":603},"end":{"line":31,"col":22,"byte":614}},"encounters\u001fencounter_class":{"start":{"line":18,"col":11,"byte":306},"end":{"line":18,"col":26,"byte":321}},"encounters\u001fencounter_id":{"start":{"line":8,"col":11,"byte":68},"end":{"line":8,"col":23,"byte":80}},"encounters\u001fencounter_key":{"start":{"line":7,"col":9,"byte":44},"end":{"line":7,"col":22,"byte":57}},"encounters\u001fencounter_start_at":{"start":{"line":23,"col":11,"byte":452},"end":{"line":23,"col":29,"byte":470}},"encounters\u001fencounter_start_date_key":{"start":{"line":17,"col":11,"byte":271},"end":{"line":17,"col":35,"byte":295}},"encounters\u001forganization_id":{"start":{"line":14,"col":11,"byte":206},"end":{"line":14,"col":26,"byte":221}},"encounters\u001forganization_key":{"start":{"line":13,"col":11,"byte":179},"end":{"line":13,"col":27,"byte":195}},"encounters\u001fpatient_age_at_encounter":{"start":{"line":22,"col":11,"byte":417},"end":{"line":22,"col":35,"byte":441}},"encounters\u001fpatient_id":{"start":{"line":10,"col":11,"byte":113},"end":{"line":10,"col":21,"byte":123}},"encounters\u001fpatient_key":{"start":{"line":9,"col":11,"byte":91},"end":{"line":9,"col":22,"byte":102}},"encounters\u001fpatient_responsibility":{"start":{"line":21,"col":11,"byte":384},"end":{"line":21,"col":33,"byte":406}},"encounters\u001fpayer_coverage":{"start":{"line":20,"col":11,"byte":359},"end":{"line":20,"col":25,"byte":373}},"encounters\u001fpayer_id":{"start":{"line":16,"col":11,"byte":252},"end":{"line":16,"col":19,"byte":260}},"encounters\u001fpayer_key":{"start":{"line":15,"col":11,"byte":232},"end":{"line":15,"col":20,"byte":241}},"encounters\u001fprovider_id":{"start":{"line":12,"col":11,"byte":157},"end":{"line":12,"col":22,"byte":168}},"encounters\u001fprovider_key":{"start":{"line":11,"col":11,"byte":134},"end":{"line":11,"col":23,"byte":146}},"encounters\u001ftotal_claim_cost":{"start":{"line":19,"col":11,"byte":332},"end":{"line":19,"col":27,"byte":348}},"encounters_with_year\u001f*":{"start":{"line":54,"col":9,"byte":1132},"end":{"line":54,"col":12,"byte":1135}},"encounters_with_year\u001fprocedure_count":{"start":{"line":56,"col":11,"byte":1170},"end":{"line":56,"col":61,"byte":1220}},"encounters_with_year\u001fyear_actual":{"start":{"line":55,"col":11,"byte":1146},"end":{"line":55,"col":24,"byte":1159}},"final\u001f_loaded_at":{"start":{"line":136,"col":11,"byte":3593},"end":{"line":136,"col":42,"byte":3624}},"final\u001fcondition_count":{"start":{"line":120,"col":11,"byte":3113},"end":{"line":120,"col":26,"byte":3128}},"final\u001fencounter_count":{"start":{"line":118,"col":11,"byte":3061},"end":{"line":118,"col":26,"byte":3076}},"final\u001fpatient_age_at_year_end":{"start":{"line":115,"col":11,"byte":2998},"end":{"line":115,"col":34,"byte":3021}},"final\u001fpatient_id":{"start":{"line":109,"col":11,"byte":2904},"end":{"line":109,"col":21,"byte":2914}},"final\u001fpatient_key":{"start":{"line":108,"col":11,"byte":2882},"end":{"line":108,"col":22,"byte":2893}},"final\u001fpatient_responsibility":{"start":{"line":127,"col":11,"byte":3329},"end":{"line":127,"col":72,"byte":3390}},"final\u001fpatient_summary_key":{"start":{"line":105,"col":9,"byte":2750},"end":{"line":105,"col":103,"byte":2844}},"final\u001fpayer_coverage":{"start":{"line":126,"col":11,"byte":3273},"end":{"line":126,"col":56,"byte":3318}},"final\u001fpayer_coverage_pct":{"start":{"line":130,"col":11,"byte":3430},"end":{"line":133,"col":34,"byte":3561}},"final\u001fprocedure_count":{"start":{"line":119,"col":11,"byte":3087},"end":{"line":119,"col":26,"byte":3102}},"final\u001ftotal_cost":{"start":{"line":125,"col":11,"byte":3225},"end":{"line":125,"col":48,"byte":3262}},"final\u001funique_organizations":{"start":{"line":122,"col":11,"byte":3166},"end":{"line":122,"col":31,"byte":3186}},"final\u001funique_providers":{"start":{"line":121,"col":11,"byte":3139},"end":{"line":121,"col":27,"byte":3155}},"final\u001fyear_actual":{"start":{"line":112,"col":11,"byte":2948},"end":{"line":112,"col":22,"b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when record was loaded","documented":true},"condition_count":{"description":"Number of distinct conditions diagnosed in the year","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"condition_count","min_value":0,"model":"{{ get_where_subquery(ref('fct_patient_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_patient_summary_condition_count__0.0ecfa64115"}],"documented":true},"encounter_count":{"description":"Number of encounters in the year","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","max_value":10000,"min_value":0,"model":"{{ get_where_subquery(ref('fct_patient_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_patient_summary_encounter_count__10000__0.1c9e5b573f"},{"kind":"not_null","kwargs":{"column_name":"encounter_count","model":"{{ get_where_subquery(ref('fct_patient_summary')) 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}}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_patient_summary_patient_responsibility__0.f50c9a0b80"}],"documented":true},"patient_summary_key":{"description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_summary_key","model":"{{ get_where_subquery(ref('fct_patient_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_patient_summary_patient_summary_key.d9b45bd20e"},{"kind":"unique","kwargs":{"column_name":"patient_summary_key","model":"{{ get_where_subquery(ref('fct_patient_summary')) }}"},"node_id":"test.healthcare_analytics.unique_fct_patient_summary_patient_summary_key.e532e1ff45"}],"documented":true},"payer_coverage":{"description":"Amount covered by insurance","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"payer_coverage","min_value":0,"model":"{{ get_where_subquery(ref('fct_patient_summary')) 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dimensions for foreign key lookups.\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n    where patient_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from {{ ref('stg_synthea__conditions') }}\n),\n\n-- join encounters to date dimension to get year\nencounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n),\n\n-- join condition counts\nwith_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_patient_summary\n    description: |\n      Annual patient-level aggregations for outcomes tracking and population health analysis.\n      One row per patient per calendar year with comprehensive utilization and cost metrics.\n      Grain: (patient_id, year_actual)\n\n      Use cases:\n      - Patient outcomes tracking over time\n      - Annual cost summaries and trends\n      - Cohort analysis and patient stratification\n      - Health trajectory visualization\n      - High-cost patient identification\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: patient_id + year_actual\n      owner: healthcare_analytics\n      uses:\n        - \"Population health reporting\"\n        - \"Cost and utilization analysis\"\n        - \"Patient stratification\"\n        - \"Year-over-year trending\"\n      example_queries:\n        - name: \"Top 10 costliest patients by year\"\n          description: \"Identify high-cost patients for care management programs\"\n          sql: |\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , fps.year_actual\n                , fps.total_cost\n                , fps.encounter_count\n                , fps.condition_count\n                , fps.payer_coverage_pct\n            from main_marts.fct_patient_summary fps\n            join main_marts.dim_patients p\n                on fps.patient_key = p.patient_key\n            where fps.year_actual = 2024\n            order by fps.total_cost desc\n            limit 10\n\n        - name: \"Patient count and cost by age group\"\n          description: \"Analyze patient population distribution and costs by age cohort\"\n          sql: |\n            select\n                case\n                    when fps.patient_age_at_year_end < 18 then '0-17'\n                    when fps.patient_age_at_year_end < 35 then '18-34'\n                    when fps.patient_age_at_year_end < 50 then '35-49'\n                    when fps.patient_age_at_year_end < 65 then '50-64'\n                    else '65+'\n                end as age_group\n                , count(distinct fps.patient_id) as patient_count\n                , round(avg(fps.total_cost), 2) as avg_annual_cost\n                , round(sum(fps.total_cost), 2) as total_cost\n                , round(avg(fps.encounter_count), 2) as avg_encounters\n            from main_marts.fct_patient_summary fps\n            where fps.year_actual = 2024\n            group by 1\n            order by 1\n\n        - name: \"Cost and utilization trends over time\"\n          description: \"Year-over-year comparison of costs and utilization metrics\"\n          sql: |\n            select\n                fps.year_actual\n                , count(distinct fps.patient_id) as patient_count\n                , round(avg(fps.encounter_count), 2) as avg_encounters_per_patient\n                , round(avg(fps.condition_count), 2) as avg_conditions_per_patient\n                , round(avg(fps.total_cost), 2) as avg_annual_cost\n                , round(sum(fps.total_cost), 2) as total_population_cost\n                , round(avg(fps.payer_coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_patient_summary fps\n            group by fps.year_actual\n            order by fps.year_actual desc\n\n        - name: \"High utilization patients\"\n          description: \"Find patients with above-average encounters and conditions\"\n          sql: |\n            with averages as (\n                select\n                    avg(encounter_count) as avg_encounters\n                    , avg(condition_count) as avg_conditions\n                from main_marts.fct_patient_summary\n                where year_actual = 2024\n            )\n            select\n                fps.patient_id\n                , fps.encounter_count\n                , fps.condition_count\n                , fps.total_cost\n                , fps.unique_providers\n            from main_marts.fct_patient_summary fps\n            cross join averages a\n            where fps.year_actual = 2024\n                and fps.encounter_count > a.avg_encounters * 2\n                and fps.condition_count > a.avg_conditions\n            order by fps.total_cost desc\n            limit 25\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - patient_id\n              - year_actual\n          config:\n            severity: error\n    columns:\n      - name: patient_summary_key\n        description: Surrogate key (MD5 hash of patient_id + year_actual)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_patients')\n                field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: Calendar year of the summary\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: patient_age_at_year_end\n        description: Patient age as of December 31 of the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 150\n              config:\n                severity: warn\n\n      - name: encounter_count\n        description: Number of encounters in the year\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 10000\n              config:\n                severity: error\n\n      - name: procedure_count\n        description: Number of procedures in the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: condition_count\n        description: Number of distinct conditions diagnosed in the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_cost\n        description: Total claim cost for the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 10000000\n              config:\n                severity: error\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: patient_responsibility\n        description: Amount owed by patient (copay + coinsurance)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: payer_coverage_pct\n        description: Percentage of costs covered by insurance (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: unique_providers\n        description: Number of distinct providers seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1\n                max_value: 1000\n              config:\n                severity: error\n\n      - name: unique_organizations\n        description: Number of distinct organizations visited\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_patient_summary","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_patient_summary_patient_summary_key.e532e1ff45"]},"evidence":[{"label":"unique_key","value":"patient_summary_key"}],"pin_node":"(final select)"},{"check":"enforcement.constraint-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_patient_summary","construct":"constraint.primary_key[(patient_id, year_actual)]","verdict":{"status":"covered","by":["test.healthcare_analytics.dbt_utils_unique_combination_of_columns_fct_patient_summary_patient_id__year_actual.cdb3418166"]},"evidence":[{"label":"constraint","value":"primary_key on (patient_id, year_actual)"},{"label":"adapter","value":"duckdb (metadata-only)"},{"label":"backing-test","value":"present"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_provider_metrics","path":"models/marts/analytics/fct_provider_metrics.sql","description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"provider_month_agg","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_provider_metrics.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"procedures","to":"encounters_with_date","edge_type":"left"},{"from":"encounters_with_date","to":"provider_month_agg","edge_type":"from"},{"from":"provider_month_agg","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":143,"col":1,"byte":3614},"end":{"line":143,"col":20,"byte":3633}},"dim_date":{"start":{"line":24,"col":1,"byte":452},"end":{"line":30,"col":2,"byte":582}},"encounters":{"start":{"line":5,"col":6,"byte":9},"end":{"line":22,"col":2,"byte":449}},"encounters_with_date":{"start":{"line":41,"col":1,"byte":815},"end":{"line":54,"col":2,"byte":1321}},"final":{"start":{"line":95,"col":1,"byte":2331},"end":{"line":141,"col":2,"byte":3612}},"procedures":{"start":{"line":32,"col":1,"byte":585},"end":{"line":38,"col":2,"byte":757}},"provider_month_agg":{"start":{"line":57,"col":1,"byte":1364},"end":{"line":93,"col":2,"byte":2328}}},"column_spans":{"(final 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select)"}},"column":"organization_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"year_month_str"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_month_str"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"unique_patient_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"unique_patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_procedures"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_procedures"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"avg_patient_age"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"avg_patient_age"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"avg_encounter_duration_minutes"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"avg_encounter_duration_minutes"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_payer_coverage"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_payer_coverage"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"avg_cost_per_encounter"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"avg_cost_per_encounter"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"encounters_per_patient_avg"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounters_per_patient_avg"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"_loaded_at"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"_loaded_at"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='provider_metrics_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Monthly Provider Metrics Fact\n\n    Purpose: Provider-level utilization and performance metrics\n    Grain: One row per provider per calendar month\n    Use cases: Provider performance dashboards, utilization trending, quality measures\n\n    Aggregates encounters, patients, procedures, and costs by provider and month.\n    Calculates derived metrics like avg cost per encounter and encounters per patient.\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","sql_diff":{"lines":[{"kind":"context","text":"{{","emphasis":null},{"kind":"context","text":"    config(","emphasis":null},{"kind":"context","text":"        materialized='table',","emphasis":null},{"kind":"context","text":"        unique_key='provider_metrics_key',","emphasis":null},{"kind":"context","text":"        tags=['analytics', 'marts', 'fact']","emphasis":null},{"kind":"context","text":"    )","emphasis":null},{"kind":"context","text":"}}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"{#","emphasis":null},{"kind":"context","text":"    Monthly Provider Metrics Fact","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    Purpose: Provider-level utilization and performance metrics","emphasis":null},{"kind":"context","text":"    Grain: One row per provider per calendar month","emphasis":null},{"kind":"context","text":"    Use cases: Provider performance dashboards, utilization trending, quality measures","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    Aggregates encounters, patients, procedures, and costs by provider and month.","emphasis":null},{"kind":"context","text":"    Calculates derived metrics like avg cost per encounter and encounters per patient.","emphasis":null},{"kind":"context","text":"#}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"with encounters as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        encounter_key","emphasis":null},{"kind":"context","text":"        , encounter_id","emphasis":null},{"kind":"context","text":"        , patient_key","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , provider_key","emphasis":null},{"kind":"context","text":"        , provider_id","emphasis":null},{"kind":"context","text":"        , organization_key","emphasis":null},{"kind":"context","text":"        , organization_id","emphasis":null},{"kind":"context","text":"        , encounter_start_date_key","emphasis":null},{"kind":"context","text":"        , total_claim_cost","emphasis":null},{"kind":"context","text":"        , payer_coverage","emphasis":null},{"kind":"context","text":"        , duration_minutes","emphasis":null},{"kind":"context","text":"        , patient_age_at_encounter","emphasis":null},{"kind":"context","text":"    from {{ ref('fct_encounters') }}","emphasis":null},{"kind":"context","text":"    where provider_key != -1","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"dim_date as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        date_key","emphasis":null},{"kind":"context","text":"        , year_actual","emphasis":null},{"kind":"context","text":"        , month_actual","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_date') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"procedures as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        encounter_id","emphasis":null},{"kind":"removed","text":"        , count(distinct procedure_code) as procedure_count","emphasis":[16,39]},{"kind":"added","text":"        , count(*) as procedure_count","emphasis":[16,17]},{"kind":"context","text":"    from {{ ref('stg_synthea__procedures') }}","emphasis":null},{"kind":"context","text":"    group by encounter_id","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"-- join encounters to date dimension to get year/month","emphasis":null},{"kind":"context","text":"encounters_with_date as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        e.*","emphasis":null},{"kind":"context","text":"        , d.year_actual","emphasis":null},{"kind":"context","text":"        , d.month_actual","emphasis":null},{"kind":"context","text":"        , cast(d.year_actual as varchar)","emphasis":null},{"kind":"context","text":"            || '-'","emphasis":null},{"kind":"context","text":"            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str","emphasis":null},{"kind":"removed","text":"        , pr.procedure_count as procedure_count","emphasis":[10,28]},{"kind":"added","text":"        , coalesce(pr.procedure_count, 0) as procedure_count","emphasis":[10,41]},{"kind":"context","text":"    from encounters as e","emphasis":null},{"kind":"context","text":"    inner join dim_date as d on e.encounter_start_date_key = d.date_key","emphasis":null},{"kind":"context","text":"    left join procedures as pr on e.encounter_id = pr.encounter_id","emphasis":null},{"kind":"context","text":"    where e.encounter_start_date_key != -1","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"-- aggregate by provider and year-month","emphasis":null},{"kind":"context","text":"provider_month_agg as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        e.provider_id","emphasis":null},{"kind":"context","text":"        , e.provider_key","emphasis":null},{"kind":"context","text":"        , e.organization_id","emphasis":null},{"kind":"context","text":"        , e.organization_key","emphasis":null},{"kind":"context","text":"        , e.year_actual","emphasis":null},{"kind":"context","text":"        , e.month_actual","emphasis":null},{"kind":"context","text":"        , e.year_month_str","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- counts","emphasis":null},{"kind":"context","text":"        , count(*) as encounter_count","emphasis":null},{"kind":"context","text":"        , count(distinct e.patient_id) as unique_patient_count","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- patient metrics","emphasis":null},{"kind":"context","text":"        , avg(e.patient_age_at_encounter) as avg_patient_age","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- encounter metrics","emphasis":null},{"kind":"context","text":"        , avg(e.duration_minutes) as avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- procedure metrics","emphasis":null},{"kind":"removed","text":"        , sum(e.procedure_count) as procedure_total","emphasis":[36,51]},{"kind":"added","text":"        , sum(e.procedure_count) as total_procedures","emphasis":[36,52]},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- cost metrics","emphasis":null},{"kind":"context","text":"        , sum(e.total_claim_cost) as total_cost","emphasis":null},{"kind":"context","text":"        , sum(e.payer_coverage) as total_payer_coverage","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    from encounters_with_date as e","emphasis":null},{"kind":"context","text":"    group by","emphasis":null},{"kind":"context","text":"        e.provider_id","emphasis":null},{"kind":"context","text":"        , e.provider_key","emphasis":null},{"kind":"context","text":"        , e.organization_id","emphasis":null},{"kind":"context","text":"        , e.organization_key","emphasis":null},{"kind":"context","text":"        , e.year_actual","emphasis":null},{"kind":"context","text":"        , e.month_actual","emphasis":null},{"kind":"context","text":"        , e.year_month_str","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"final as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        -- surrogate key","emphasis":null},{"kind":"context","text":"        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- dimension keys","emphasis":null},{"kind":"context","text":"        , provider_key","emphasis":null},{"kind":"context","text":"        , provider_id","emphasis":null},{"kind":"context","text":"        , organization_key","emphasis":null},{"kind":"context","text":"        , organization_id","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- time grain","emphasis":null},{"kind":"context","text":"        , year_actual","emphasis":null},{"kind":"context","text":"        , month_actual","emphasis":null},{"kind":"context","text":"        , year_month_str","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: counts","emphasis":null},{"kind":"context","text":"        , encounter_count","emphasis":null},{"kind":"context","text":"        , unique_patient_count","emphasis":null},{"kind":"context","text":"        , total_procedures","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: averages","emphasis":null},{"kind":"context","text":"        , round(avg_patient_age, 2) as avg_patient_age","emphasis":null},{"kind":"context","text":"        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: costs","emphasis":null},{"kind":"context","text":"        , coalesce(total_cost, 0) as total_cost","emphasis":null},{"kind":"context","text":"        , coalesce(total_payer_coverage, 0) as total_payer_coverage","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: derived ratios","emphasis":null},{"kind":"context","text":"        , case","emphasis":null},{"kind":"context","text":"            when encounter_count > 0","emphasis":null},{"kind":"context","text":"            then round(total_cost / encounter_count, 2)","emphasis":null},{"kind":"context","text":"            else 0","emphasis":null},{"kind":"context","text":"        end as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        , case","emphasis":null},{"kind":"context","text":"            when unique_patient_count > 0","emphasis":null},{"kind":"context","text":"            then round(cast(encounter_count as decimal) / unique_patient_count, 2)","emphasis":null},{"kind":"context","text":"            else 0","emphasis":null},{"kind":"context","text":"        end as encounters_per_patient_avg","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- metadata","emphasis":null},{"kind":"context","text":"        , current_timestamp as _loaded_at","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    from provider_month_agg","emphasis":null},{"kind":"context","text":")","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"select * from final","emphasis":null}]},"model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_provider_metrics\n    description: |\n      Monthly provider-level utilization and performance metrics.\n      One row per provider per calendar month with patient volumes, costs, and efficiency metrics.\n      Grain: (provider_id, year_actual, month_actual)\n\n      Use cases:\n      - Provider performance tracking\n      - Workload distribution analysis\n      - Cost efficiency comparisons\n      - Capacity planning and staffing\n      - Quality improvement initiatives\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: provider_id + year_month_str\n      owner: healthcare_analytics\n      uses:\n        - \"Provider productivity reporting\"\n        - \"Workload balancing\"\n        - \"Cost efficiency analysis\"\n        - \"Capacity planning\"\n      example_queries:\n        - name: \"Top providers by patient volume\"\n          description: \"Identify highest-volume providers for capacity planning\"\n          sql: |\n            select\n                p.provider_id\n                , p.name as provider_name\n                , p.specialty\n                , sum(fpm.encounter_count) as total_encounters\n                , sum(fpm.unique_patient_count) as total_patients\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n            from main_marts.fct_provider_metrics fpm\n            join main_marts.dim_providers p\n                on fpm.provider_key = p.provider_key\n            where fpm.year_actual = 2024\n            group by 1, 2, 3\n            order by total_encounters desc\n            limit 15\n\n        - name: \"Provider metrics trend by month\"\n          description: \"Track provider productivity over time\"\n          sql: |\n            select\n                fpm.year_month_str\n                , count(distinct fpm.provider_id) as active_providers\n                , sum(fpm.encounter_count) as total_encounters\n                , sum(fpm.unique_patient_count) as total_patients\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n                , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\n            from main_marts.fct_provider_metrics fpm\n            where fpm.year_actual >= 2023\n            group by fpm.year_month_str\n            order by fpm.year_month_str desc\n\n        - name: \"Provider efficiency comparison\"\n          description: \"Compare providers by cost and volume efficiency\"\n          sql: |\n            select\n                p.provider_id\n                , p.name as provider_name\n                , p.specialty\n                , sum(fpm.encounter_count) as total_encounters\n                , round(sum(fpm.total_cost), 2) as total_cost\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n                , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\n                , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\n            from main_marts.fct_provider_metrics fpm\n            join main_marts.dim_providers p\n                on fpm.provider_key = p.provider_key\n            where fpm.year_actual = 2024\n            group by 1, 2, 3\n            having sum(fpm.encounter_count) >= 10\n            order by avg_cost_per_encounter\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - provider_id\n              - year_actual\n              - month_actual\n          config:\n            severity: error\n    columns:\n      - name: provider_metrics_key\n        description: Surrogate key (MD5 hash of provider_id + year_month_str)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_providers')\n                field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations\n\n      - name: organization_id\n        description: Natural organization identifier\n\n      - name: year_actual\n        description: Calendar year\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Calendar month (1-12)\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1\n                max_value: 12\n              config:\n                severity: error\n\n      - name: year_month_str\n        description: Year-month string (YYYY-MM)\n\n      - name: encounter_count\n        description: Number of encounters in the month\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: unique_patient_count\n        description: Number of distinct patients seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_patient_age\n        description: Average age of patients seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 150\n              config:\n                severity: warn\n\n      - name: avg_encounter_duration_minutes\n        description: Average encounter duration in minutes\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_procedures\n        description: Total procedures performed\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_cost\n        description: Total claim cost for the month\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_cost_per_encounter\n        description: Average cost per encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounters_per_patient_avg\n        description: Average encounters per patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded","diff":{"lines":[{"kind":"context","text":"  - name: fct_provider_metrics","emphasis":null},{"kind":"context","text":"    description: |","emphasis":null},{"kind":"context","text":"      Monthly provider-level utilization and performance metrics.","emphasis":null},{"kind":"removed","text":"      One row per provider per month.","emphasis":[31,36]},{"kind":"added","text":"      One row per provider per calendar month with patient volumes, costs, and efficiency metrics.","emphasis":[31,97]},{"kind":"context","text":"      Grain: (provider_id, year_actual, month_actual)","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      Use cases:","emphasis":null},{"kind":"context","text":"      - Provider performance tracking","emphasis":null},{"kind":"context","text":"      - Workload distribution analysis","emphasis":null},{"kind":"context","text":"      - Cost efficiency comparisons","emphasis":null},{"kind":"context","text":"      - Capacity planning and staffing","emphasis":null},{"kind":"context","text":"      - Quality improvement initiatives","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    config:","emphasis":null},{"kind":"context","text":"      tags: ['analytics', 'marts', 'fact']","emphasis":null},{"kind":"context","text":"    meta:","emphasis":null},{"kind":"context","text":"      grain: provider_id + year_month_str","emphasis":null},{"kind":"context","text":"      owner: healthcare_analytics","emphasis":null},{"kind":"context","text":"      uses:","emphasis":null},{"kind":"context","text":"        - \"Provider productivity reporting\"","emphasis":null},{"kind":"context","text":"        - \"Workload balancing\"","emphasis":null},{"kind":"context","text":"        - \"Cost efficiency analysis\"","emphasis":null},{"kind":"context","text":"        - \"Capacity planning\"","emphasis":null},{"kind":"context","text":"      example_queries:","emphasis":null},{"kind":"context","text":"        - name: \"Top providers by patient volume\"","emphasis":null},{"kind":"context","text":"          description: \"Identify highest-volume providers for capacity planning\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                p.provider_id","emphasis":null},{"kind":"context","text":"                , p.name as provider_name","emphasis":null},{"kind":"context","text":"                , p.specialty","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , sum(fpm.unique_patient_count) as total_patients","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            join main_marts.dim_providers p","emphasis":null},{"kind":"context","text":"                on fpm.provider_key = p.provider_key","emphasis":null},{"kind":"context","text":"            where fpm.year_actual = 2024","emphasis":null},{"kind":"context","text":"            group by 1, 2, 3","emphasis":null},{"kind":"context","text":"            order by total_encounters desc","emphasis":null},{"kind":"context","text":"            limit 15","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        - name: \"Provider metrics trend by month\"","emphasis":null},{"kind":"context","text":"          description: \"Track provider productivity over time\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                fpm.year_month_str","emphasis":null},{"kind":"context","text":"                , count(distinct fpm.provider_id) as active_providers","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , sum(fpm.unique_patient_count) as total_patients","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            where fpm.year_actual >= 2023","emphasis":null},{"kind":"context","text":"            group by fpm.year_month_str","emphasis":null},{"kind":"context","text":"            order by fpm.year_month_str desc","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        - name: \"Provider efficiency comparison\"","emphasis":null},{"kind":"context","text":"          description: \"Compare providers by cost and volume efficiency\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                p.provider_id","emphasis":null},{"kind":"context","text":"                , p.name as provider_name","emphasis":null},{"kind":"context","text":"                , p.specialty","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , round(sum(fpm.total_cost), 2) as total_cost","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient","emphasis":null},{"kind":"context","text":"                , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            join main_marts.dim_providers p","emphasis":null},{"kind":"context","text":"                on fpm.provider_key = p.provider_key","emphasis":null},{"kind":"context","text":"            where fpm.year_actual = 2024","emphasis":null},{"kind":"context","text":"            group by 1, 2, 3","emphasis":null},{"kind":"context","text":"            having sum(fpm.encounter_count) >= 10","emphasis":null},{"kind":"context","text":"            order by avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    data_tests:","emphasis":null},{"kind":"context","text":"      # Grain test: enforce unique combination","emphasis":null},{"kind":"context","text":"      - dbt_utils.unique_combination_of_columns:","emphasis":null},{"kind":"context","text":"          arguments:","emphasis":null},{"kind":"context","text":"            combination_of_columns:","emphasis":null},{"kind":"context","text":"              - provider_id","emphasis":null},{"kind":"context","text":"              - year_actual","emphasis":null},{"kind":"context","text":"              - month_actual","emphasis":null},{"kind":"context","text":"          config:","emphasis":null},{"kind":"context","text":"            severity: error","emphasis":null},{"kind":"context","text":"    columns:","emphasis":null},{"kind":"context","text":"      - name: provider_metrics_key","emphasis":null},{"kind":"context","text":"        description: Surrogate key (MD5 hash of provider_id + year_month_str)","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - unique","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: provider_key","emphasis":null},{"kind":"context","text":"        description: Foreign key to dim_providers","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - relationships:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                to: ref('dim_providers')","emphasis":null},{"kind":"context","text":"                field: provider_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: provider_id","emphasis":null},{"kind":"context","text":"        description: Natural provider identifier","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: organization_key","emphasis":null},{"kind":"context","text":"        description: Foreign key to dim_organizations","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: organization_id","emphasis":null},{"kind":"context","text":"        description: Natural organization identifier","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: year_actual","emphasis":null},{"kind":"context","text":"        description: Calendar year","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 1900","emphasis":null},{"kind":"context","text":"                max_value: 2100","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: warn","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: month_actual","emphasis":null},{"kind":"context","text":"        description: Calendar month (1-12)","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 1","emphasis":null},{"kind":"context","text":"                max_value: 12","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: year_month_str","emphasis":null},{"kind":"context","text":"        description: Year-month string (YYYY-MM)","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: encounter_count","emphasis":null},{"kind":"context","text":"        description: Number of encounters in the month","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: unique_patient_count","emphasis":null},{"kind":"context","text":"        description: Number of distinct patients seen","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_patient_age","emphasis":null},{"kind":"context","text":"        description: Average age of patients seen","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"                max_value: 150","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: warn","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"        description: Average encounter duration in minutes","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_procedures","emphasis":null},{"kind":"context","text":"        description: Total procedures performed","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_cost","emphasis":null},{"kind":"context","text":"        description: Total claim cost for the month","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_payer_coverage","emphasis":null},{"kind":"context","text":"        description: Amount covered by insurance","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"        description: Average cost per encounter","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: encounters_per_patient_avg","emphasis":null},{"kind":"context","text":"        description: Average encounters per patient","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: _loaded_at","emphasis":null},{"kind":"context","text":"        description: Timestamp when record was loaded","emphasis":null}]}},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_provider_metrics","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_provider_metrics_provider_metrics_key.d58939d602"]},"evidence":[{"label":"unique_key","value":"provider_metrics_key"}],"degraded":[{"by":"test.healthcare_analytics.unique_fct_provider_metrics_provider_metrics_key.d58939d602","causes":["where-filtered — only rows matching `year_actual >= 2024` are checked; the grain is unverified outside the filter","limit: 100 — failing-row reporting caps at 100 rows; the test still trips on the first failure, but the failure surface is truncated"]}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":true,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_provider_scorecard","path":"models/marts/analytics/fct_provider_scorecard.sql","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).\nselect\n    provider_id\n    , avg(quality_score) as avg_quality_score\n    , count(*) as encounter_count\nfrom \"memory\".\"main_marts\".\"fct_provider_metrics\"\ngroup by provider_id"},"code_map":{"compiled":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).\nselect\n    provider_id\n    , avg(quality_score) as avg_quality_score\n    , count(*) as encounter_count\nfrom \"memory\".\"main_marts\".\"fct_provider_metrics\"\ngroup by provider_id","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":7,"col":21,"byte":250}}},"column_spans":{"(final select)\u001favg_quality_score":{"start":{"line":4,"col":7,"byte":106},"end":{"line":4,"col":46,"byte":145}},"(final select)\u001fencounter_count":{"start":{"line":5,"col":7,"byte":152},"end":{"line":5,"col":34,"byte":179}},"(final select)\u001fprovider_id":{"start":{"line":3,"col":5,"byte":88},"end":{"line":3,"col":16,"byte":99}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"fct_provider_metrics"}},"column":"provider_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"provider_id"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'scorecard']\n    )\n}}\n\n-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).\nselect\n    provider_id\n    , avg(quality_score) as avg_quality_score\n    , count(*) as encounter_count\nfrom {{ ref('fct_provider_metrics') }}\ngroup by provider_id","sql_diff":{"lines":[{"kind":"added","text":"{{","emphasis":null},{"kind":"added","text":"    config(","emphasis":null},{"kind":"added","text":"        materialized='table',","emphasis":null},{"kind":"added","text":"        tags=['analytics', 'scorecard']","emphasis":null},{"kind":"added","text":"    )","emphasis":null},{"kind":"added","text":"}}","emphasis":null},{"kind":"added","text":"","emphasis":null},{"kind":"added","text":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).","emphasis":null},{"kind":"added","text":"select","emphasis":null},{"kind":"added","text":"    provider_id","emphasis":null},{"kind":"added","text":"    , avg(quality_score) as avg_quality_score","emphasis":null},{"kind":"added","text":"    , count(*) as encounter_count","emphasis":null},{"kind":"added","text":"from {{ ref('fct_provider_metrics') }}","emphasis":null},{"kind":"added","text":"group by provider_id","emphasis":null}]},"model_yaml":{"missing":"No schema file declares this model in the manifest — there is no authored models: entry to show."},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_provider_scorecard","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"provider_metrics_key"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns."}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":true,"config":false,"unit_test":false},"state":"new"},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final select)\u001f*":{"start":{"line":30,"col":8,"byte":542},"end":{"line":30,"col":9,"byte":543}},"grid\u001fmonth_actual":{"start":{"line":23,"col":11,"byte":423},"end":{"line":23,"col":30,"byte":442}},"grid\u001fmonth_key":{"start":{"line":21,"col":14,"byte":312},"end":{"line":21,"col":85,"byte":383}},"grid\u001fmonth_name":{"start":{"line":24,"col":11,"byte":453},"end":{"line":24,"col":28,"byte":470}},"grid\u001fstate":{"start":{"line":25,"col":11,"byte":481},"end":{"line":25,"col":23,"byte":493}},"grid\u001fyear_actual":{"start":{"line":22,"col":11,"byte":394},"end":{"line":22,"col":29,"byte":412}},"months\u001fmonth_actual":{"start":{"line":8,"col":11,"byte":71},"end":{"line":8,"col":23,"byte":83}},"months\u001fmonth_name":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":21,"byte":104}},"months\u001fyear_actual":{"start":{"line":7,"col":9,"byte":49},"end":{"line":7,"col":20,"byte":60}},"states\u001fstate":{"start":{"line":14,"col":21,"byte":183},"end":{"line":14,"col":26,"byte":188}}}},"column_lineage":{"context":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_date_state_grid\n    description: |\n      Dense month x state reporting scaffold.\n      One row per (calendar month x patient state) combination — the full\n      cartesian product of dim_date months and the non-null states observed\n      in patient staging. Left-join sparse activity facts against this grid\n      so zero-activity cells render as explicit zeros.\n      Grain: month_key x state\n\n      Use cases:\n      - Dense time-series dashboards (no missing month/state cells)\n      - Zero-activity gap detection\n      - cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n\n    data_tests:\n      # New-style `arguments:` nesting (fusion-clean; dbt-core 1.10+).\n      # The legacy top-level-args form elsewhere in this file predates\n      # the planned dbt-autofix sweep.\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - month_key\n              - state\n\n    config:\n      tags: ['analytics', 'reporting', 'marts']\n      meta:\n        grain: month_key x state\n        owner: healthcare_analytics\n        uses:\n          - \"Dense reporting grids\"\n          - \"Zero-activity gap detection\"\n\n    columns:\n      - name: month_key\n        description: \"Month surrogate key in YYYYMM integer form (e.g., 202401)\"\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: \"Calendar year of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_actual\n        description: \"Calendar month number (1-12) of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_name\n        description: \"Full month name (e.g., January)\"\n\n      - name: state\n        description: \"Patient state, distinct and non-null from staging\"\n        data_tests:\n          - not_null"},"tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # Cross-join demo (cute-dbt#64): mart_date_state_grid's CROSS JOIN\n  # CTE gives cute-dbt's renderer a real cross-join edge to draw. This\n  # test also mixes given formats (dict + csv) in a single unit_test.\n  - name: test_mart_date_state_grid_cross_joins_months_and_states\n    description: |\n      mart_date_state_grid CROSS JOINs the distinct calendar months from\n      dim_date with the distinct non-null patient states from staging.\n      Three daily dim_date rows collapse to two months; four patient\n      rows collapse to two states (one NULL filtered out, one duplicate\n      deduplicated). The result is the full 2 x 2 cartesian product —\n      four grid rows. Mixes a dict given (dim_date) with a csv given\n      (patients) in one test.\n    model: mart_date_state_grid\n    given:\n      - input: ref('dim_date')\n        format: dict\n        rows:\n          - date_actual: 2024-01-15\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-01-16\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-02-01\n            year_actual: 2024\n            month_actual: 2\n            month_name: 'February'\n      - input: ref('stg_synthea__patients')\n        format: csv\n        fixture: mart_date_state_grid_patients\n    expect:\n      format: csv\n      rows: |\n        month_key,year_actual,month_actual,month_name,state\n        202401,2024,1,January,Massachusetts\n        202401,2024,1,January,Connecticut\n        202402,2024,2,February,Massachusetts\n        202402,2024,2,February,Connecticut","data_diff":{"given":[{"ordinal":1,"input":"ref('stg_synthea__patients')","diff":{"columns":[{"name":"patient_id","status":"present"},{"name":"state","status":"present"}],"rows":[{"kind":"unchanged","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},"new":{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},"changed":false},{"old":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"new":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"changed":false}]},{"kind":"modified","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},"new":{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},"changed":false},{"old":{"display":"Connecticutt","key":{"t":"str","v":"Connecticutt"}},"new":{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},"changed":true}]},{"kind":"modified","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},"new":{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},"changed":false},{"old":{"display":"Vermont","key":{"t":"str","v":"Vermont"}},"new":{"display":"","key":{"t":"null"}},"changed":true}]},{"kind":"unchanged","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},"new":{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},"changed":false},{"old":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"new":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"changed":false}]}]}}],"expect":null},"given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","table":{"columns":["patient_id","state"],"rows":[{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},{"display":"","key":{"t":"null"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":"patient_id,state\na00000aa-0000-0000-0000-000000000001,Massachusetts\na00000aa-0000-0000-0000-000000000002,Connecticut\na00000aa-0000-0000-0000-000000000003,\na00000aa-0000-0000-0000-000000000004,Massachusetts\n","format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv","column_meta":{"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"var_references":[{"name":"default_state_grid_density","tier":"config"}],"axes":{"body":false,"config":true,"unit_test":true},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_dq_summary\n    description: |\n      Data quality summary metrics for monitoring quarantine rates and validation failures.\n      One row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\n      Grain: entity_type\n\n      Use cases:\n      - Data quality monitoring dashboards\n      - Quarantine rate trending\n      - Validation failure root cause analysis\n      - Data quality SLA tracking\n\n    config:\n      tags: ['analytics', 'data_quality', 'marts']\n    meta:\n      grain: entity_type\n      owner: healthcare_analytics\n      uses:\n        - \"Data quality dashboards\"\n        - \"Quarantine monitoring\"\n        - \"Data quality alerting\"\n\n    columns:\n      - name: entity_type\n        description: \"Type of entity being measured (encounters, medications)\"\n        tests:\n          - not_null\n          - accepted_values:\n              values: ['encounters', 'medications']\n\n      - name: quarantined_count\n        description: \"Number of records failing data quality validations\"\n        tests:\n          - not_null\n\n      - name: total_count\n        description: \"Total number of records (valid + quarantined)\"\n        tests:\n          - not_null\n\n      - name: quarantine_rate_pct\n        description: \"Percentage of records quarantined (quarantined_count / total_count * 100)\"\n        tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 100\n\n      - name: failed_timestamp_validations\n        description: \"Count of records failing timestamp/date sequence validations\"\n\n      - name: failed_future_date_validations\n        description: \"Count of records with future dates\"\n\n      - name: failed_historical_end_validations\n        description: \"Count of records with end dates before 1900\"\n\n      - name: failed_historical_start_validations\n        description: \"Count of records with start dates before 1900\"\n\n      - name: _generated_at\n        description: \"Timestamp when summary was generated\""},"tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 1 — the slicer attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test (no blank-line gap below) to this test's authored slice\n  # (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_combines_encounter_and_medication_metrics\n    description: |\n      mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\n      and stg_synthea__medications. This test mocks two encounter rows\n      (one invalid) and two medication rows (both invalid), then asserts\n      the per-entity counts + quarantine rate. Uses csv format for both\n      GIVEN and EXPECT — compact tabular form across many boolean\n      dq-flag columns.\n    model: mart_dq_summary\n    # INSIDE bracket for test 1 — comments at deeper indent than\n    # `- name:` ride along with the body slice as raw text (cute-dbt#69\n    # / #73).\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: csv\n        rows: |\n          is_dq_valid,valid_encounter_timestamps,no_future_encounter_dates,end_after_1900,start_after_1900\n          false,false,true,true,true\n          true,true,true,true,true\n      - input: ref(\"stg_synthea__medications\")\n        format: csv\n        rows: |\n          is_dq_valid,valid_medication_dates,no_future_medication_dates,start_after_1900,end_after_1900_if_present\n          false,false,true,true,true\n          false,true,false,true,true\n    expect:\n      format: csv\n      rows: |\n        entity_type,quarantined_count,total_count,quarantine_rate_pct,failed_timestamp_validations\n        encounters,1,2,50.00,1\n        medications,2,2,100.00,1\n  # TRAILING bracket for test 1 — the comment block butts against the\n  # body above (no blank-line gap) and is terminated below by the blank\n  # line separating the two tests (cute-dbt#69 / #73).","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":true,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. Uses sql format for the GIVEN mocks and dict format\nfor the EXPECT — dict expects only compare listed columns, so\nnon-deterministic outputs (`_generated_at` from\n`current_timestamp`) are excluded from comparison.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 2 — sits between the blank-line\n  # terminator above and `- name:` below with no further gap, so the\n  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_zero_quarantined_when_all_valid\n    description: |\n      mart_dq_summary should report zero quarantined_count and 0.00\n      quarantine_rate_pct when every upstream row passes data-quality\n      validations. Uses sql format for the GIVEN mocks and dict format\n      for the EXPECT — dict expects only compare listed columns, so\n      non-deterministic outputs (`_generated_at` from\n      `current_timestamp`) are excluded from comparison.\n    model: mart_dq_summary\n    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /\n    # #73). Demonstrates that the slicer ride-along applies to every\n    # unit_test, not just the first in the file.\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_encounter_timestamps\n            , true as no_future_encounter_dates\n            , true as end_after_1900\n            , true as start_after_1900\n      - input: ref(\"stg_synthea__medications\")\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_medication_dates\n            , true as no_future_medication_dates\n            , true as start_after_1900\n            , true as end_after_1900_if_present\n    expect:\n      format: dict\n      rows:\n        - entity_type: 'encounters'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n        - entity_type: 'medications'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n    overrides:\n      macros:\n        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"\n      vars:\n        dq_quarantine_threshold: 5\n      env_vars:\n        DBT_DQ_REGION: 'us-east-1'\n  # TRAILING bracket for test 2 — terminated by the blank line\n  # separating it from test 3 (cute-dbt#69 / #73).","yaml_diff":{"lines":[{"kind":"context","text":"  # LEADING bracket for test 2 — sits between the blank-line","emphasis":null},{"kind":"context","text":"  # terminator above and `- name:` below with no further gap, so the","emphasis":null},{"kind":"context","text":"  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).","emphasis":null},{"kind":"context","text":"  - name: test_mart_dq_summary_zero_quarantined_when_all_valid","emphasis":null},{"kind":"context","text":"    description: |","emphasis":null},{"kind":"context","text":"      mart_dq_summary should report zero quarantined_count and 0.00","emphasis":null},{"kind":"context","text":"      quarantine_rate_pct when every upstream row passes data-quality","emphasis":null},{"kind":"context","text":"      validations. Uses sql format for the GIVEN mocks and dict format","emphasis":null},{"kind":"context","text":"      for the EXPECT — dict expects only compare listed columns, so","emphasis":null},{"kind":"context","text":"      non-deterministic outputs (`_generated_at` from","emphasis":null},{"kind":"context","text":"      `current_timestamp`) are excluded from comparison.","emphasis":null},{"kind":"context","text":"    model: mart_dq_summary","emphasis":null},{"kind":"context","text":"    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /","emphasis":null},{"kind":"context","text":"    # #73). Demonstrates that the slicer ride-along applies to every","emphasis":null},{"kind":"context","text":"    # unit_test, not just the first in the file.","emphasis":null},{"kind":"context","text":"    given:","emphasis":null},{"kind":"context","text":"      - input: ref('stg_synthea__encounters')","emphasis":null},{"kind":"context","text":"        format: sql","emphasis":null},{"kind":"context","text":"        rows: |","emphasis":null},{"kind":"context","text":"          select","emphasis":null},{"kind":"context","text":"            true as is_dq_valid","emphasis":null},{"kind":"context","text":"            , true as valid_encounter_timestamps","emphasis":null},{"kind":"context","text":"            , true as no_future_encounter_dates","emphasis":null},{"kind":"context","text":"            , true as end_after_1900","emphasis":null},{"kind":"context","text":"            , true as start_after_1900","emphasis":null},{"kind":"context","text":"      - input: ref(\"stg_synthea__medications\")","emphasis":null},{"kind":"context","text":"        format: sql","emphasis":null},{"kind":"context","text":"        rows: |","emphasis":null},{"kind":"context","text":"          select","emphasis":null},{"kind":"context","text":"            true as is_dq_valid","emphasis":null},{"kind":"context","text":"            , true as valid_medication_dates","emphasis":null},{"kind":"context","text":"            , true as no_future_medication_dates","emphasis":null},{"kind":"context","text":"            , true as start_after_1900","emphasis":null},{"kind":"context","text":"            , true as end_after_1900_if_present","emphasis":null},{"kind":"context","text":"    expect:","emphasis":null},{"kind":"context","text":"      format: dict","emphasis":null},{"kind":"context","text":"      rows:","emphasis":null},{"kind":"context","text":"        - entity_type: 'encounters'","emphasis":null},{"kind":"removed","text":"          quarantined_count: null","emphasis":[29,33]},{"kind":"removed","text":"          total_count: 2","emphasis":[23,24]},{"kind":"added","text":"          quarantined_count: 0","emphasis":[29,30]},{"kind":"added","text":"          total_count: 1","emphasis":[23,24]},{"kind":"context","text":"          quarantine_rate_pct: 0.00","emphasis":null},{"kind":"context","text":"        - entity_type: 'medications'","emphasis":null},{"kind":"removed","text":"          quarantined_count: 1","emphasis":[29,30]},{"kind":"removed","text":"          total_count: 2","emphasis":[23,24]},{"kind":"added","text":"          quarantined_count: 0","emphasis":[29,30]},{"kind":"added","text":"          total_count: 1","emphasis":[23,24]},{"kind":"context","text":"          quarantine_rate_pct: 0.00","emphasis":null},{"kind":"context","text":"    overrides:","emphasis":null},{"kind":"context","text":"      macros:","emphasis":null},{"kind":"context","text":"        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"","emphasis":null},{"kind":"context","text":"      vars:","emphasis":null},{"kind":"removed","text":"        dq_quarantine_threshold: 10","emphasis":[33,35]},{"kind":"added","text":"        dq_quarantine_threshold: 5","emphasis":[33,34]},{"kind":"context","text":"      env_vars:","emphasis":null},{"kind":"context","text":"        DBT_DQ_REGION: 'us-east-1'","emphasis":null},{"kind":"context","text":"  # TRAILING bracket for test 2 — terminated by the blank line","emphasis":null},{"kind":"context","text":"  # separating it from test 3 (cute-dbt#69 / #73).","emphasis":null}]},"data_diff":{"given":[],"expect":{"columns":[{"name":"entity_type","status":"present"},{"name":"quarantine_rate_pct","status":"present"},{"name":"quarantined_count","status":"present"},{"name":"total_count","status":"present"}],"rows":[{"kind":"modified","cells":[{"old":{"display":"encounters","key":{"t":"str","v":"encounters"}},"new":{"display":"encounters","key":{"t":"str","v":"encounters"}},"changed":false},{"old":{"display":"0.00","key":{"t":"number","v":"0"}},"new":{"display":"0.0","key":{"t":"number","v":"0"}},"changed":false},{"old":{"display":"","key":{"t":"null"}},"new":{"display":"0","key":{"t":"number","v":"0"}},"changed":true},{"old":{"display":"2","key":{"t":"number","v":"2"}},"new":{"display":"1","key":{"t":"number","v":"1"}},"changed":true}]},{"kind":"modified","cells":[{"old":{"display":"medications","key":{"t":"str","v":"medications"}},"new":{"display":"medications","key":{"t":"str","v":"medications"}},"changed":false},{"old":{"display":"0.00","key":{"t":"number","v":"0"}},"new":{"display":"0.0","key":{"t":"number","v":"0"}},"changed":false},{"old":{"display":"1","key":{"t":"number","v":"1"}},"new":{"display":"0","key":{"t":"number","v":"0"}},"changed":true},{"old":{"display":"2","key":{"t":"number","v":"2"}},"new":{"display":"1","key":{"t":"number","v":"1"}},"changed":true}]}]}},"overrides":{"env_vars":{"DBT_DQ_REGION":"us-east-1"},"macros":{"dbt.current_timestamp":"'2024-01-15 00:00:00'"},"vars":{"dq_quarantine_threshold":5}},"given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["is_dq_valid","valid_encounter_timestamps","no_future_encounter_dates","end_after_1900","start_after_1900"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_encounter_timestamps\n  , true as no_future_encounter_dates\n  , true as end_after_1900\n  , true as start_after_1900","format":"sql"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["is_dq_valid","valid_medication_dates","no_future_medication_dates","start_after_1900","end_after_1900_if_present"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_medication_dates\n  , true as no_future_medication_dates\n  , true as start_after_1900\n  , true as end_after_1900_if_present","format":"sql"}],"expected":{"table":{"columns":["entity_type","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]}]},"rows":[{"entity_type":"encounters","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1},{"entity_type":"medications","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1}],"format":"dict","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"entity_type"},{"label":"exists but disabled","value":"test.healthcare_analytics.unique_mart_dq_summary_entity_type.4f2a9c7d10 — a uniqueness test on (entity_type) is declared but disabled (config.enabled: false); it asserts nothing and never counts as coverage"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"var_references":[{"name":"dq_quarantine_threshold","tier":"direct"}],"axes":{"body":false,"config":true,"unit_test":true},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_encounter_summary","path":"models/marts/analytics/views/v_encounter_summary.sql","description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"patients","role":"import"},{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"payers","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_encounter_summary.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"providers","to":"final","edge_type":"left"},{"from":"organizations","to":"final","edge_type":"left"},{"from":"payers","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_at\n        , encounter_class\n        , encounter_description\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_name\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , full_name as patient_name\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_name\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_name\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_at\n        , encounter_class\n        , encounter_description\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\npatients as (\n    select\n        patient_key\n        , full_name as patient_name\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_name\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_name\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_name\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\nfinal as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":95,"col":1,"byte":2262},"end":{"line":95,"col":20,"byte":2281}},"encounters":{"start":{"line":5,"col":6,"byte":9},"end":{"line":25,"col":2,"byte":509}},"final":{"start":{"line":56,"col":1,"byte":1041},"end":{"line":93,"col":2,"byte":2260}},"organizations":{"start":{"line":42,"col":1,"byte":793},"end":{"line":47,"col":2,"byte":928}},"patients":{"start":{"line":27,"col":1,"byte":512},"end":{"line":33,"col":2,"byte":668}},"payers":{"start":{"line":49,"col":1,"byte":931},"end":{"line":54,"col":2,"byte":1038}},"providers":{"start":{"line":35,"col":1,"byte":671},"end":{"line":40,"col":2,"byte":790}}},"column_spans":{"(final 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of encounter","documented":true},"encounter_date":{"description":"Date of the encounter","documented":true},"encounter_id":{"description":"Unique encounter identifier","documented":true},"organization_id":{"description":"Organization identifier","documented":true},"organization_name":{"description":"Organization name","documented":true},"patient_id":{"description":"Patient identifier","documented":true},"patient_name":{"description":"Full patient name","documented":true},"patient_responsibility":{"description":"Amount owed by patient","documented":true},"payer_coverage":{"description":"Amount covered by insurance","documented":true},"payer_id":{"description":"Payer identifier","documented":true},"payer_name":{"description":"Payer name","documented":true},"provider_id":{"description":"Provider identifier","documented":true},"provider_name":{"description":"Full provider name","documented":true},"total_cost":{"description":"Total claim 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provider_name\n    from {{ ref('dim_providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_name\n    from {{ ref('dim_organizations') }}\n),\n\npayers as (\n    select\n        payer_key\n        , payer_name\n    from {{ ref('dim_payers') }}\n),\n\nfinal as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_encounter_summary\n    description: |\n      Enriched encounter view with dimension attributes for ad-hoc analysis.\n      Ephemeral (not persisted) - used as a building block for other queries.\n      Joins encounters with patient, provider, organization, and payer details.\n\n      Use cases:\n      - Ad-hoc encounter analysis\n      - Building block for custom reports\n      - Quick encounter lookups with full context\n\n    config:\n      materialized: ephemeral\n      tags: ['analytics', 'marts', 'view']\n    columns:\n      - name: encounter_id\n        description: Unique encounter identifier\n\n      - name: encounter_date\n        description: Date of the encounter\n\n      - name: patient_id\n        description: Patient identifier\n\n      - name: patient_name\n        description: Full patient name\n\n      - name: provider_id\n        description: Provider identifier\n\n      - name: provider_name\n        description: Full provider name\n\n      - name: organization_id\n        description: Organization identifier\n\n      - name: organization_name\n        description: Organization name\n\n      - name: payer_id\n        description: Payer identifier\n\n      - name: payer_name\n        description: Payer name\n\n      - name: encounter_class\n        description: Type of encounter\n\n      - name: total_cost\n        description: Total claim cost\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n\n      - name: patient_responsibility\n        description: Amount owed by patient"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_patient_current_conditions","path":"models/marts/analytics/views/v_patient_current_conditions.sql","description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition 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patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_patient_current_conditions\n    description: |\n      Current (active) conditions for each patient, filtered from fct_condition_cohorts.\n      Use for patient panels, disease management dashboards, and care gap identification.\n      Grain: (patient_id, condition_code) - only active conditions\n\n      Use cases:\n      - Patient panel management\n      - Active disease registries\n      - Care gap identification\n      - Real-time patient condition views\n\n    config:\n      materialized: table\n      tags: ['analytics', 'marts', 'view']\n    meta:\n      grain: patient_id + condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Patient panel displays\"\n        - \"Active condition registries\"\n        - \"Care gap dashboards\"\n      example_queries:\n        - name: \"Patient active conditions lookup\"\n          description: \"Get all active conditions for a specific patient\"\n          sql: |\n            select\n                vpcc.patient_id\n                , vpcc.condition_code\n                , vpcc.condition_description\n                , vpcc.first_diagnosis_date\n                , vpcc.months_with_condition\n                , vpcc.encounter_count\n            from main_marts.v_patient_current_conditions vpcc\n            where vpcc.patient_id = 'patient-uuid-here'\n            order by vpcc.first_diagnosis_date\n\n        - name: \"Active condition summary\"\n          description: \"Count of patients by active condition for disease registries\"\n          sql: |\n            select\n                vpcc.condition_code\n                , vpcc.condition_description\n                , count(distinct vpcc.patient_id) as active_patient_count\n                , round(avg(vpcc.months_with_condition), 1) as avg_duration_months\n                , round(avg(vpcc.encounter_count), 1) as avg_encounters\n            from main_marts.v_patient_current_conditions vpcc\n            group by 1, 2\n            order by active_patient_count desc\n            limit 20\n\n    columns:\n      - name: patient_key\n        description: Foreign key to dim_patients\n\n      - name: patient_id\n        description: Natural patient identifier\n\n      - name: condition_key\n        description: Foreign key to dim_conditions\n        data_tests:\n          - relationships:\n              arguments:\n                to: ref('dim_conditions')\n                field: condition_key\n\n      - name: condition_code\n        description: ICD/SNOMED condition code\n\n      - name: condition_description\n        description: Full condition name\n\n      - name: first_diagnosis_date\n        description: Date of first diagnosis\n\n      - name: months_with_condition\n        description: Duration of condition in months\n\n      - name: encounter_count\n        description: Number of encounters with this condition"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_provider_active_patients","path":"models/marts/analytics/views/v_provider_active_patients.sql","description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","dag":{"nodes":[{"id":"providers","role":"import"},{"id":"current_conditions","role":"import"},{"id":"patient_recent_encounters","role":"import"},{"id":"patient_provider_assignment","role":"transform"},{"id":"active_conditions_by_provider","role":"transform"},{"id":"provider_summary","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_provider_active_patients.sql","role":"final"}],"edges":[{"from":"patient_recent_encounters","to":"patient_provider_assignment","edge_type":"from"},{"from":"current_conditions","to":"active_conditions_by_provider","edge_type":"from"},{"from":"patient_provider_assignment","to":"active_conditions_by_provider","edge_type":"inner"},{"from":"active_conditions_by_provider","to":"provider_summary","edge_type":"from"},{"from":"providers","to":"final","edge_type":"from"},{"from":"provider_summary","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","active_conditions_by_provider":"active_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":96,"col":1,"byte":2429},"end":{"line":96,"col":20,"byte":2448}},"active_conditions_by_provider":{"start":{"line":52,"col":1,"byte":1176},"end":{"line":61,"col":2,"byte":1451}},"current_conditions":{"start":{"line":17,"col":1,"byte":294},"end":{"line":25,"col":2,"byte":523}},"final":{"start":{"line":74,"col":1,"byte":1744},"end":{"line":94,"col":2,"byte":2427}},"patient_provider_assignment":{"start":{"line":42,"col":1,"byte":962},"end":{"line":49,"col":2,"byte":1123}},"patient_recent_encounters":{"start":{"line":28,"col":1,"byte":599},"end":{"line":39,"col":2,"byte":910}},"provider_summary":{"start":{"line":64,"col":1,"byte":1479},"end":{"line":72,"col":2,"byte":1741}},"providers":{"start":{"line":5,"col":6,"byte":9},"end":{"line":14,"col":2,"byte":232}}},"column_spans":{"(final 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Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_provider_active_patients\n    description: |\n      Active patient count per provider for panel management and workload analysis.\n      Aggregates patients with active conditions by their treating provider.\n      Grain: provider_id\n\n      Use cases:\n      - Provider panel size management\n      - Workload distribution analysis\n      - Capacity planning\n      - Provider assignment optimization\n\n    config:\n      materialized: table\n      tags: ['analytics', 'marts', 'view']\n    meta:\n      grain: provider_id\n      owner: healthcare_analytics\n      uses:\n        - \"Provider panel management\"\n        - \"Workload balancing\"\n        - \"Capacity planning\"\n      example_queries:\n        - name: \"Provider panel sizes\"\n          description: \"View provider workload by active patient and condition counts\"\n          sql: |\n            select\n                vpap.provider_id\n                , vpap.provider_name\n                , vpap.active_patient_count\n                , vpap.total_active_conditions\n                , vpap.avg_conditions_per_patient\n            from main_marts.v_provider_active_patients vpap\n            where vpap.active_patient_count > 0\n            order by vpap.active_patient_count desc\n            limit 25\n\n        - name: \"Provider workload distribution\"\n          description: \"Analyze distribution of patient panels across providers\"\n          sql: |\n            select\n                case\n                    when vpap.active_patient_count = 0 then '0'\n                    when vpap.active_patient_count \u003c= 10 then '1-10'\n                    when vpap.active_patient_count \u003c= 50 then '11-50'\n                    when vpap.active_patient_count \u003c= 100 then '51-100'\n                    else '100+'\n                end as panel_size_bucket\n                , count(*) as provider_count\n                , sum(vpap.active_patient_count) as total_patients\n            from main_marts.v_provider_active_patients vpap\n            group by 1\n            order by 1\n\n    columns:\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_name\n        description: Full name of the provider\n\n      - name: organization_key\n        description: Foreign key to dim_organizations\n\n      - name: organization_id\n        description: Natural organization identifier\n\n      - name: active_patient_count\n        description: Number of patients with active conditions\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_active_conditions\n        description: Total number of active conditions managed\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_conditions_per_patient\n        description: Average number of active conditions per patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). Includes an unknown member row (provider_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"provider_id","description":"Natural provider identifier (UUID)","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"enforcement.constraint-unbacked":{"name":"Declared constraint without a backing test","group":"enforcement","tier":"total","instrument":"data-test","conditions":["the model declares a primary_key or unique constraint (model-level constraints[] or a column-level constraint) whose enforcement on the manifest's adapter is metadata-only (NotEnforced) — the warehouse accepts the declaration but does not enforce uniqueness at write time","no enabled generic uniqueness data test (unique, attached to the model) backs the constrained column — the constraint→test edge is INFERRED by column + test-name match, since the manifest never links a constraint to its test","the verdict is UNCOVERED only when the constraint is declared, metadata-only, AND has no inferred backing test; a backing test (even one cute-dbt could miss) keeps it silent"],"exclusions":["a constraint the adapter ENFORCES at write time (e.g. not_null / foreign_key on Postgres/DuckDB) is never a gap — the warehouse guarantees it","a constraint kind with no column-level generic-test backing (check / custom / foreign_key) is out of this inference and never reported here","the inferred edge can MISS a renamed test or a singular/custom test asserting the same uniqueness — the copy says \"backing test\" (an authoring-discipline cue), never that the warehouse lacks an index; columns are authored-YAML-only, so this is never a warehouse-truth claim","the whole `enforcement` group is gated behind the governance experiment — off by default, it never fires on a non-governance report"],"rationale":"A primary-key / unique constraint that the warehouse treats as metadata-only is a DECLARED guarantee with nothing checking it: duplicate rows load silently, and any downstream join or incremental merge that trusts the declared grain corrupts. A backing data test is the only thing that actually verifies the declared uniqueness on this adapter.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/enforcement.constraint-unbacked.html"},"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}},"project_definition":{"name":"healthcare_analytics","version":"1.0.0","require_dbt_version":">=1.8.0","vars":{"default_state_grid_density":"monthly","dq_quarantine_threshold":5},"vars_spans":{"default_state_grid_density":{"line":12,"column":3},"dq_quarantine_threshold":{"line":11,"column":3}},"config_trees":{"models":{"children":{"healthcare_analytics":{"configs":{"materialized":"view"},"children":{"marts":{"configs":{"materialized":"table"}},"staging":{"configs":{"materialized":"view"}}}}}}},"dispatch":[{"macro_namespace":"dbt_utils","search_order":["healthcare_analytics","dbt_utils"]}],"on_run_start":["grant select on all tables in schema analytics to role reporting"],"paths":{"clean-targets":["target","dbt_packages"],"model-paths":["models"],"test-paths":["tests"]},"other":{"profile":"healthcare_analytics"}},"project_change_panel":{"kind":"categorized","changes":[{"category":"vars","label":"default_state_grid_density","old":"weekly","new":"monthly","vars":{"entries":[{"name":"default_state_grid_density","old":"weekly","new":"monthly","config":["model.healthcare_analytics.mart_date_state_grid"]}],"footprint":{"models_scanned":35,"macros_scanned":89,"python_models":0}}},{"category":"vars","label":"dq_quarantine_threshold","old":10,"new":5,"vars":{"entries":[{"name":"dq_quarantine_threshold","old":10,"new":5,"direct":["model.healthcare_analytics.mart_dq_summary"],"via_macros":[{"model":"model.healthcare_analytics.stg_synthea__encounters","via":"macro.healthcare_analytics.add_dq_flags"},{"model":"model.healthcare_analytics.stg_synthea__medications","via":"macro.healthcare_analytics.add_dq_flags"}],"insulated_tests":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]}],"footprint":{"models_scanned":35,"macros_scanned":89,"python_models":0}}},{"category":"config_tree","label":"models.healthcare_analytics.marts: +materialized","old":"view","new":"table","tree":{"section":"models","segments":["healthcare_analytics","marts"],"key":"materialized"}},{"category":"dispatch","label":"dispatch","old":[{"macro_namespace":"dbt_utils","search_order":["dbt_utils","healthcare_analytics"]}],"new":[{"macro_namespace":"dbt_utils","search_order":["healthcare_analytics","dbt_utils"]}]},{"category":"hooks","label":"on-run-start","old":["grant usage on schema analytics to role reporting"],"new":["grant select on all tables in schema analytics to role reporting"],"hook":{"sql_diff":{"lines":[{"kind":"removed","text":"grant usage on schema analytics to role reporting","emphasis":[6,13]},{"kind":"added","text":"grant select on all tables in schema analytics to role reporting","emphasis":[6,28]}]},"operation_ids":["operation.healthcare_analytics.healthcare_analytics-on-run-start-0"],"manifest":"matched"}}]},"governance":{"group_chips":[{"group":"clinical_quality","owner_name":"Clinical Quality Team","owner_email":"clinical-quality@example.com"}],"blast_radius":[{"exposure_label":"provider_quality_dashboard","exposure_type":"dashboard","owner_name":"Clinical Quality Team","owner_email":"clinical-quality@example.com","in_scope_model_count":7}],"lifecycle_chips":[{"kind":"group-owner-touch","label":"Touches group clinical_quality (owner: clinical-quality@example.com)"}],"meta_tags":[{"model":"dim_conditions","tags":["analytics","dimension","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify the most prevalent conditions in the patient population\",\"name\":\"Top 10 most common conditions\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , patient_count\\n    , encounter_count\\nfrom main_marts.dim_conditions\\norder by patient_count desc\\nlimit 10\"},{\"description\":\"Find conditions that drive the most healthcare utilization\",\"name\":\"Conditions by encounter frequency\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , encounter_count\\n    , patient_count\\n    , round(encounter_count * 1.0 / patient_count, 2) as avg_encounters_per_patient\\nfrom main_marts.dim_conditions\\nwhere patient_count > 0\\norder by encounter_count desc\\nlimit 20\"},{\"description\":\"Find conditions first diagnosed in the last year\",\"name\":\"Recently diagnosed conditions\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , first_occurrence_date\\n    , last_occurrence_date\\n    , patient_count\\nfrom main_marts.dim_conditions\\nwhere first_occurrence_date >= current_date - interval '1 year'\\norder by first_occurrence_date desc\"}]"},{"key":"grain","value":"condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Condition lookup and filtering\",\"Cohort analysis by diagnosis\",\"Disease prevalence reporting\"]"}],"columns":[]},{"model":"dim_payers","tags":[],"meta":[],"columns":[{"column":"payer_key","tags":["dimension_key"],"meta":[{"key":"owner","value":"clinical-quality"},{"key":"pii","value":"false"}]}]},{"model":"fct_condition_cohorts","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Find conditions with the most patients for population health programs\",\"name\":\"Largest condition cohorts\",\"sql\":\"select\\n    fcc.condition_code\\n    , fcc.condition_description\\n    , count(distinct fcc.patient_id) as patient_count\\n    , sum(fcc.encounter_count) as total_encounters\\n    , round(avg(fcc.months_with_condition), 1) as avg_months_with_condition\\n    , sum(case when fcc.is_active then 1 else 0 end) as active_patients\\nfrom main_marts.fct_condition_cohorts fcc\\ngroup by 1, 2\\norder by patient_count desc\\nlimit 15\"},{\"description\":\"Identify patients with long-standing conditions for care management\",\"name\":\"Chronic condition patients (long duration)\",\"sql\":\"select\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , fcc.condition_description\\n    , fcc.first_diagnosis_date\\n    , fcc.months_with_condition\\n    , fcc.encounter_count\\n    , fcc.is_active\\nfrom main_marts.fct_condition_cohorts fcc\\njoin main_marts.dim_patients p\\n    on fcc.patient_key = p.patient_key\\nwhere fcc.months_with_condition >= 24\\n    and fcc.is_active = true\\norder by fcc.months_with_condition desc\\nlimit 50\"},{\"description\":\"Find patients with comorbidities for complex care programs\",\"name\":\"Patients with multiple conditions\",\"sql\":\"with patient_condition_counts as (\\n    select\\n        patient_id\\n        , patient_key\\n        , count(distinct condition_code) as condition_count\\n        , sum(case when is_active then 1 else 0 end) as active_condition_count\\n    from main_marts.fct_condition_cohorts\\n    group by 1, 2\\n)\\nselect\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , pcc.condition_count\\n    , pcc.active_condition_count\\nfrom patient_condition_counts pcc\\njoin main_marts.dim_patients p\\n    on pcc.patient_key = p.patient_key\\nwhere pcc.condition_count >= 3\\norder by pcc.condition_count desc\\nlimit 25\"}]"},{"key":"grain","value":"patient_id + condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Disease management programs\",\"Cohort identification\",\"Chronic condition tracking\",\"Care gap analysis\"]"}],"columns":[]},{"model":"fct_cost_analysis","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Analyze cost distribution across different insurance payers\",\"name\":\"Cost breakdown by payer\",\"sql\":\"select\\n    py.payer_name\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(sum(fca.payer_coverage), 2) as total_payer_coverage\\n    , round(sum(fca.patient_responsibility), 2) as total_patient_responsibility\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\njoin main_marts.dim_payers py\\n    on fca.payer_key = py.payer_key\\ngroup by py.payer_name\\norder by total_cost desc\"},{\"description\":\"Track cost trends over time for financial forecasting\",\"name\":\"Monthly cost trends\",\"sql\":\"select\\n    strftime(fca.encounter_date, '%Y-%m') as year_month\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(avg(fca.total_cost), 2) as avg_cost_per_encounter\\n    , round(sum(fca.payer_coverage), 2) as total_payer_coverage\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\nwhere fca.year_actual >= 2023\\ngroup by 1\\norder by 1 desc\"},{\"description\":\"Compare costs across different encounter classes\",\"name\":\"Cost by encounter type\",\"sql\":\"select\\n    fca.encounter_class\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(avg(fca.total_cost), 2) as avg_cost\\n    , round(avg(fca.procedure_count), 2) as avg_procedures\\n    , round(avg(fca.cost_per_procedure), 2) as avg_cost_per_procedure\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\ngroup by fca.encounter_class\\norder by total_cost desc\"},{\"description\":\"Identify individual high-cost encounters for review\",\"name\":\"High-cost encounters\",\"sql\":\"select\\n    fca.encounter_id\\n    , fca.encounter_date\\n    , fca.encounter_class\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , py.payer_name\\n    , fca.total_cost\\n    , fca.payer_coverage\\n    , fca.patient_responsibility\\n    , fca.procedure_count\\nfrom main_marts.fct_cost_analysis fca\\njoin main_marts.dim_patients p\\n    on fca.patient_key = p.patient_key\\njoin main_marts.dim_payers py\\n    on fca.payer_key = py.payer_key\\norder by fca.total_cost desc\\nlimit 25\"}]"},{"key":"grain","value":"encounter_id"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Financial reporting\",\"Payer mix analysis\",\"Cost allocation tracking\",\"Revenue cycle management\"]"}],"columns":[]},{"model":"fct_patient_summary","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify high-cost patients for care management programs\",\"name\":\"Top 10 costliest patients by year\",\"sql\":\"select\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , fps.year_actual\\n    , fps.total_cost\\n    , fps.encounter_count\\n    , fps.condition_count\\n    , fps.payer_coverage_pct\\nfrom main_marts.fct_patient_summary fps\\njoin main_marts.dim_patients p\\n    on fps.patient_key = p.patient_key\\nwhere fps.year_actual = 2024\\norder by fps.total_cost desc\\nlimit 10\"},{\"description\":\"Analyze patient population distribution and costs by age cohort\",\"name\":\"Patient count and cost by age group\",\"sql\":\"select\\n    case\\n        when fps.patient_age_at_year_end < 18 then '0-17'\\n        when fps.patient_age_at_year_end < 35 then '18-34'\\n        when fps.patient_age_at_year_end < 50 then '35-49'\\n        when fps.patient_age_at_year_end < 65 then '50-64'\\n        else '65+'\\n    end as age_group\\n    , count(distinct fps.patient_id) as patient_count\\n    , round(avg(fps.total_cost), 2) as avg_annual_cost\\n    , round(sum(fps.total_cost), 2) as total_cost\\n    , round(avg(fps.encounter_count), 2) as avg_encounters\\nfrom main_marts.fct_patient_summary fps\\nwhere fps.year_actual = 2024\\ngroup by 1\\norder by 1\"},{\"description\":\"Year-over-year comparison of costs and utilization metrics\",\"name\":\"Cost and utilization trends over time\",\"sql\":\"select\\n    fps.year_actual\\n    , count(distinct fps.patient_id) as patient_count\\n    , round(avg(fps.encounter_count), 2) as avg_encounters_per_patient\\n    , round(avg(fps.condition_count), 2) as avg_conditions_per_patient\\n    , round(avg(fps.total_cost), 2) as avg_annual_cost\\n    , round(sum(fps.total_cost), 2) as total_population_cost\\n    , round(avg(fps.payer_coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_patient_summary fps\\ngroup by fps.year_actual\\norder by fps.year_actual desc\"},{\"description\":\"Find patients with above-average encounters and conditions\",\"name\":\"High utilization patients\",\"sql\":\"with averages as (\\n    select\\n        avg(encounter_count) as avg_encounters\\n        , avg(condition_count) as avg_conditions\\n    from main_marts.fct_patient_summary\\n    where year_actual = 2024\\n)\\nselect\\n    fps.patient_id\\n    , fps.encounter_count\\n    , fps.condition_count\\n    , fps.total_cost\\n    , fps.unique_providers\\nfrom main_marts.fct_patient_summary fps\\ncross join averages a\\nwhere fps.year_actual = 2024\\n    and fps.encounter_count > a.avg_encounters * 2\\n    and fps.condition_count > a.avg_conditions\\norder by fps.total_cost desc\\nlimit 25\"}]"},{"key":"grain","value":"patient_id + year_actual"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Population health reporting\",\"Cost and utilization analysis\",\"Patient stratification\",\"Year-over-year trending\"]"}],"columns":[]},{"model":"fct_provider_metrics","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify highest-volume providers for capacity planning\",\"name\":\"Top providers by patient volume\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\norder by total_encounters desc\\nlimit 15\"},{\"description\":\"Track provider productivity over time\",\"name\":\"Provider metrics trend by month\",\"sql\":\"select\\n    fpm.year_month_str\\n    , count(distinct fpm.provider_id) as active_providers\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\\nfrom main_marts.fct_provider_metrics fpm\\nwhere fpm.year_actual >= 2023\\ngroup by fpm.year_month_str\\norder by fpm.year_month_str desc\"},{\"description\":\"Compare providers by cost and volume efficiency\",\"name\":\"Provider efficiency comparison\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , round(sum(fpm.total_cost), 2) as total_cost\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\\n    , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\nhaving sum(fpm.encounter_count) >= 10\\norder by avg_cost_per_encounter\"}]"},{"key":"grain","value":"provider_id + year_month_str"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider productivity reporting\",\"Workload balancing\",\"Cost efficiency analysis\",\"Capacity planning\"]"}],"columns":[]},{"model":"fct_provider_scorecard","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify highest-volume providers for capacity planning\",\"name\":\"Top providers by patient volume\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\norder by total_encounters desc\\nlimit 15\"},{\"description\":\"Track provider productivity over time\",\"name\":\"Provider metrics trend by month\",\"sql\":\"select\\n    fpm.year_month_str\\n    , count(distinct fpm.provider_id) as active_providers\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\\nfrom main_marts.fct_provider_metrics fpm\\nwhere fpm.year_actual >= 2023\\ngroup by fpm.year_month_str\\norder by fpm.year_month_str desc\"},{\"description\":\"Compare providers by cost and volume efficiency\",\"name\":\"Provider efficiency comparison\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , round(sum(fpm.total_cost), 2) as total_cost\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\\n    , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\nhaving sum(fpm.encounter_count) >= 10\\norder by avg_cost_per_encounter\"}]"},{"key":"grain","value":"provider_id + year_month_str"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider productivity reporting\",\"Workload balancing\",\"Cost efficiency analysis\",\"Capacity planning\"]"}],"columns":[]},{"model":"mart_date_state_grid","tags":["analytics","marts","reporting"],"meta":[{"key":"grain","value":"month_key x state"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Dense reporting grids\",\"Zero-activity gap detection\"]"}],"columns":[]},{"model":"mart_dq_summary","tags":["analytics","data_quality","marts"],"meta":[{"key":"grain","value":"entity_type"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Data quality dashboards\",\"Quarantine monitoring\",\"Data quality alerting\"]"}],"columns":[]},{"model":"v_encounter_summary","tags":["analytics","marts","view"],"meta":[],"columns":[]},{"model":"v_patient_current_conditions","tags":["analytics","marts","view"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Get all active conditions for a specific patient\",\"name\":\"Patient active conditions lookup\",\"sql\":\"select\\n    vpcc.patient_id\\n    , vpcc.condition_code\\n    , vpcc.condition_description\\n    , vpcc.first_diagnosis_date\\n    , vpcc.months_with_condition\\n    , vpcc.encounter_count\\nfrom main_marts.v_patient_current_conditions vpcc\\nwhere vpcc.patient_id = 'patient-uuid-here'\\norder by vpcc.first_diagnosis_date\"},{\"description\":\"Count of patients by active condition for disease registries\",\"name\":\"Active condition summary\",\"sql\":\"select\\n    vpcc.condition_code\\n    , vpcc.condition_description\\n    , count(distinct vpcc.patient_id) as active_patient_count\\n    , round(avg(vpcc.months_with_condition), 1) as avg_duration_months\\n    , round(avg(vpcc.encounter_count), 1) as avg_encounters\\nfrom main_marts.v_patient_current_conditions vpcc\\ngroup by 1, 2\\norder by active_patient_count desc\\nlimit 20\"}]"},{"key":"grain","value":"patient_id + condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Patient panel displays\",\"Active condition registries\",\"Care gap dashboards\"]"}],"columns":[]},{"model":"v_provider_active_patients","tags":["analytics","marts","view"],"meta":[{"key":"example_queries","value":"[{\"description\":\"View provider workload by active patient and condition counts\",\"name\":\"Provider panel sizes\",\"sql\":\"select\\n    vpap.provider_id\\n    , vpap.provider_name\\n    , vpap.active_patient_count\\n    , vpap.total_active_conditions\\n    , vpap.avg_conditions_per_patient\\nfrom main_marts.v_provider_active_patients vpap\\nwhere vpap.active_patient_count > 0\\norder by vpap.active_patient_count desc\\nlimit 25\"},{\"description\":\"Analyze distribution of patient panels across providers\",\"name\":\"Provider workload distribution\",\"sql\":\"select\\n    case\\n        when vpap.active_patient_count = 0 then '0'\\n        when vpap.active_patient_count \u003c= 10 then '1-10'\\n        when vpap.active_patient_count \u003c= 50 then '11-50'\\n        when vpap.active_patient_count \u003c= 100 then '51-100'\\n        else '100+'\\n    end as panel_size_bucket\\n    , count(*) as provider_count\\n    , sum(vpap.active_patient_count) as total_patients\\nfrom main_marts.v_provider_active_patients vpap\\ngroup by 1\\norder by 1\"}]"},{"key":"grain","value":"provider_id"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider panel management\",\"Workload balancing\",\"Capacity planning\"]"}],"columns":[]}]},"macro_lens":{"macros":[{"name":"quarantine_filter","package":"healthcare_analytics","path":"macros/data_quality/quarantine_filter.sql","diff":{"lines":[{"kind":"context","text":"{% macro quarantine_filter(enabled=true, field_name='is_dq_valid') %}","emphasis":null},{"kind":"context","text":"    {% if enabled %}","emphasis":null},{"kind":"removed","text":"    where {{ field_name }} is true","emphasis":[27,29]},{"kind":"added","text":"    where {{ field_name }} = true","emphasis":[27,28]},{"kind":"context","text":"    {% endif %}","emphasis":null},{"kind":"context","text":"{% endmacro %}","emphasis":null}]},"body_lines":[{"kind":"context","text":"{% macro quarantine_filter(enabled=true, field_name='is_dq_valid') %}","emphasis":null},{"kind":"context","text":"    {% if enabled %}","emphasis":null},{"kind":"context","text":"    where {{ field_name }} = true","emphasis":null},{"kind":"context","text":"    {% endif %}","emphasis":null},{"kind":"context","text":"{% endmacro %}","emphasis":null}],"impacted_count":3,"tree":[{"kind":"dir","label":"models","depth":0,"model_id":""},{"kind":"dir","label":"intermediate","depth":1,"model_id":""},{"kind":"dir","label":"healthcare","depth":2,"model_id":""},{"kind":"model","label":"int_encounters__enriched","depth":3,"model_id":"model.healthcare_analytics.int_encounters__enriched"},{"kind":"dir","label":"marts","depth":1,"model_id":""},{"kind":"dir","label":"core","depth":2,"model_id":""},{"kind":"model","label":"fct_clinical_events","depth":3,"model_id":"model.healthcare_analytics.fct_clinical_events"},{"kind":"model","label":"fct_encounters","depth":3,"model_id":"model.healthcare_analytics.fct_encounters"}],"impacted_models":[{"model_id":"model.healthcare_analytics.fct_clinical_events","name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","sql_lines":[{"kind":"context","text":"{{","emphasis":null},{"kind":"context","text":"    config(","emphasis":null},{"kind":"context","text":"        materialized='table',","emphasis":null},{"kind":"context","text":"        unique_key='clinical_event_key'","emphasis":null},{"kind":"context","text":"    )","emphasis":null},{"kind":"context","text":"}}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"with valid_encounters as (","emphasis":null},{"kind":"context","text":"    select encounter_id","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__encounters') }}","emphasis":null},{"kind":"context","text":"    {{ quarantine_filter() }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"conditions as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        condition_id as event_id","emphasis":null},{"kind":"context","text":"        , 'CONDITION' as event_type","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , cond.encounter_id","emphasis":null},{"kind":"context","text":"        , condition_start_date as event_start_date","emphasis":null},{"kind":"context","text":"        , condition_end_date as event_end_date","emphasis":null},{"kind":"context","text":"        , condition_code as code","emphasis":null},{"kind":"context","text":"        , 'SNOMED-CT' as code_system","emphasis":null},{"kind":"context","text":"        , condition_description as description","emphasis":null},{"kind":"context","text":"        , cast(null as varchar) as reason_code","emphasis":null},{"kind":"context","text":"        , cast(null as varchar) as reason_description","emphasis":null},{"kind":"context","text":"        , cast(null as decimal(18, 2)) as event_cost","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__conditions') }} as 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diff --git a/examples/explore-macro/tests.html b/examples/explore-macro/tests.html
index e290d07..ae47c55 100644
--- a/examples/explore-macro/tests.html
+++ b/examples/explore-macro/tests.html
@@ -1147,7 +1147,7 @@ <h2>v_provider_active_patients</h2>
 
 
 
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condition_description\n)","conditions_source":"conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n)","deduped":"deduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n)","final":"final as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n)","unknown_member":"unknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n)"},"code_map":{"compiled":"\n\n\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , 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when record was loaded","documented":true},"condition_code":{"description":"ICD/SNOMED code (natural key)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_code.193c903fa2"},{"kind":"unique","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_code.99cd9f3457"}],"documented":true},"condition_description":{"description":"Full condition name/description","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_description","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_description.dc79ef5b8c"}],"documented":true},"condition_key":{"description":"Surrogate key 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dataset","documented":true},"last_occurrence_date":{"description":"Most recent date this condition was diagnosed","documented":true},"patient_count":{"description":"Number of unique patients with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final 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This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}]},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":409,"col":1,"byte":12325},"end":{"line":409,"col":20,"byte":12344}},"date_spine":{"start":{"line":5,"col":6,"byte":9},"end":{"line":333,"col":2,"byte":8986}},"final":{"start":{"line":335,"col":1,"byte":8989},"end":{"line":407,"col":2,"byte":12323}}},"column_spans":{"(final 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Valid range is years 0001-2147483\n    (integer max 2,147,483,647). Safe for all practical date ranges.\n\n    Note on day_of_week: dbt_date uses US convention (Sunday=1, Saturday=7),\n    NOT ISO 8601 (Monday=1). Weekend and holiday logic accounts for this.\n#}\n\nwith date_spine as (\n    {{ dbt_date.get_date_dimension('1909-01-01', '2025-12-31') }}\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_date","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_date_date_key.0869fd48f9"]},"evidence":[{"label":"unique_key","value":"date_key"}],"pin_node":"(final select)"}]},{"name":"dim_organizations","path":"models/marts/core/dim_organizations.sql","description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_organizations.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":61,"col":1,"byte":1382},"end":{"line":61,"col":20,"byte":1401}},"final":{"start":{"line":55,"col":1,"byte":1293},"end":{"line":59,"col":2,"byte":1380}},"sequenced":{"start":{"line":7,"col":1,"byte":94},"end":{"line":35,"col":2,"byte":648}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":91}},"unknown_member":{"start":{"line":38,"col":1,"byte":708},"end":{"line":53,"col":2,"byte":1290}}},"column_spans":{"(final select)\u001f*":{"start":{"line":61,"col":8,"byte":1389},"end":{"line":61,"col":9,"byte":1390}},"sequenced\u001f_loaded_at":{"start":{"line":32,"col":11,"byte":598},"end":{"line":32,"col":42,"byte":629}},"sequenced\u001faddress":{"start":{"line":17,"col":11,"byte":373},"end":{"line":17,"col":18,"byte":380}},"sequenced\u001fcity":{"start":{"line":18,"col":11,"byte":391},"end":{"line":18,"col":15,"byte":395}},"sequenced\u001flatitude":{"start":{"line":24,"col":11,"byte":478},"end":{"line":24,"col":19,"byte":486}},"sequenced\u001flongitude":{"start":{"line":25,"col":11,"byte":497},"end":{"line":25,"col":20,"byte":506}},"sequenced\u001forganization_id":{"start":{"line":13,"col":11,"byte":296},"end":{"line":13,"col":26,"byte":311}},"sequenced\u001forganization_key":{"start":{"line":10,"col":9,"byte":197},"end":{"line":10,"col":73,"byte":261}},"sequenced\u001forganization_name":{"start":{"line":16,"col":11,"byte":345},"end":{"line":16,"col":28,"byte":362}},"sequenced\u001fphone":{"start":{"line":21,"col":11,"byte":441},"end":{"line":21,"col":16,"byte":446}},"sequenced\u001frevenue":{"start":{"line":28,"col":11,"byte":537},"end":{"line":28,"col":18,"byte":544}},"sequenced\u001fstate":{"start":{"line":19,"col":11,"byte":406},"end":{"line":19,"col":16,"byte":411}},"sequenced\u001futilization":{"start":{"line":29,"col":11,"byte":555},"end":{"line":29,"col":22,"byte":566}},"sequenced\u001fzip_code":{"start":{"line":20,"col":11,"byte":422},"end":{"line":20,"col":19,"byte":430}},"source\u001f*":{"start":{"line":4,"col":12,"byte":30},"end":{"line":4,"col":13,"byte":31}},"unknown_member\u001f_loaded_at":{"start":{"line":52,"col":11,"byte":1257},"end":{"line":52,"col":42,"byte":1288}},"unknown_member\u001faddress":{"start":{"line":43,"col":16,"byte":878},"end":{"line":43,"col":43,"byte":905}},"unknown_member\u001fcity":{"start":{"line":44,"col":16,"byte":921},"end":{"line":44,"col":40,"byte":945}},"unknown_member\u001flatitude":{"start":{"line":48,"col":16,"byte":1087},"end":{"line":48,"col":43,"byte":1114}},"unknown_member\u001flongitude":{"start":{"line":49,"col":16,"byte":1130},"end":{"line":49,"col":44,"byte":1158}},"unknown_member\u001forganization_id":{"start":{"line":41,"col":11,"byte":780},"end":{"line":41,"col":39,"byte":808}},"unknown_member\u001forganization_key":{"start":{"line":40,"col":10,"byte":748},"end":{"line":40,"col":31,"byte":769}},"unknown_member\u001forganization_name":{"start":{"line":42,"col":11,"byte":819},"end":{"line":42,"col":54,"byte":862}},"unknown_member\u001fphone":{"start":{"line":47,"col":16,"byte":1046},"end":{"line":47,"col":41,"byte":1071}},"unknown_member\u001frevenue":{"start":{"line":50,"col":16,"byte":1174},"end":{"line":50,"col":42,"byte":1200}},"unknown_member\u001fstate":{"start":{"line":45,"col":16,"byte":961},"end":{"line":45,"col":41,"byte":986}},"unknown_member\u001futilization":{"start":{"line":51,"col":16,"byte":1216},"end":{"line":51,"col":46,"byte":1246}},"unknown_member\u001fzip_code":{"start":{"line":46,"col":16,"byte":1002},"end":{"line":46,"col":44,"byte":1030}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"city":{"description":"City","documented":true},"latitude":{"description":"Latitude coordinate","documented":true},"longitude":{"description":"Longitude coordinate","documented":true},"organization_id":{"description":"Natural organization identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_id.8ca9bc4237"}],"documented":true},"organization_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_key.a539e5f42a"},{"kind":"unique","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"}],"documented":true},"organization_name":{"description":"Name of the healthcare organization","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_name","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_name.7fb64e1577"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"revenue","min_value":0,"model":"{{ get_where_subquery(ref('dim_organizations')) }}","row_condition":"revenue is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_organizations_revenue__0__revenue_is_not_null.3e63617295"}],"documented":true},"state":{"description":"State (full state name)","documented":true},"utilization":{"description":"Total utilization count","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"utilization","min_value":0,"model":"{{ get_where_subquery(ref('dim_organizations')) }}","row_condition":"utilization is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_organizations_utilization__0__utilization_is_not_null.2e3baf0d0c"}],"documented":true},"zip_code":{"description":"ZIP code","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__organizations"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"organization_id"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"organization_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"organization_name"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"organization_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"address"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"address"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"zip_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"zip_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"phone"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"phone"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"latitude"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"latitude"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"longitude"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"longitude"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"revenue"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"revenue"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"utilization"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"utilization"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='organization_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"]},"evidence":[{"label":"unique_key","value":"organization_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__organizations')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}"]}]},{"name":"dim_patients","path":"models/marts/core/dim_patients.sql","description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","dag":{"nodes":[{"id":"snapshot_source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_patients.sql","role":"final"}],"edges":[{"from":"snapshot_source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\n-- depends_on: \"memory\".\"snapshots\".\"snp_patients\"\n-- depends_on: \"memory\".\"main_staging\".\"stg_synthea__patients\"\n\n\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final 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Each row represents a\n    specific version of a patient's record.\n\n    SCD Type 2 Columns:\n    - patient_key: Surrogate key (unique per version)\n    - patient_id: Natural key (same across versions)\n    - valid_from: When this version became active\n    - valid_to: When this version was superseded (null = current)\n    - is_current: True for the active version\n\n    To query current patients only:\n        WHERE is_current = true\n\n    To query patient state at a point in time:\n        WHERE valid_from <= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_patients","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised 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This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}]},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as 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bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total 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get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme 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Health"}},{"display":"555-0100","key":{"t":"str","v":"555-0100"}},{"display":"1250.0","key":{"t":"number","v":"1250"}},{"display":"MA","key":{"t":"str","v":"MA"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"8","key":{"t":"number","v":"8"}},{"display":"02101","key":{"t":"str","v":"02101"}}]}]},"rows":[{"address":"1 Acme Way","amount_covered":1000.0,"amount_uncovered":250.0,"average_quality_of_life_score":0.85,"city":"Boston","covered_encounters":10,"covered_immunizations":4,"covered_medications":5,"covered_procedures":3,"member_months":96,"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_name":"Acme Health","phone":"555-0100","revenue":1250.0,"state":"MA","uncovered_encounters":2,"uncovered_immunizations":0,"uncovered_medications":1,"uncovered_procedures":0,"unique_customers":8,"zip_code":"02101"}],"format":"dict","column_meta":{"address":{"description":"Street address"},"amount_covered":{"description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"amount_uncovered":{"description":"Total amount not covered by payer"},"average_quality_of_life_score":{"description":"Average quality of life score for members"},"city":{"description":"City"},"covered_encounters":{"description":"Number of encounters covered"},"covered_immunizations":{"description":"Number of immunizations covered"},"covered_medications":{"description":"Number of medications covered"},"covered_procedures":{"description":"Number of procedures covered"},"member_months":{"description":"Total member months"},"payer_id":{"description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]},"phone":{"description":"Phone number"},"revenue":{"description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"state":{"description":"State where headquartered"},"uncovered_encounters":{"description":"Number of encounters not covered"},"uncovered_immunizations":{"description":"Number of immunizations not covered"},"uncovered_medications":{"description":"Number of medications not covered"},"uncovered_procedures":{"description":"Number of procedures not covered"},"unique_customers":{"description":"Number of unique customers"},"zip_code":{"description":"ZIP code (stored as numeric, 5 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\"memory\".\"main_marts\".\"dim_organizations\"\n)","providers":"providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n)","unknown_member":"unknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='provider_key'\n    )\n}}\n\nwith providers as (\n    select * from {{ ref('stg_synthea__providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from {{ ref('dim_organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_providers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"]},"evidence":[{"label":"unique_key","value":"provider_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_providers","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('dim_organizations'), ref('stg_synthea__providers')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. 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cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_encounter_id.5c743b1ef7"}],"documented":true},"encounter_key":{"description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_encounter_key.cc0e40eeb8"},{"kind":"relationships","kwargs":{"column_name":"encounter_key","field":"encounter_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","to":"ref('fct_encounters')"},"node_id":"test.healthcare_analytics.relationships_fct_clinical_events_encounter_key__encounter_key__ref_fct_encounters_.7a30909f42"}],"documented":true},"event_cost":{"description":"Cost of the clinical event","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"event_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","row_condition":"event_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_clinical_events_event_cost__0__event_cost_is_not_null.2c2582a25b"}],"documented":true},"event_date_key":{"description":"Foreign key to dim_date for event start","tests":[{"kind":"not_null","kwargs":{"column_name":"event_date_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_event_date_key.d605213965"},{"kind":"relationships","kwargs":{"column_name":"event_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_clinical_events_event_date_key__date_key__ref_dim_date_.0995b8993f"}],"documented":true},"event_end_date":{"description":"End date of the clinical event (null if ongoing or 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unique_key='clinical_event_key'\n    )\n}}\n\nwith valid_encounters as (\n    select encounter_id\n    from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from {{ ref('stg_synthea__conditions') }} as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from {{ ref('stg_synthea__medications') }} as med\n    {{ quarantine_filter() }}\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from {{ ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as 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mod","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_condition_cohorts","path":"models/marts/analytics/fct_condition_cohorts.sql","description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"patients","role":"import"},{"id":"dim_conditions","role":"import"},{"id":"condition_windows","role":"transform"},{"id":"with_dimensions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final 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end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n)","conditions_source":"conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n)","dim_conditions":"dim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","with_dimensions":"with_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n)"},"code_map":{"compiled":"\n\n\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","node_spans":{"(final 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when record was loaded","documented":true},"condition_code":{"description":"ICD/SNOMED condition code","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_condition_cohorts_condition_code.180142995a"}],"documented":true},"condition_cohort_key":{"description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_cohort_key","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_condition_cohorts_condition_cohort_key.1edb245ae1"},{"kind":"unique","kwargs":{"column_name":"condition_cohort_key","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"}],"documented":true},"condition_description":{"description":"Full condition 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unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}]},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as 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encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","node_spans":{"(final 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when record was loaded","documented":true},"cost_analysis_key":{"description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"kind":"not_null","kwargs":{"column_name":"cost_analysis_key","model":"{{ get_where_subquery(ref('fct_cost_analysis')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_cost_analysis_cost_analysis_key.a72ad66418"},{"kind":"unique","kwargs":{"column_name":"cost_analysis_key","model":"{{ get_where_subquery(ref('fct_cost_analysis')) }}"},"node_id":"test.healthcare_analytics.unique_fct_cost_analysis_cost_analysis_key.2aecf62159"}],"documented":true},"cost_per_procedure":{"description":"Average cost per procedure (null if no procedures)","documented":true},"coverage_pct":{"description":"Percentage covered by insurance (0-100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('fct_cost_analysis')) 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unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end 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This finding's evidence carries a copy-pasteable given sketch.","pin_node":"encounters_enriched","sketches":["- input: ref('fct_encounters')\n  rows:\n    - {encounter_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__procedures')\n  rows:\n    - {encounter_id: 1}\n# expect: the no-match row with NULL stg_synthea__procedures columns (or the intended fallback)"]}]},{"name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. 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timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final select)\u001f*":{"start":{"line":109,"col":8,"byte":2736},"end":{"line":109,"col":9,"byte":2737}},"dim_date\u001fdate_actual":{"start":{"line":45,"col":11,"byte":721},"end":{"line":45,"col":22,"byte":732}},"dim_date\u001fdate_key":{"start":{"line":44,"col":9,"byte":702},"end":{"line":44,"col":17,"byte":710}},"encounters\u001f*":{"start":{"line":4,"col":12,"byte":34},"end":{"line":4,"col":13,"byte":35}},"final\u001f_loaded_at":{"start":{"line":98,"col":11,"byte":2250},"end":{"line":98,"col":42,"byte":2281}},"final\u001fbase_encounter_cost":{"start":{"line":86,"col":11,"byte":1836},"end":{"line":86,"col":32,"byte":1857}},"final\u001fduration_minutes":{"start":{"line":83,"col":11,"byte":1726},"end":{"line":83,"col":92,"byte":1807}},"final\u001fencounter_class":{"start":{"line":72,"col":11,"byte":1470},"end":{"line":72,"col":28,"byte":1487}},"final\u001fencounter_code":{"start":{"line":73,"col":11,"byte":1498},"end":{"line":73,"col":27,"byte":1514}},"final\u001fencounter_description":{"start":{"line":74,"col":11,"byte":1525},"end":{"line":74,"col":34,"byte":1548}},"final\u001fencounter_end_at":{"start":{"line":80,"col":11,"byte":1668},"end":{"line":80,"col":29,"byte":1686}},"final\u001fencounter_end_date_key":{"start":{"line":69,"col":11,"byte":1385},"end":{"line":69,"col":62,"byte":1436}},"final\u001fencounter_id":{"start":{"line":55,"col":11,"byte":929},"end":{"line":55,"col":25,"byte":943}},"final\u001fencounter_key":{"start":{"line":52,"col":9,"byte":834},"end":{"line":52,"col":69,"byte":894}},"final\u001fencounter_start_at":{"start":{"line":79,"col":11,"byte":1637},"end":{"line":79,"col":31,"byte":1657}},"final\u001fencounter_start_date_key":{"start":{"line":68,"col":11,"byte":1321},"end":{"line":68,"col":64,"byte":1374}},"final\u001forganization_id":{"start":{"line":63,"col":11,"byte":1200},"end":{"line":63,"col":28,"byte":1217}},"final\u001forganization_key":{"start":{"line":62,"col":11,"byte":1137},"end":{"line":62,"col":63,"byte":1189}},"final\u001fpatient_age_at_encounter":{"start":{"line":92,"col":11,"byte":2047},"end":{"line":95,"col":40,"byte":2218}},"final\u001fpatient_id":{"start":{"line":59,"col":11,"byte":1034},"end":{"line":59,"col":23,"byte":1046}},"final\u001fpatient_key":{"start":{"line":58,"col":11,"byte":981},"end":{"line":58,"col":53,"byte":1023}},"final\u001fpatient_responsibility":{"start":{"line":89,"col":11,"byte":1924},"end":{"line":89,"col":87,"byte":2000}},"final\u001fpayer_coverage":{"start":{"line":88,"col":11,"byte":1897},"end":{"line":88,"col":27,"byte":1913}},"final\u001fpayer_id":{"start":{"line":65,"col":11,"byte":1278},"end":{"line":65,"col":21,"byte":1288}},"final\u001fpayer_key":{"start":{"line":64,"col":11,"byte":1228},"end":{"line":64,"col":50,"byte":1267}},"final\u001fprovider_id":{"start":{"line":61,"col":11,"byte":1113},"end":{"line":61,"col":24,"byte":1126}},"final\u001fprovider_key":{"start":{"line":60,"col":11,"byte":1057},"end":{"line":60,"col":56,"byte":1102}},"final\u001freason_code":{"start":{"line":75,"col":11,"byte":1559},"end":{"line":75,"col":24,"byte":1572}},"final\u001freason_description":{"start":{"line":76,"col":11,"byte":1583},"end":{"line":76,"col":31,"byte":1603}},"final\u001ftotal_claim_cost":{"start":{"line":87,"col":11,"byte":1868},"end":{"line":87,"col":29,"byte":1886}},"organizations\u001forganization_id":{"start":{"line":31,"col":11,"byte":490},"end":{"line":31,"col":26,"byte":505}},"organizations\u001forganization_key":{"start":{"line":30,"col":9,"byte":463},"end":{"line":30,"col":25,"byte":479}},"patients\u001fbirth_date":{"start":{"line":16,"col":11,"byte":216},"end":{"line":16,"col":21,"byte":226}},"patients\u001fpatient_id":{"start":{"line":15,"col":11,"byte":195},"end":{"line":15,"col":21,"byte":205}},"patients\u001fpatient_key":{"start":{"line":14,"col":9,"byte":173},"end":{"line":14,"col":20,"byte":184}},"payers\u001fpayer_id":{"start":{"line":38,"col":11,"byte":612},"end":{"line":38,"col":19,"byte":620}},"payers\u001fpayer_key":{"start":{"line":37,"col":9,"byte":592},"end":{"line":37,"col":18,"byte":601}},"providers\u001fprovider_id":{"start":{"line":24,"col":11,"byte":362},"end":{"line":24,"col":22,"byte":373}},"providers\u001fprovider_key":{"start":{"line":23,"col":9,"byte":339},"end":{"line":23,"col":21,"byte":351}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"base_encounter_cost":{"description":"Base cost of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"base_encounter_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"base_encounter_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_base_encounter_cost__0__base_encounter_cost_is_not_null.a096f214be"}],"documented":true},"duration_minutes":{"description":"Duration of encounter in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_duration_minutes__0__duration_minutes_is_not_null.ce51123f28"}],"documented":true},"encounter_class":{"description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.59f26de6a5"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_class.9419876296"}],"documented":true},"encounter_code":{"description":"SNOMED CT code for the encounter type","documented":true},"encounter_description":{"description":"Description of the encounter type","documented":true},"encounter_end_at":{"description":"End timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_end_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"encounter_end_at is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_end_at__current_timestamp___1900_01_01_timestamp__encounter_end_at_is_not_null.393dec12f4"}],"documented":true},"encounter_end_date_key":{"description":"Foreign key to dim_date for encounter end","documented":true},"encounter_id":{"description":"Natural encounter identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_id.f7111b4eea"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_id.ce88bae928"}],"documented":true},"encounter_key":{"description":"Surrogate key for the encounter","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_key.4fa2f03d5e"},{"kind":"unique","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"}],"documented":true},"encounter_start_at":{"description":"Start timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_start_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_start_at__current_timestamp___1900_01_01_timestamp.f32b839e27"},{"kind":"not_null","kwargs":{"column_name":"encounter_start_at","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_at.c47cc214ac"}],"documented":true},"encounter_start_date_key":{"description":"Foreign key to dim_date for encounter start","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_start_date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_date_key.73ce57ba43"},{"kind":"relationships","kwargs":{"column_name":"encounter_start_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_encounter_start_date_key__date_key__ref_dim_date_.4f1d88383c"}],"documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_id.acfb633ecb"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_key.815fa1363c"},{"kind":"relationships","kwargs":{"column_name":"organization_key","field":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_organizations')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_organization_key__organization_key__ref_dim_organizations_.5feb1b3ad6"}],"documented":true},"patient_age_at_encounter":{"description":"Patient's age at the time of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_age_at_encounter","max_value":150,"min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_age_at_encounter is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_age_at_encounter__150__0__patient_age_at_encounter_is_not_null.bbd237256e"}],"documented":true},"patient_id":{"description":"Natural patient identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_id.9e325e7ac6"}],"documented":true},"patient_key":{"description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_key.e5abbfbe29"},{"kind":"relationships","kwargs":{"column_name":"patient_key","field":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_patients')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_patient_key__patient_key__ref_dim_patients_.ad1c292e28"}],"documented":true},"patient_responsibility":{"description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_responsibility","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_responsibility is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_responsibility__0__patient_responsibility_is_not_null.7a61f00fd0"}],"documented":true},"payer_coverage":{"description":"Amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"payer_coverage","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"payer_coverage is not 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This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"fct_encounters_monthly","path":"models/marts/core/fct_encounters_monthly.sql","description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters_monthly.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"encounters_with_date","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":53,"col":1,"byte":1374},"end":{"line":53,"col":20,"byte":1393}},"dim_date":{"start":{"line":16,"col":1,"byte":301},"end":{"line":22,"col":2,"byte":431}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":298}},"encounters_with_date":{"start":{"line":24,"col":1,"byte":434},"end":{"line":34,"col":2,"byte":790}},"final":{"start":{"line":36,"col":1,"byte":793},"end":{"line":51,"col":2,"byte":1372}}},"column_spans":{"(final select)\u001f*":{"start":{"line":53,"col":8,"byte":1381},"end":{"line":53,"col":9,"byte":1382}},"dim_date\u001fdate_key":{"start":{"line":18,"col":9,"byte":334},"end":{"line":18,"col":17,"byte":342}},"dim_date\u001fmonth_actual":{"start":{"line":20,"col":11,"byte":375},"end":{"line":20,"col":23,"byte":387}},"dim_date\u001fyear_actual":{"start":{"line":19,"col":11,"byte":353},"end":{"line":19,"col":22,"byte":364}},"encounters\u001fduration_minutes":{"start":{"line":12,"col":11,"byte":232},"end":{"line":12,"col":27,"byte":248}},"encounters\u001fencounter_class":{"start":{"line":8,"col":11,"byte":121},"end":{"line":8,"col":26,"byte":136}},"encounters\u001fencounter_id":{"start":{"line":5,"col":9,"byte":42},"end":{"line":5,"col":21,"byte":54}},"encounters\u001fencounter_start_date_key":{"start":{"line":7,"col":11,"byte":86},"end":{"line":7,"col":35,"byte":110}},"encounters\u001fpatient_id":{"start":{"line":6,"col":11,"byte":65},"end":{"line":6,"col":21,"byte":75}},"encounters\u001fpatient_responsibility":{"start":{"line":11,"col":11,"byte":199},"end":{"line":11,"col":33,"byte":221}},"encounters\u001fpayer_coverage":{"start":{"line":10,"col":11,"byte":174},"end":{"line":10,"col":25,"byte":188}},"encounters\u001ftotal_claim_cost":{"start":{"line":9,"col":11,"byte":147},"end":{"line":9,"col":27,"byte":163}},"encounters_with_date\u001f*":{"start":{"line":26,"col":9,"byte":479},"end":{"line":26,"col":12,"byte":482}},"encounters_with_date\u001fmonth_actual":{"start":{"line":28,"col":11,"byte":517},"end":{"line":28,"col":25,"byte":531}},"encounters_with_date\u001fyear_actual":{"start":{"line":27,"col":11,"byte":493},"end":{"line":27,"col":24,"byte":506}},"encounters_with_date\u001fyear_month":{"start":{"line":29,"col":16,"byte":547},"end":{"line":30,"col":72,"byte":654}},"final\u001f_loaded_at":{"start":{"line":48,"col":11,"byte":1241},"end":{"line":48,"col":42,"byte":1272}},"final\u001favg_duration_minutes":{"start":{"line":47,"col":11,"byte":1185},"end":{"line":47,"col":56,"byte":1230}},"final\u001fencounter_class":{"start":{"line":41,"col":11,"byte":889},"end":{"line":41,"col":26,"byte":904}},"final\u001fencounter_count":{"start":{"line":42,"col":11,"byte":915},"end":{"line":42,"col":38,"byte":942}},"final\u001fmonth_actual":{"start":{"line":40,"col":11,"byte":866},"end":{"line":40,"col":23,"byte":878}},"final\u001ftotal_claim_cost":{"start":{"line":44,"col":11,"byte":1009},"end":{"line":44,"col":52,"byte":1050}},"final\u001ftotal_patient_responsibility":{"start":{"line":46,"col":11,"byte":1115},"end":{"line":46,"col":70,"byte":1174}},"final\u001ftotal_payer_coverage":{"start":{"line":45,"col":11,"byte":1061},"end":{"line":45,"col":54,"byte":1104}},"final\u001funique_patients":{"start":{"line":43,"col":11,"byte":953},"end":{"line":43,"col":56,"byte":998}},"final\u001fyear_actual":{"start":{"line":39,"col":11,"byte":844},"end":{"line":39,"col":22,"byte":855}},"final\u001fyear_month":{"start":{"line":38,"col":9,"byte":823},"end":{"line":38,"col":19,"byte":833}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"avg_duration_minutes":{"description":"Average encounter duration in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"avg_duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"avg_duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_avg_duration_minutes__0__avg_duration_minutes_is_not_null.0cea70c94f"}],"documented":true},"encounter_class":{"description":"Type of encounter","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_monthly_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.274b5b6532"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_encounter_class.d503677b5a"}],"documented":true},"encounter_count":{"description":"Number of encounters in the month","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_encounter_count__1.03132101e0"},{"kind":"not_null","kwargs":{"column_name":"encounter_count","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_encounter_count.5046b087e4"}],"documented":true},"month_actual":{"description":"Month number (1-12)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"month_actual","max_value":12,"min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_month_actual__12__1.f650fcabb6"},{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_month_actual.82083ad465"}],"documented":true},"total_claim_cost":{"description":"Sum of total claim costs","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_claim_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_claim_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_claim_cost__0__total_claim_cost_is_not_null.09195ba1c6"}],"documented":true},"total_patient_responsibility":{"description":"Sum of patient responsibility amounts","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_patient_responsibility","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_patient_responsibility is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_patient_responsibility__0__total_patient_responsibility_is_not_null.e824258649"}],"documented":true},"total_payer_coverage":{"description":"Sum of payer coverage amounts","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_payer_coverage","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_payer_coverage is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_payer_coverage__0__total_payer_coverage_is_not_null.1cae11e2af"}],"documented":true},"unique_patients":{"description":"Number of unique patients with encounters","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"unique_patients","min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_unique_patients__1.86f77befe3"},{"kind":"not_null","kwargs":{"column_name":"unique_patients","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) 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   where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , cast(count(*) as decimal(18, 2)) / nullif(count(distinct patient_id), 0) as encounters_per_patient\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_actual, encounter_class\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_yearly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_actual, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"}]},{"name":"fct_patient_summary","path":"models/marts/analytics/fct_patient_summary.sql","description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health 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\"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","conditions_per_patient_year":"conditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from 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e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct 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 select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","node_spans":{"(final 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select)\u001f*":{"start":{"line":143,"col":8,"byte":3621},"end":{"line":143,"col":9,"byte":3622}},"dim_date\u001fdate_key":{"start":{"line":26,"col":9,"byte":485},"end":{"line":26,"col":17,"byte":493}},"dim_date\u001fmonth_actual":{"start":{"line":28,"col":11,"byte":526},"end":{"line":28,"col":23,"byte":538}},"dim_date\u001fyear_actual":{"start":{"line":27,"col":11,"byte":504},"end":{"line":27,"col":22,"byte":515}},"encounters\u001fduration_minutes":{"start":{"line":18,"col":11,"byte":319},"end":{"line":18,"col":27,"byte":335}},"encounters\u001fencounter_id":{"start":{"line":8,"col":11,"byte":68},"end":{"line":8,"col":23,"byte":80}},"encounters\u001fencounter_key":{"start":{"line":7,"col":9,"byte":44},"end":{"line":7,"col":22,"byte":57}},"encounters\u001fencounter_start_date_key":{"start":{"line":15,"col":11,"byte":232},"end":{"line":15,"col":35,"byte":256}},"encounters\u001forganization_id":{"start":{"line":14,"col":11,"byte":206},"end":{"line":14,"col":26,"byte":221}},"encounters\u001forganization_key":{"start":{"line":13,"col":11,"byte":179},"end":{"line":13,"col":27,"byte":195}},"encounters\u001fpatient_age_at_encounter":{"start":{"line":19,"col":11,"byte":346},"end":{"line":19,"col":35,"byte":370}},"encounters\u001fpatient_id":{"start":{"line":10,"col":11,"byte":113},"end":{"line":10,"col":21,"byte":123}},"encounters\u001fpatient_key":{"start":{"line":9,"col":11,"byte":91},"end":{"line":9,"col":22,"byte":102}},"encounters\u001fpayer_coverage":{"start":{"line":17,"col":11,"byte":294},"end":{"line":17,"col":25,"byte":308}},"encounters\u001fprovider_id":{"start":{"line":12,"col":11,"byte":157},"end":{"line":12,"col":22,"byte":168}},"encounters\u001fprovider_key":{"start":{"line":11,"col":11,"byte":134},"end":{"line":11,"col":23,"byte":146}},"encounters\u001ftotal_claim_cost":{"start":{"line":16,"col":11,"byte":267},"end":{"line":16,"col":27,"byte":283}},"encounters_with_date\u001f*":{"start":{"line":43,"col":9,"byte":860},"end":{"line":43,"col":12,"byte":863}},"encounters_with_date\u001fmonth_actual":{"start":{"line":45,"col":11,"byte":898},"end":{"line":45,"col":25,"byte":912}},"encounters_with_date\u001fprocedure_count":{"start":{"line":49,"col":11,"byte":1062},"end":{"line":49,"col":61,"byte":1112}},"encounters_with_date\u001fyear_actual":{"start":{"line":44,"col":11,"byte":874},"end":{"line":44,"col":24,"byte":887}},"encounters_with_date\u001fyear_month_str":{"start":{"line":46,"col":16,"byte":928},"end":{"line":48,"col":79,"byte":1051}},"final\u001f_loaded_at":{"start":{"line":138,"col":11,"byte":3550},"end":{"line":138,"col":42,"byte":3581}},"final\u001favg_cost_per_encounter":{"start":{"line":125,"col":11,"byte":3162},"end":{"line":129,"col":38,"byte":3316}},"final\u001favg_encounter_duration_minutes":{"start":{"line":118,"col":11,"byte":2896},"end":{"line":118,"col":85,"byte":2970}},"final\u001favg_patient_age":{"start":{"line":117,"col":11,"byte":2841},"end":{"line":117,"col":55,"byte":2885}},"final\u001fencounter_count":{"start":{"line":112,"col":11,"byte":2726},"end":{"line":112,"col":26,"byte":2741}},"final\u001fencounters_per_patient_avg":{"start":{"line":131,"col":11,"byte":3328},"end":{"line":135,"col":42,"byte":3518}},"final\u001fmonth_actual":{"start":{"line":108,"col":11,"byte":2649},"end":{"line":108,"col":23,"byte":2661}},"final\u001forganization_id":{"start":{"line":104,"col":11,"byte":2578},"end":{"line":104,"col":26,"byte":2593}},"final\u001forganization_key":{"start":{"line":103,"col":11,"byte":2551},"end":{"line":103,"col":27,"byte":2567}},"final\u001fprovider_id":{"start":{"line":102,"col":11,"byte":2529},"end":{"line":102,"col":22,"byte":2540}},"final\u001fprovider_key":{"start":{"line":101,"col":11,"byte":2506},"end":{"line":101,"col":23,"byte":2518}},"final\u001fprovider_metrics_key":{"start":{"line":98,"col":9,"byte":2386},"end":{"line":98,"col":91,"byte":2468}},"final\u001ftotal_cost":{"start":{"line":121,"col":11,"byte":3009},"end":{"line":121,"col":48,"byte":3046}},"final\u001ftotal_payer_coverage":{"start":{"line":122,"col":11,"byte":3057},"end":{"line":122,"col":68,"byte":3114}},"final\u001ftotal_procedures":{"start":{"line":114,"col":11,"byte":2783},"end":{"line":114,"col":27,"byte":2799}},"final\u001funique_patient_count":{"start":{"line":113,"col":11,"byte":2752},"end":{"line":113,"col":31,"byte":2772}},"final\u001fyear_actual":{"start":{"line":107,"col":11,"byte":2627},"end":{"line":107,"col":22,"byte":2638}},"final\u001fyear_month_str":{"start":{"line":109,"col":11,"byte":2672},"end":{"line":109,"col":25,"byte":2686}},"procedures\u001fencounter_id":{"start":{"line":34,"col":9,"byte":620},"end":{"line":34,"col":21,"byte":632}},"procedures\u001fprocedure_count":{"start":{"line":35,"col":11,"byte":643},"end":{"line":35,"col":38,"byte":670}},"provider_month_agg\u001favg_encounter_duration_minutes":{"start":{"line":75,"col":11,"byte":1828},"end":{"line":75,"col":68,"byte":1885}},"provider_month_agg\u001favg_patient_age":{"start":{"line":72,"col":11,"byte":1737},"end":{"l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   materialized='view'\n    )\n}}\n\nwith encounters as (\n    select\n        encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nconditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from {{ ref('stg_synthea__conditions') }}\n    group by encounter_id\n),\n\nmedications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from {{ ref('stg_synthea__medications') }}\n    {{ quarantine_filter() }}\n    group by encounter_id\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nfinal as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"int_patients__with_conditions","path":"models/intermediate/healthcare/int_patients__with_conditions.sql","description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","dag":{"nodes":[{"id":"patients","role":"import"},{"id":"conditions","role":"import"},{"id":"condition_stats","role":"transform"},{"id":"chronic_flags","role":"transform"},{"id":"chronic_count","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_patients__with_conditions.sql","role":"final"}],"edges":[{"from":"conditions","to":"condition_stats","edge_type":"from"},{"from":"conditions","to":"chronic_flags","edge_type":"from"},{"from":"chronic_flags","to":"chronic_count","edge_type":"from"},{"from":"patients","to":"final","edge_type":"from"},{"from":"condition_stats","to":"final","edge_type":"left"},{"from":"chronic_flags","to":"final","edge_type":"left"},{"from":"chronic_count","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","chronic_count":"chronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n)","chronic_flags":"chronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n)","condition_stats":"condition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n)","conditions":"conditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","final":"final as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)","patients":"patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)"},"code_map":{"compiled":"\n\n\n\nwith patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":80,"col":1,"byte":2459},"end":{"line":80,"col":20,"byte":2478}},"chronic_count":{"start":{"line":56,"col":1,"byte":1519},"end":{"line":61,"col":2,"byte":1681}},"chronic_flags":{"start":{"line":33,"col":1,"byte":705},"end":{"line":54,"col":2,"byte":1516}},"condition_stats":{"start":{"line":22,"col":1,"byte":357},"end":{"line":31,"col":2,"byte":702}},"conditions":{"start":{"line":12,"col":1,"byte":131},"end":{"line":20,"col":2,"byte":354}},"final":{"start":{"line":63,"col":1,"byte":1684},"end":{"line":78,"col":2,"byte":2457}},"patients":{"start":{"line":5,"col":6,"byte":9},"end":{"line":10,"col":2,"byte":128}}},"column_spans":{"(final select)\u001f*":{"start":{"line":80,"col":8,"byte":2466},"end":{"line":80,"col":9,"byte":2467}},"chronic_count\u001fchronic_condition_count":{"start":{"line":59,"col":11,"byte":1578},"end":{"line":59,"col":89,"byte":1656}},"chronic_count\u001fpatient_id":{"start":{"line":58,"col":9,"byte":1557},"end":{"line":58,"col":19,"byte":1567}},"chronic_flags\u001fhas_diabetes":{"start":{"line":36,"col":11,"byte":764},"end":{"line":39,"col":29,"byte":891}},"chronic_flags\u001fhas_heart_disease":{"start":{"line":45,"col":11,"byte":1125},"end":{"line":51,"col":34,"byte":1470}},"chronic_flags\u001fhas_hypertension":{"start":{"line":40,"col":11,"byte":902},"end":{"line":44,"col":33,"byte":1114}},"chronic_flags\u001fpatient_id":{"start":{"line":35,"col":9,"byte":743},"end":{"line":35,"col":19,"byte":753}},"condition_stats\u001factive_conditions":{"start":{"line":26,"col":11,"byte":457},"end":{"line":26,"col":91,"byte":537}},"condition_stats\u001ffirst_condition_date":{"start":{"line":27,"col":11,"byte":548},"end":{"line":27,"col":60,"byte":597}},"condition_stats\u001flast_condition_date":{"start":{"line":28,"col":11,"byte":608},"end":{"line":28,"col":59,"byte":656}},"condition_stats\u001fpatient_id":{"start":{"line":24,"col":9,"byte":397},"end":{"line":24,"col":19,"byte":407}},"condition_stats\u001ftotal_conditions":{"start":{"line":25,"col":11,"byte":418},"end":{"line":25,"col":39,"byte":446}},"conditions\u001fcondition_code":{"start":{"line":17,"col":11,"byte":247},"end":{"line":17,"col":25,"byte":261}},"conditions\u001fcondition_description":{"start":{"line":18,"col":11,"byte":272},"end":{"line":18,"col":32,"byte":293}},"conditions\u001fcondition_end_date":{"start":{"line":16,"col":11,"byte":218},"end":{"line":16,"col":29,"byte":236}},"conditions\u001fcondition_start_date":{"start":{"line":15,"col":11,"byte":187},"end":{"line":15,"col":31,"byte":207}},"conditions\u001fpatient_id":{"start":{"line":14,"col":9,"byte":166},"end":{"line":14,"col":19,"byte":176}},"final\u001factive_conditions":{"start":{"line":67,"col":11,"byte":1800},"end":{"line":67,"col":65,"byte":1854}},"final\u001fchronic_condition_count":{"start":{"line":73,"col":11,"byte":2178},"end":{"line":73,"col":77,"byte":2244}},"final\u001ffirst_condition_date":{"start":{"line":68,"col":11,"byte":1865},"end":{"line":68,"col":34,"byte":1888}},"final\u001fhas_diabetes":{"start":{"line":70,"col":11,"byte":1932},"end":{"line":70,"col":76,"byte":1997}},"final\u001fhas_heart_disease":{"start":{"line":72,"col":11,"byte":2092},"end":{"line":72,"col":86,"byte":2167}},"final\u001fhas_hypertension":{"start":{"line":71,"col":11,"byte":2008},"end":{"line":71,"col":84,"byte":2081}},"final\u001flast_condition_date":{"start":{"line":69,"col":11,"byte":1899},"end":{"line":69,"col":33,"byte":1921}},"final\u001fpatient_id":{"start":{"line":65,"col":9,"byte":1714},"end":{"line":65,"col":21,"byte":1726}},"final\u001ftotal_conditions":{"start":{"line":66,"col":11,"byte":1737},"end":{"line":66,"col":63,"byte":1789}},"patients\u001fpatient_id":{"start":{"line":7,"col":9,"byte":42},"end":{"line":7,"col":19,"byte":52}}}},"column_lineage":{"context":{"active_conditions":{"description":"Number of currently active conditions (no end date)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"active_conditions","min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_active_conditions__0.4537ef7532"},{"kind":"not_null","kwargs":{"column_name":"active_conditions","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_active_conditions.4aeeebebb6"}],"documented":true},"chronic_condition_count":{"description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"chronic_condition_count","max_value":3,"min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_chronic_condition_count__3__0.b747d1158d"},{"kind":"not_null","kwargs":{"column_name":"chronic_condition_count","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_chronic_condition_count.9d5583fe6d"}],"documented":true},"first_condition_date":{"description":"Date of earliest condition diagnosis for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"first_condition_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","row_condition":"first_condition_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_first_condition_date__current_date___1900_01_01_date__first_condition_date_is_not_null.bb01167c58"}],"documented":true},"has_diabetes":{"description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_diabetes__False__True__False.d905d84361"},{"kind":"not_null","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_diabetes.bdb31b80da"}],"documented":true},"has_heart_disease":{"description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_heart_disease","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_heart_disease__False__True__False.37ed4a68fe"},{"kind":"not_null","kwargs":{"column_name":"has_heart_disease","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_heart_disease.12051b0631"}],"documented":true},"has_hypertension":{"description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_hypertension","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_hypertension__False__True__False.189f22b6bf"},{"kind":"not_null","kwargs":{"column_name":"has_hypertension","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_hypertension.e101ed97a8"}],"documented":true},"last_condition_date":{"description":"Date of most recent condition diagnosis for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"last_condition_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","row_condition":"last_condition_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_last_condition_date__current_date___1900_01_01_date__last_condition_date_is_not_null.8d4e9038fa"}],"documented":true},"patient_id":{"description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_patient_id.a53ab7d4d1"},{"kind":"unique","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_int_patients__with_conditions_patient_id.dbb08a220b"}],"documented":true},"total_conditions":{"description":"Total number of conditions ever diagnosed for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_conditions","min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_total_conditions__0.b1764f2a6e"},{"kind":"not_null","kwargs":{"column_name":"total_conditions","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) 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materialized='view'\n    )\n}}\n\n{#\n    Patient condition history with chronic disease flags.\n\n    IMPORTANT: Chronic Condition Detection Limitations\n    --------------------------------------------------\n    The chronic condition flags (has_diabetes, has_hypertension, has_heart_disease)\n    use simplified text matching on condition_description. This is NOT clinical-grade\n    detection and should be used for analytics/reporting only, not clinical decisions.\n\n    Detection patterns:\n    - Diabetes: description contains 'diabetes'\n    - Hypertension: description contains 'hypertension' or 'high blood pressure'\n    - Heart disease: description contains 'heart disease', 'cardiac', 'coronary',\n                     or 'atrial fibrillation'\n\n    For production clinical use, consider:\n    1. Using SNOMED codes instead of text matching\n    2. Implementing ICD-10 code mappings\n    3. Consulting clinical informaticists for proper condition groupings\n    4. Moving patterns to a seed file or reference table for maintainability\n#}\n\nwith patients as (\n    select\n        patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from {{ ref('stg_synthea__conditions') }}\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.int_patients__with_conditions","construct":"left_join[final:condition_stats]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"final — LEFT JOIN condition_stats ON patients.patient_id = condition_stats.patient_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"final","sketches":["- input: ref('dim_patients')\n  rows:\n    - {patient_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__conditions')\n  rows:\n    - {patient_id: 1}\n# expect: the no-match row with NULL stg_synthea__conditions columns (or the intended fallback)"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final select)\u001f*":{"start":{"line":30,"col":8,"byte":542},"end":{"line":30,"col":9,"byte":543}},"grid\u001fmonth_actual":{"start":{"line":23,"col":11,"byte":423},"end":{"line":23,"col":30,"byte":442}},"grid\u001fmonth_key":{"start":{"line":21,"col":14,"byte":312},"end":{"line":21,"col":85,"byte":383}},"grid\u001fmonth_name":{"start":{"line":24,"col":11,"byte":453},"end":{"line":24,"col":28,"byte":470}},"grid\u001fstate":{"start":{"line":25,"col":11,"byte":481},"end":{"line":25,"col":23,"byte":493}},"grid\u001fyear_actual":{"start":{"line":22,"col":11,"byte":394},"end":{"line":22,"col":29,"byte":412}},"months\u001fmonth_actual":{"start":{"line":8,"col":11,"byte":71},"end":{"line":8,"col":23,"byte":83}},"months\u001fmonth_name":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":21,"byte":104}},"months\u001fyear_actual":{"start":{"line":7,"col":9,"byte":49},"end":{"line":7,"col":20,"byte":60}},"states\u001fstate":{"start":{"line":14,"col":21,"byte":183},"end":{"line":14,"col":26,"byte":188}}}},"column_lineage":{"context":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","rows":null,"format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv"}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. 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condition_end_date\n\n        -- condition details\n        , CODE as condition_code\n        , DESCRIPTION as condition_description\n    from source\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_start_date as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as condition_id\n        , *\n    from renamed\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","node_spans":{"(final 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as gender\n        , SPECIALITY as specialty\n\n        -- location\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , UTILIZATION as utilization\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_encounter_summary","path":"models/marts/analytics/views/v_encounter_summary.sql","description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full 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provider_name\n    from {{ ref('dim_providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_name\n    from {{ ref('dim_organizations') }}\n),\n\npayers as (\n    select\n        payer_key\n        , payer_name\n    from {{ ref('dim_payers') }}\n),\n\nfinal as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_patient_current_conditions","path":"models/marts/analytics/views/v_patient_current_conditions.sql","description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient 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patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from 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ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, 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Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","tests":[],"is_recursive":false}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). 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identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at <= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_dq_quarantine__medications":{"description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"end_after_1900_if_present","description":"Flag: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"no_future_medication_dates","description":"Flag: cast(medication_start_at as date) <= current_date"},{"name":"start_after_1900","description":"Flag: medication_start_at >= timestamp '1900-01-01'"},{"name":"valid_medication_dates","description":"Flag: medication_end_at is null or medication_end_at >= medication_start_at"}]},"int_encounters__enriched":{"description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","materialized":"view","columns":[{"name":"condition_count","description":"Number of conditions diagnosed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID), sourced from fct_encounters","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_count","description":"Number of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_count","description":"Number of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_condition_cost","description":"Total cost associated with conditions (currently always 0 per model logic)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_cost","description":"Total cost of all clinical events (conditions + medications + procedures). Note: Condition costs are currently set to 0 in the model.\n","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_count","description":"Total count of all clinical events (conditions + medications + procedures)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_medication_cost","description":"Total cost of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_procedure_cost","description":"Total cost of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"int_patients__with_conditions":{"description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","materialized":"view","columns":[{"name":"active_conditions","description":"Number of currently active conditions (no end date)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"chronic_condition_count","description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"first_condition_date","description":"Date of earliest condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"has_diabetes","description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_heart_disease","description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_hypertension","description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_condition_date","description":"Date of most recent condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_conditions","description":"Total number of conditions ever diagnosed for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__conditions":{"description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"condition_code","description":"SNOMED CT code for the condition","tests":[{"name":"not null"}]},{"name":"condition_description","description":"Description of the condition","tests":[{"name":"not null"}]},{"name":"condition_end_date","description":"Date when condition resolved (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"condition_id","description":"Surrogate key for the condition record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_start_date","description":"Date when condition was diagnosed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__observations":{"description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"encounter_id","description":"Foreign key to the encounter. Note: ~10% of observations have null encounter_id (from external sources or historical data imports).\n","tests":[{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"observation_at","description":"Timestamp when observation was recorded","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"observation_code","description":"LOINC code for the observation","tests":[{"name":"not null"}]},{"name":"observation_description","description":"Description of the observation","tests":[{"name":"not null"}]},{"name":"observation_id","description":"Surrogate key for the observation record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"observation_type","description":"Type of observation (numeric, text, etc.)"},{"name":"observation_value","description":"Value of the observation (stored as string)"},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"units","description":"Units of measurement"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__organizations":{"description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Unique organization identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__procedures":{"description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost of the procedure","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"procedure_at","description":"Timestamp when the procedure was performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_code","description":"SNOMED CT code for the procedure","tests":[{"name":"not null"}]},{"name":"procedure_description","description":"Description of the procedure","tests":[{"name":"not null"}]},{"name":"procedure_id","description":"Surrogate key for the procedure record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of the procedure"},{"name":"reason_description","description":"Description of the reason for the procedure"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__providers":{"description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Foreign key to the provider's organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"provider_id","description":"Unique provider identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
+<script type="application/json" id="cute-dbt-data">{"baseline":"","models":[{"name":"dim_conditions","path":"models/marts/analytics/dim_conditions.sql","description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"condition_aggregates","role":"transform"},{"id":"deduped","role":"transform"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_conditions.sql","role":"final"}],"edges":[{"from":"conditions_source","to":"condition_aggregates","edge_type":"from"},{"from":"condition_aggregates","to":"deduped","edge_type":"from"},{"from":"deduped","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_aggregates":"condition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n)","conditions_source":"conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n)","deduped":"deduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n)","final":"final as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n)","unknown_member":"unknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n)"},"code_map":{"compiled":"\n\n\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":105,"col":1,"byte":2681},"end":{"line":105,"col":20,"byte":2700}},"condition_aggregates":{"start":{"line":18,"col":1,"byte":420},"end":{"line":28,"col":2,"byte":854}},"conditions_source":{"start":{"line":5,"col":6,"byte":9},"end":{"line":16,"col":2,"byte":417}},"deduped":{"start":{"line":31,"col":1,"byte":917},"end":{"line":44,"col":2,"byte":1295}},"final":{"start":{"line":79,"col":1,"byte":2118},"end":{"line":103,"col":2,"byte":2679}},"sequenced":{"start":{"line":46,"col":1,"byte":1298},"end":{"line":65,"col":2,"byte":1735}},"unknown_member":{"start":{"line":68,"col":1,"byte":1795},"end":{"line":77,"col":2,"byte":2115}}},"column_spans":{"(final 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when record was loaded","documented":true},"condition_code":{"description":"ICD/SNOMED code (natural key)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_code.193c903fa2"},{"kind":"unique","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_code.99cd9f3457"}],"documented":true},"condition_description":{"description":"Full condition name/description","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_description","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_description.dc79ef5b8c"}],"documented":true},"condition_key":{"description":"Surrogate key (row_number)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_key.5e35e4562f"},{"kind":"unique","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"}],"documented":true},"encounter_count":{"description":"Total number of encounters with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_encounter_count__0.9a92420a5b"}],"documented":true},"first_occurrence_date":{"description":"Earliest date this condition was diagnosed in the dataset","documented":true},"last_occurrence_date":{"description":"Most recent date this condition was diagnosed","documented":true},"patient_count":{"description":"Number of unique patients with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_patient_count__0.4c1de369ac"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_start_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_start_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_end_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_end_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}]},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":409,"col":1,"byte":12325},"end":{"line":409,"col":20,"byte":12344}},"date_spine":{"start":{"line":5,"col":6,"byte":9},"end":{"line":333,"col":2,"byte":8986}},"final":{"start":{"line":335,"col":1,"byte":8989},"end":{"line":407,"col":2,"byte":12323}}},"column_spans":{"(final select)\u001f*":{"start":{"line":409,"col":8,"byte":12332},"end":{"line":409,"col":9,"byte":12333}},"date_spine\u001fdate_day":{"start":{"line":178,"col":9,"byte":3934},"end":{"line":178,"col":19,"byte":3944}},"date_spine\u001fday_of_month":{"start":{"line":193,"col":9,"byte":4584},"end":{"line":193,"col":53,"byte":4628}},"date_spine\u001fday_of_week":{"start":{"line":188,"col":14,"byte":4285},"end":{"line":188,"col":73,"byte":4344}},"date_spine\u001fday_of_week_iso":{"start":{"line":190,"col":14,"byte":4394},"end":{"line":190,"col":76,"byte":4456}},"date_spine\u001fday_of_week_name":{"start":{"line":191,"col":9,"byte":4466},"end":{"line":191,"col":48,"byte":4505}},"date_spine\u001fday_of_week_name_short":{"start":{"line":192,"col":16,"byte":4522},"end":{"line":192,"col":68,"byte":4574}},"date_spine\u001fday_of_year":{"start":{"line":194,"col":9,"byte":4638},"end":{"line":194,"col":58,"byte":4687}},"date_spine\u001fiso_week_end_date":{"start":{"line":239,"col":11,"byte":5901},"end":{"line":239,"col":118,"byte":6008}},"date_spine\u001fiso_week_of_year":{"start":{"line":247,"col":10,"byte":6319},"end":{"line":247,"col":71,"byte":6380}},"date_spine\u001fiso_week_start_date":{"start":{"line":236,"col":14,"byte":5812},"end":{"line":236,"col":76,"byte":5874}},"date_spine\u001fiso_year_week":{"start":{"line":248,"col":9,"byte":6390},"end":{"line":267,"col":25,"byte":7034}},"date_spine\u001fmonth_end_date":{"start":{"line":287,"col":6,"byte":7592},"end":{"line":288,"col":44,"byte":7746}},"date_spine\u001fmonth_name":{"start":{"line":278,"col":9,"byte":7373},"end":{"line":278,"col":44,"byte":7408}},"date_spine\u001fmonth_name_short":{"start":{"line":279,"col":16,"byte":7425},"end":{"line":279,"col":64,"byte":7473}},"date_spine\u001fmonth_of_year":{"start":{"line":276,"col":13,"byte":7295},"end":{"line":277,"col":27,"byte":7363}},"date_spine\u001fmonth_start_date":{"start":{"line":281,"col":14,"byte":7489},"end":{"line":281,"col":74,"byte":7549}},"date_spine\u001fnext_date_day":{"start":{"line":184,"col":6,"byte":4064},"end":{"line":184,"col":80,"byte":4138}},"date_spine\u001fprior_date_day":{"start":{"line":181,"col":6,"byte":3966},"end":{"line":181,"col":82,"byte":4042}},"date_spine\u001fprior_year_date_day":{"start":{"line":185,"col":9,"byte":4148},"end":{"line":185,"col":53,"byte":4192}},"date_spine\u001fprior_year_iso_week_end_date":{"start":{"line":244,"col":11,"byte":6152},"end":{"line":245,"col":40,"byte":6299}},"date_spine\u001fprior_year_iso_week_of_year":{"start":{"line":272,"col":10,"byte":7165},"end":{"line":273,"col":39,"byte":7266}},"date_spine\u001fprior_year_iso_week_start_date":{"start":{"line":240,"col":14,"byte":6023},"end":{"line":241,"col":42,"byte":6125}},"date_spine\u001fprior_year_month_end_date":{"start":{"line":299,"col":6,"byte":7922},"end":{"line":301,"col":39,"byte":8107}},"date_spine\u001fprior_year_month_start_date":{"start":{"line":291,"col":13,"byte":7775},"end":{"line":292,"col":41,"byte":7866}},"date_spine\u001fprior_year_over_year_date_day":{"start":{"line":186,"col":9,"byte":4202},"end":{"line":186,"col":40,"byte":4233}},"date_spine\u001fprior_year_week_end_date":{"start":{"line":229,"col":6,"byte":5494},"end":{"line":233,"col":36,"byte":5726}},"date_spine\u001fprior_year_week_of_year":{"start":{"line":269,"col":19,"byte":7055},"end":{"line":270,"col":35,"byte":7145}},"date_spine\u001fprior_year_week_start_date":{"start":{"line":218,"col":6,"byte":5231},"end":{"line":222,"col":38,"byte":5418}},"date_spine\u001fquarter_end_date":{"start":{"line":315,"col":6,"byte":8409},"end":{"line":316,"col":46,"byte":8567}},"date_spine\u001fquarter_of_year":{"start":{"line":304,"col":13,"byte":8136},"end":{"line":305,"col":29,"byte":8208}},"date_spine\u001fquarter_start_date":{"start":{"line":307,"col":13,"byte":8236},"end":{"line":308,"col":32,"byte":8309}},"date_spine\u001fweek_end_date":{"start":{"line":210,"col":6,"byte":4975},"end":{"line":213,"col":70,"byte":5167}},"date_spine\u001fweek_of_year":{"start":{"line":234,"col":19,"byte":5746},"end":{"line":234,"col":69,"byte":5796}},"date_spine\u001fweek_start_date":{"start":{"line":200,"col":6,"byte":4752},"end":{"line":203,"col":25,"byte":4899}},"date_spine\u001fyear_end_date":{"start":{"line":327,"col":6,"byte":8777},"end":{"line":328,"col":43,"byte":8928}},"date_spine\u001fyear_number":{"start":{"line":319,"col":13,"byte":8596},"end":{"line":320,"col":25,"byte":8661}},"date_spine\u001fyear_start_date":{"start":{"line":321,"col":14,"byte":8676},"end":{"line":321,"col":72,"byte":8734}},"final\u001fdate_actual":{"start":{"line":341,"col":11,"byte":9213},"end":{"line":341,"col":34,"byte":9236}},"final\u001fdate_key":{"start":{"line":338,"col":14,"byte":9098},"end":{"line":338,"col":94,"byte":9178}},"final\u001fday_name":{"start":{"line":345,"col":11,"byte":9355},"end":{"line":345,"col":39,"byte":9383}},"final\u001fday_name_short":{"start":{"line":346,"col":11,"byte":9394},"end":{"line":346,"col":51,"byte":9434}},"final\u001fday_of_month":{"sta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actual date value","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"date_actual","max_value":"'2025-12-31'::date","min_value":"'1909-01-01'::date","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_date_date_actual___2025_12_31_date___1909_01_01_date.f3b5c5d47b"},{"kind":"not_null","kwargs":{"column_name":"date_actual","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_date_actual.85284edc63"},{"kind":"unique","kwargs":{"column_name":"date_actual","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.unique_dim_date_date_actual.107fe1b6a2"}],"documented":true},"date_key":{"description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"date_key","max_value":20251231,"min_value":19090101,"model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_date_date_key__20251231__19090101.ddd369949a"},{"kind":"not_null","kwargs":{"column_name":"date_key","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_date_key.881d0a31b6"},{"kind":"unique","kwargs":{"column_name":"date_key","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.unique_dim_date_date_key.0869fd48f9"}],"documented":true},"day_name":{"description":"Full day name (e.g., Monday, Tuesday)","tests":[{"kind":"not_null","kwargs":{"column_name":"day_name","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_day_name.3a968349a4"}],"documented":true},"day_name_short":{"description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"kind":"not_null","kwargs":{"column_name":"day_name_short","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_day_name_short.ddec7c56fd"}],"documented":true},"day_of_month":{"description":"Day of the month (1-31)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"day_of_month","max_value":31,"min_value":1,"model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_date_day_of_month__31__1.95bef47e40"},{"kind":"not_null","kwargs":{"column_name":"day_of_month","model":"{{ get_where_subquery(ref('dim_date')) 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)\n}}\n\n{#\n    Calendar dimension covering 1909-01-01 to 2025-12-31.\n\n    Note on date_key: Uses YYYYMMDD integer format. 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organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n  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as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final 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organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from 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as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , 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      -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as 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Each row represents a\n    specific version of a patient's record.\n\n    SCD Type 2 Columns:\n    - patient_key: Surrogate key (unique per version)\n    - patient_id: Natural key (same across versions)\n    - valid_from: When this version became active\n    - valid_to: When this version was superseded (null = current)\n    - is_current: True for the active version\n\n    To query current patients only:\n        WHERE is_current = true\n\n    To query patient state at a point in time:\n        WHERE valid_from <= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_patients","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__patients')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}]},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as 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      -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total 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get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme 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Health"}},{"display":"555-0100","key":{"t":"str","v":"555-0100"}},{"display":"1250.0","key":{"t":"number","v":"1250"}},{"display":"MA","key":{"t":"str","v":"MA"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"8","key":{"t":"number","v":"8"}},{"display":"02101","key":{"t":"str","v":"02101"}}]}]},"rows":[{"address":"1 Acme Way","amount_covered":1000.0,"amount_uncovered":250.0,"average_quality_of_life_score":0.85,"city":"Boston","covered_encounters":10,"covered_immunizations":4,"covered_medications":5,"covered_procedures":3,"member_months":96,"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_name":"Acme Health","phone":"555-0100","revenue":1250.0,"state":"MA","uncovered_encounters":2,"uncovered_immunizations":0,"uncovered_medications":1,"uncovered_procedures":0,"unique_customers":8,"zip_code":"02101"}],"format":"dict","column_meta":{"address":{"description":"Street address"},"amount_covered":{"description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"amount_uncovered":{"description":"Total amount not covered by payer"},"average_quality_of_life_score":{"description":"Average quality of life score for members"},"city":{"description":"City"},"covered_encounters":{"description":"Number of encounters covered"},"covered_immunizations":{"description":"Number of immunizations covered"},"covered_medications":{"description":"Number of medications covered"},"covered_procedures":{"description":"Number of procedures covered"},"member_months":{"description":"Total member months"},"payer_id":{"description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]},"phone":{"description":"Phone number"},"revenue":{"description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"state":{"description":"State where headquartered"},"uncovered_encounters":{"description":"Number of encounters not covered"},"uncovered_immunizations":{"description":"Number of immunizations not covered"},"uncovered_medications":{"description":"Number of medications not covered"},"uncovered_procedures":{"description":"Number of procedures not covered"},"unique_customers":{"description":"Number of unique customers"},"zip_code":{"description":"ZIP code (stored as numeric, 5 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\"memory\".\"main_marts\".\"dim_organizations\"\n)","providers":"providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n)","unknown_member":"unknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='provider_key'\n    )\n}}\n\nwith providers as (\n    select * from {{ ref('stg_synthea__providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from {{ ref('dim_organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_providers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"]},"evidence":[{"label":"unique_key","value":"provider_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_providers","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('dim_organizations'), ref('stg_synthea__providers')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. 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cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_encounter_id.5c743b1ef7"}],"documented":true},"encounter_key":{"description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_encounter_key.cc0e40eeb8"},{"kind":"relationships","kwargs":{"column_name":"encounter_key","field":"encounter_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","to":"ref('fct_encounters')"},"node_id":"test.healthcare_analytics.relationships_fct_clinical_events_encounter_key__encounter_key__ref_fct_encounters_.7a30909f42"}],"documented":true},"event_cost":{"description":"Cost of the clinical event","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"event_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","row_condition":"event_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_clinical_events_event_cost__0__event_cost_is_not_null.2c2582a25b"}],"documented":true},"event_date_key":{"description":"Foreign key to dim_date for event start","tests":[{"kind":"not_null","kwargs":{"column_name":"event_date_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_event_date_key.d605213965"},{"kind":"relationships","kwargs":{"column_name":"event_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_clinical_events_event_date_key__date_key__ref_dim_date_.0995b8993f"}],"documented":true},"event_end_date":{"description":"End date of the clinical event (null if ongoing or 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unique_key='clinical_event_key'\n    )\n}}\n\nwith valid_encounters as (\n    select encounter_id\n    from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from {{ ref('stg_synthea__conditions') }} as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from {{ ref('stg_synthea__medications') }} as med\n    {{ quarantine_filter() }}\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from {{ ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as 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mod","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_condition_cohorts","path":"models/marts/analytics/fct_condition_cohorts.sql","description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"patients","role":"import"},{"id":"dim_conditions","role":"import"},{"id":"condition_windows","role":"transform"},{"id":"with_dimensions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final 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end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n)","conditions_source":"conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n)","dim_conditions":"dim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","with_dimensions":"with_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n)"},"code_map":{"compiled":"\n\n\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","node_spans":{"(final 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when record was loaded","documented":true},"condition_code":{"description":"ICD/SNOMED condition code","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_condition_cohorts_condition_code.180142995a"}],"documented":true},"condition_cohort_key":{"description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_cohort_key","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_condition_cohorts_condition_cohort_key.1edb245ae1"},{"kind":"unique","kwargs":{"column_name":"condition_cohort_key","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"}],"documented":true},"condition_description":{"description":"Full condition 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unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}]},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as 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encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","node_spans":{"(final 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when record was loaded","documented":true},"cost_analysis_key":{"description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"kind":"not_null","kwargs":{"column_name":"cost_analysis_key","model":"{{ get_where_subquery(ref('fct_cost_analysis')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_cost_analysis_cost_analysis_key.a72ad66418"},{"kind":"unique","kwargs":{"column_name":"cost_analysis_key","model":"{{ get_where_subquery(ref('fct_cost_analysis')) }}"},"node_id":"test.healthcare_analytics.unique_fct_cost_analysis_cost_analysis_key.2aecf62159"}],"documented":true},"cost_per_procedure":{"description":"Average cost per procedure (null if no procedures)","documented":true},"coverage_pct":{"description":"Percentage covered by insurance (0-100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('fct_cost_analysis')) 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unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end 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This finding's evidence carries a copy-pasteable given sketch.","pin_node":"encounters_enriched","sketches":["- input: ref('fct_encounters')\n  rows:\n    - {encounter_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__procedures')\n  rows:\n    - {encounter_id: 1}\n# expect: the no-match row with NULL stg_synthea__procedures columns (or the intended fallback)"]}]},{"name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. 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timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final select)\u001f*":{"start":{"line":109,"col":8,"byte":2736},"end":{"line":109,"col":9,"byte":2737}},"dim_date\u001fdate_actual":{"start":{"line":45,"col":11,"byte":721},"end":{"line":45,"col":22,"byte":732}},"dim_date\u001fdate_key":{"start":{"line":44,"col":9,"byte":702},"end":{"line":44,"col":17,"byte":710}},"encounters\u001f*":{"start":{"line":4,"col":12,"byte":34},"end":{"line":4,"col":13,"byte":35}},"final\u001f_loaded_at":{"start":{"line":98,"col":11,"byte":2250},"end":{"line":98,"col":42,"byte":2281}},"final\u001fbase_encounter_cost":{"start":{"line":86,"col":11,"byte":1836},"end":{"line":86,"col":32,"byte":1857}},"final\u001fduration_minutes":{"start":{"line":83,"col":11,"byte":1726},"end":{"line":83,"col":92,"byte":1807}},"final\u001fencounter_class":{"start":{"line":72,"col":11,"byte":1470},"end":{"line":72,"col":28,"byte":1487}},"final\u001fencounter_code":{"start":{"line":73,"col":11,"byte":1498},"end":{"line":73,"col":27,"byte":1514}},"final\u001fencounter_description":{"start":{"line":74,"col":11,"byte":1525},"end":{"line":74,"col":34,"byte":1548}},"final\u001fencounter_end_at":{"start":{"line":80,"col":11,"byte":1668},"end":{"line":80,"col":29,"byte":1686}},"final\u001fencounter_end_date_key":{"start":{"line":69,"col":11,"byte":1385},"end":{"line":69,"col":62,"byte":1436}},"final\u001fencounter_id":{"start":{"line":55,"col":11,"byte":929},"end":{"line":55,"col":25,"byte":943}},"final\u001fencounter_key":{"start":{"line":52,"col":9,"byte":834},"end":{"line":52,"col":69,"byte":894}},"final\u001fencounter_start_at":{"start":{"line":79,"col":11,"byte":1637},"end":{"line":79,"col":31,"byte":1657}},"final\u001fencounter_start_date_key":{"start":{"line":68,"col":11,"byte":1321},"end":{"line":68,"col":64,"byte":1374}},"final\u001forganization_id":{"start":{"line":63,"col":11,"byte":1200},"end":{"line":63,"col":28,"byte":1217}},"final\u001forganization_key":{"start":{"line":62,"col":11,"byte":1137},"end":{"line":62,"col":63,"byte":1189}},"final\u001fpatient_age_at_encounter":{"start":{"line":92,"col":11,"byte":2047},"end":{"line":95,"col":40,"byte":2218}},"final\u001fpatient_id":{"start":{"line":59,"col":11,"byte":1034},"end":{"line":59,"col":23,"byte":1046}},"final\u001fpatient_key":{"start":{"line":58,"col":11,"byte":981},"end":{"line":58,"col":53,"byte":1023}},"final\u001fpatient_responsibility":{"start":{"line":89,"col":11,"byte":1924},"end":{"line":89,"col":87,"byte":2000}},"final\u001fpayer_coverage":{"start":{"line":88,"col":11,"byte":1897},"end":{"line":88,"col":27,"byte":1913}},"final\u001fpayer_id":{"start":{"line":65,"col":11,"byte":1278},"end":{"line":65,"col":21,"byte":1288}},"final\u001fpayer_key":{"start":{"line":64,"col":11,"byte":1228},"end":{"line":64,"col":50,"byte":1267}},"final\u001fprovider_id":{"start":{"line":61,"col":11,"byte":1113},"end":{"line":61,"col":24,"byte":1126}},"final\u001fprovider_key":{"start":{"line":60,"col":11,"byte":1057},"end":{"line":60,"col":56,"byte":1102}},"final\u001freason_code":{"start":{"line":75,"col":11,"byte":1559},"end":{"line":75,"col":24,"byte":1572}},"final\u001freason_description":{"start":{"line":76,"col":11,"byte":1583},"end":{"line":76,"col":31,"byte":1603}},"final\u001ftotal_claim_cost":{"start":{"line":87,"col":11,"byte":1868},"end":{"line":87,"col":29,"byte":1886}},"organizations\u001forganization_id":{"start":{"line":31,"col":11,"byte":490},"end":{"line":31,"col":26,"byte":505}},"organizations\u001forganization_key":{"start":{"line":30,"col":9,"byte":463},"end":{"line":30,"col":25,"byte":479}},"patients\u001fbirth_date":{"start":{"line":16,"col":11,"byte":216},"end":{"line":16,"col":21,"byte":226}},"patients\u001fpatient_id":{"start":{"line":15,"col":11,"byte":195},"end":{"line":15,"col":21,"byte":205}},"patients\u001fpatient_key":{"start":{"line":14,"col":9,"byte":173},"end":{"line":14,"col":20,"byte":184}},"payers\u001fpayer_id":{"start":{"line":38,"col":11,"byte":612},"end":{"line":38,"col":19,"byte":620}},"payers\u001fpayer_key":{"start":{"line":37,"col":9,"byte":592},"end":{"line":37,"col":18,"byte":601}},"providers\u001fprovider_id":{"start":{"line":24,"col":11,"byte":362},"end":{"line":24,"col":22,"byte":373}},"providers\u001fprovider_key":{"start":{"line":23,"col":9,"byte":339},"end":{"line":23,"col":21,"byte":351}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"base_encounter_cost":{"description":"Base cost of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"base_encounter_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"base_encounter_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_base_encounter_cost__0__base_encounter_cost_is_not_null.a096f214be"}],"documented":true},"duration_minutes":{"description":"Duration of encounter in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_duration_minutes__0__duration_minutes_is_not_null.ce51123f28"}],"documented":true},"encounter_class":{"description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.59f26de6a5"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_class.9419876296"}],"documented":true},"encounter_code":{"description":"SNOMED CT code for the encounter type","documented":true},"encounter_description":{"description":"Description of the encounter type","documented":true},"encounter_end_at":{"description":"End timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_end_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"encounter_end_at is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_end_at__current_timestamp___1900_01_01_timestamp__encounter_end_at_is_not_null.393dec12f4"}],"documented":true},"encounter_end_date_key":{"description":"Foreign key to dim_date for encounter end","documented":true},"encounter_id":{"description":"Natural encounter identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_id.f7111b4eea"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_id.ce88bae928"}],"documented":true},"encounter_key":{"description":"Surrogate key for the encounter","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_key.4fa2f03d5e"},{"kind":"unique","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"}],"documented":true},"encounter_start_at":{"description":"Start timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_start_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_start_at__current_timestamp___1900_01_01_timestamp.f32b839e27"},{"kind":"not_null","kwargs":{"column_name":"encounter_start_at","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_at.c47cc214ac"}],"documented":true},"encounter_start_date_key":{"description":"Foreign key to dim_date for encounter start","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_start_date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_date_key.73ce57ba43"},{"kind":"relationships","kwargs":{"column_name":"encounter_start_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_encounter_start_date_key__date_key__ref_dim_date_.4f1d88383c"}],"documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_id.acfb633ecb"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_key.815fa1363c"},{"kind":"relationships","kwargs":{"column_name":"organization_key","field":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_organizations')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_organization_key__organization_key__ref_dim_organizations_.5feb1b3ad6"}],"documented":true},"patient_age_at_encounter":{"description":"Patient's age at the time of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_age_at_encounter","max_value":150,"min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_age_at_encounter is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_age_at_encounter__150__0__patient_age_at_encounter_is_not_null.bbd237256e"}],"documented":true},"patient_id":{"description":"Natural patient identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_id.9e325e7ac6"}],"documented":true},"patient_key":{"description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_key.e5abbfbe29"},{"kind":"relationships","kwargs":{"column_name":"patient_key","field":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_patients')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_patient_key__patient_key__ref_dim_patients_.ad1c292e28"}],"documented":true},"patient_responsibility":{"description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_responsibility","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_responsibility is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_responsibility__0__patient_responsibility_is_not_null.7a61f00fd0"}],"documented":true},"payer_coverage":{"description":"Amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"payer_coverage","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"payer_coverage is not 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This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"fct_encounters_monthly","path":"models/marts/core/fct_encounters_monthly.sql","description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters_monthly.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"encounters_with_date","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)\n\nselect * from final","node_spans":{"(final 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select)\u001f*":{"start":{"line":53,"col":8,"byte":1381},"end":{"line":53,"col":9,"byte":1382}},"dim_date\u001fdate_key":{"start":{"line":18,"col":9,"byte":334},"end":{"line":18,"col":17,"byte":342}},"dim_date\u001fmonth_actual":{"start":{"line":20,"col":11,"byte":375},"end":{"line":20,"col":23,"byte":387}},"dim_date\u001fyear_actual":{"start":{"line":19,"col":11,"byte":353},"end":{"line":19,"col":22,"byte":364}},"encounters\u001fduration_minutes":{"start":{"line":12,"col":11,"byte":232},"end":{"line":12,"col":27,"byte":248}},"encounters\u001fencounter_class":{"start":{"line":8,"col":11,"byte":121},"end":{"line":8,"col":26,"byte":136}},"encounters\u001fencounter_id":{"start":{"line":5,"col":9,"byte":42},"end":{"line":5,"col":21,"byte":54}},"encounters\u001fencounter_start_date_key":{"start":{"line":7,"col":11,"byte":86},"end":{"line":7,"col":35,"byte":110}},"encounters\u001fpatient_id":{"start":{"line":6,"col":11,"byte":65},"end":{"line":6,"col":21,"byte":75}},"encounters\u001fpatient_responsibility":{"start":{"line":11,"col":11,"byte":199},"end":{"line":11,"col":33,"byte":221}},"encounters\u001fpayer_coverage":{"start":{"line":10,"col":11,"byte":174},"end":{"line":10,"col":25,"byte":188}},"encounters\u001ftotal_claim_cost":{"start":{"line":9,"col":11,"byte":147},"end":{"line":9,"col":27,"byte":163}},"encounters_with_date\u001f*":{"start":{"line":26,"col":9,"byte":479},"end":{"line":26,"col":12,"byte":482}},"encounters_with_date\u001fmonth_actual":{"start":{"line":28,"col":11,"byte":517},"end":{"line":28,"col":25,"byte":531}},"encounters_with_date\u001fyear_actual":{"start":{"line":27,"col":11,"byte":493},"end":{"line":27,"col":24,"byte":506}},"encounters_with_date\u001fyear_month":{"start":{"line":29,"col":16,"byte":547},"end":{"line":30,"col":72,"byte":654}},"final\u001f_loaded_at":{"start":{"line":48,"col":11,"byte":1241},"end":{"line":48,"col":42,"byte":1272}},"final\u001favg_duration_minutes":{"start":{"line":47,"col":11,"byte":1185},"end":{"line":47,"col":56,"byte":1230}},"final\u001fencounter_class":{"start":{"line":41,"col":11,"byte":889},"end":{"line":41,"col":26,"byte":904}},"final\u001fencounter_count":{"start":{"line":42,"col":11,"byte":915},"end":{"line":42,"col":38,"byte":942}},"final\u001fmonth_actual":{"start":{"line":40,"col":11,"byte":866},"end":{"line":40,"col":23,"byte":878}},"final\u001ftotal_claim_cost":{"start":{"line":44,"col":11,"byte":1009},"end":{"line":44,"col":52,"byte":1050}},"final\u001ftotal_patient_responsibility":{"start":{"line":46,"col":11,"byte":1115},"end":{"line":46,"col":70,"byte":1174}},"final\u001ftotal_payer_coverage":{"start":{"line":45,"col":11,"byte":1061},"end":{"line":45,"col":54,"byte":1104}},"final\u001funique_patients":{"start":{"line":43,"col":11,"byte":953},"end":{"line":43,"col":56,"byte":998}},"final\u001fyear_actual":{"start":{"line":39,"col":11,"byte":844},"end":{"line":39,"col":22,"byte":855}},"final\u001fyear_month":{"start":{"line":38,"col":9,"byte":823},"end":{"line":38,"col":19,"byte":833}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"avg_duration_minutes":{"description":"Average encounter duration in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"avg_duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"avg_duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_avg_duration_minutes__0__avg_duration_minutes_is_not_null.0cea70c94f"}],"documented":true},"encounter_class":{"description":"Type of encounter","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_monthly_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.274b5b6532"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_encounter_class.d503677b5a"}],"documented":true},"encounter_count":{"description":"Number of encounters in the month","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_encounter_count__1.03132101e0"},{"kind":"not_null","kwargs":{"column_name":"encounter_count","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_encounter_count.5046b087e4"}],"documented":true},"month_actual":{"description":"Month number (1-12)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"month_actual","max_value":12,"min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_month_actual__12__1.f650fcabb6"},{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_month_actual.82083ad465"}],"documented":true},"total_claim_cost":{"description":"Sum of total claim costs","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_claim_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_claim_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_claim_cost__0__total_claim_cost_is_not_null.09195ba1c6"}],"documented":true},"total_patient_responsibility":{"description":"Sum of patient responsibility amounts","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_patient_responsibility","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_patient_responsibility is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_patient_responsibility__0__total_patient_responsibility_is_not_null.e824258649"}],"documented":true},"total_payer_coverage":{"description":"Sum of payer coverage amounts","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_payer_coverage","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_payer_coverage is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_payer_coverage__0__total_payer_coverage_is_not_null.1cae11e2af"}],"documented":true},"unique_patients":{"description":"Number of unique patients with encounters","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"unique_patients","min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_unique_patients__1.86f77befe3"},{"kind":"not_null","kwargs":{"column_name":"unique_patients","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) 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   where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , cast(count(*) as decimal(18, 2)) / nullif(count(distinct patient_id), 0) as encounters_per_patient\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_actual, encounter_class\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_yearly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_actual, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"}]},{"name":"fct_patient_summary","path":"models/marts/analytics/fct_patient_summary.sql","description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health 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\"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","conditions_per_patient_year":"conditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from 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e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct 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 select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","node_spans":{"(final 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select)\u001f*":{"start":{"line":143,"col":8,"byte":3621},"end":{"line":143,"col":9,"byte":3622}},"dim_date\u001fdate_key":{"start":{"line":26,"col":9,"byte":485},"end":{"line":26,"col":17,"byte":493}},"dim_date\u001fmonth_actual":{"start":{"line":28,"col":11,"byte":526},"end":{"line":28,"col":23,"byte":538}},"dim_date\u001fyear_actual":{"start":{"line":27,"col":11,"byte":504},"end":{"line":27,"col":22,"byte":515}},"encounters\u001fduration_minutes":{"start":{"line":18,"col":11,"byte":319},"end":{"line":18,"col":27,"byte":335}},"encounters\u001fencounter_id":{"start":{"line":8,"col":11,"byte":68},"end":{"line":8,"col":23,"byte":80}},"encounters\u001fencounter_key":{"start":{"line":7,"col":9,"byte":44},"end":{"line":7,"col":22,"byte":57}},"encounters\u001fencounter_start_date_key":{"start":{"line":15,"col":11,"byte":232},"end":{"line":15,"col":35,"byte":256}},"encounters\u001forganization_id":{"start":{"line":14,"col":11,"byte":206},"end":{"line":14,"col":26,"byte":221}},"encounters\u001forganization_key":{"start":{"line":13,"col":11,"byte":179},"end":{"line":13,"col":27,"byte":195}},"encounters\u001fpatient_age_at_encounter":{"start":{"line":19,"col":11,"byte":346},"end":{"line":19,"col":35,"byte":370}},"encounters\u001fpatient_id":{"start":{"line":10,"col":11,"byte":113},"end":{"line":10,"col":21,"byte":123}},"encounters\u001fpatient_key":{"start":{"line":9,"col":11,"byte":91},"end":{"line":9,"col":22,"byte":102}},"encounters\u001fpayer_coverage":{"start":{"line":17,"col":11,"byte":294},"end":{"line":17,"col":25,"byte":308}},"encounters\u001fprovider_id":{"start":{"line":12,"col":11,"byte":157},"end":{"line":12,"col":22,"byte":168}},"encounters\u001fprovider_key":{"start":{"line":11,"col":11,"byte":134},"end":{"line":11,"col":23,"byte":146}},"encounters\u001ftotal_claim_cost":{"start":{"line":16,"col":11,"byte":267},"end":{"line":16,"col":27,"byte":283}},"encounters_with_date\u001f*":{"start":{"line":43,"col":9,"byte":860},"end":{"line":43,"col":12,"byte":863}},"encounters_with_date\u001fmonth_actual":{"start":{"line":45,"col":11,"byte":898},"end":{"line":45,"col":25,"byte":912}},"encounters_with_date\u001fprocedure_count":{"start":{"line":49,"col":11,"byte":1062},"end":{"line":49,"col":61,"byte":1112}},"encounters_with_date\u001fyear_actual":{"start":{"line":44,"col":11,"byte":874},"end":{"line":44,"col":24,"byte":887}},"encounters_with_date\u001fyear_month_str":{"start":{"line":46,"col":16,"byte":928},"end":{"line":48,"col":79,"byte":1051}},"final\u001f_loaded_at":{"start":{"line":138,"col":11,"byte":3550},"end":{"line":138,"col":42,"byte":3581}},"final\u001favg_cost_per_encounter":{"start":{"line":125,"col":11,"byte":3162},"end":{"line":129,"col":38,"byte":3316}},"final\u001favg_encounter_duration_minutes":{"start":{"line":118,"col":11,"byte":2896},"end":{"line":118,"col":85,"byte":2970}},"final\u001favg_patient_age":{"start":{"line":117,"col":11,"byte":2841},"end":{"line":117,"col":55,"byte":2885}},"final\u001fencounter_count":{"start":{"line":112,"col":11,"byte":2726},"end":{"line":112,"col":26,"byte":2741}},"final\u001fencounters_per_patient_avg":{"start":{"line":131,"col":11,"byte":3328},"end":{"line":135,"col":42,"byte":3518}},"final\u001fmonth_actual":{"start":{"line":108,"col":11,"byte":2649},"end":{"line":108,"col":23,"byte":2661}},"final\u001forganization_id":{"start":{"line":104,"col":11,"byte":2578},"end":{"line":104,"col":26,"byte":2593}},"final\u001forganization_key":{"start":{"line":103,"col":11,"byte":2551},"end":{"line":103,"col":27,"byte":2567}},"final\u001fprovider_id":{"start":{"line":102,"col":11,"byte":2529},"end":{"line":102,"col":22,"byte":2540}},"final\u001fprovider_key":{"start":{"line":101,"col":11,"byte":2506},"end":{"line":101,"col":23,"byte":2518}},"final\u001fprovider_metrics_key":{"start":{"line":98,"col":9,"byte":2386},"end":{"line":98,"col":91,"byte":2468}},"final\u001ftotal_cost":{"start":{"line":121,"col":11,"byte":3009},"end":{"line":121,"col":48,"byte":3046}},"final\u001ftotal_payer_coverage":{"start":{"line":122,"col":11,"byte":3057},"end":{"line":122,"col":68,"byte":3114}},"final\u001ftotal_procedures":{"start":{"line":114,"col":11,"byte":2783},"end":{"line":114,"col":27,"byte":2799}},"final\u001funique_patient_count":{"start":{"line":113,"col":11,"byte":2752},"end":{"line":113,"col":31,"byte":2772}},"final\u001fyear_actual":{"start":{"line":107,"col":11,"byte":2627},"end":{"line":107,"col":22,"byte":2638}},"final\u001fyear_month_str":{"start":{"line":109,"col":11,"byte":2672},"end":{"line":109,"col":25,"byte":2686}},"procedures\u001fencounter_id":{"start":{"line":34,"col":9,"byte":620},"end":{"line":34,"col":21,"byte":632}},"procedures\u001fprocedure_count":{"start":{"line":35,"col":11,"byte":643},"end":{"line":35,"col":38,"byte":670}},"provider_month_agg\u001favg_encounter_duration_minutes":{"start":{"line":75,"col":11,"byte":1828},"end":{"line":75,"col":68,"byte":1885}},"provider_month_agg\u001favg_patient_age":{"start":{"line":72,"col":11,"byte":1737},"end":{"l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   materialized='view'\n    )\n}}\n\nwith encounters as (\n    select\n        encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nconditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from {{ ref('stg_synthea__conditions') }}\n    group by encounter_id\n),\n\nmedications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from {{ ref('stg_synthea__medications') }}\n    {{ quarantine_filter() }}\n    group by encounter_id\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nfinal as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"int_patients__with_conditions","path":"models/intermediate/healthcare/int_patients__with_conditions.sql","description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","dag":{"nodes":[{"id":"patients","role":"import"},{"id":"conditions","role":"import"},{"id":"condition_stats","role":"transform"},{"id":"chronic_flags","role":"transform"},{"id":"chronic_count","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_patients__with_conditions.sql","role":"final"}],"edges":[{"from":"conditions","to":"condition_stats","edge_type":"from"},{"from":"conditions","to":"chronic_flags","edge_type":"from"},{"from":"chronic_flags","to":"chronic_count","edge_type":"from"},{"from":"patients","to":"final","edge_type":"from"},{"from":"condition_stats","to":"final","edge_type":"left"},{"from":"chronic_flags","to":"final","edge_type":"left"},{"from":"chronic_count","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","chronic_count":"chronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n)","chronic_flags":"chronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n)","condition_stats":"condition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n)","conditions":"conditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","final":"final as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)","patients":"patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)"},"code_map":{"compiled":"\n\n\n\nwith patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":80,"col":1,"byte":2459},"end":{"line":80,"col":20,"byte":2478}},"chronic_count":{"start":{"line":56,"col":1,"byte":1519},"end":{"line":61,"col":2,"byte":1681}},"chronic_flags":{"start":{"line":33,"col":1,"byte":705},"end":{"line":54,"col":2,"byte":1516}},"condition_stats":{"start":{"line":22,"col":1,"byte":357},"end":{"line":31,"col":2,"byte":702}},"conditions":{"start":{"line":12,"col":1,"byte":131},"end":{"line":20,"col":2,"byte":354}},"final":{"start":{"line":63,"col":1,"byte":1684},"end":{"line":78,"col":2,"byte":2457}},"patients":{"start":{"line":5,"col":6,"byte":9},"end":{"line":10,"col":2,"byte":128}}},"column_spans":{"(final select)\u001f*":{"start":{"line":80,"col":8,"byte":2466},"end":{"line":80,"col":9,"byte":2467}},"chronic_count\u001fchronic_condition_count":{"start":{"line":59,"col":11,"byte":1578},"end":{"line":59,"col":89,"byte":1656}},"chronic_count\u001fpatient_id":{"start":{"line":58,"col":9,"byte":1557},"end":{"line":58,"col":19,"byte":1567}},"chronic_flags\u001fhas_diabetes":{"start":{"line":36,"col":11,"byte":764},"end":{"line":39,"col":29,"byte":891}},"chronic_flags\u001fhas_heart_disease":{"start":{"line":45,"col":11,"byte":1125},"end":{"line":51,"col":34,"byte":1470}},"chronic_flags\u001fhas_hypertension":{"start":{"line":40,"col":11,"byte":902},"end":{"line":44,"col":33,"byte":1114}},"chronic_flags\u001fpatient_id":{"start":{"line":35,"col":9,"byte":743},"end":{"line":35,"col":19,"byte":753}},"condition_stats\u001factive_conditions":{"start":{"line":26,"col":11,"byte":457},"end":{"line":26,"col":91,"byte":537}},"condition_stats\u001ffirst_condition_date":{"start":{"line":27,"col":11,"byte":548},"end":{"line":27,"col":60,"byte":597}},"condition_stats\u001flast_condition_date":{"start":{"line":28,"col":11,"byte":608},"end":{"line":28,"col":59,"byte":656}},"condition_stats\u001fpatient_id":{"start":{"line":24,"col":9,"byte":397},"end":{"line":24,"col":19,"byte":407}},"condition_stats\u001ftotal_conditions":{"start":{"line":25,"col":11,"byte":418},"end":{"line":25,"col":39,"byte":446}},"conditions\u001fcondition_code":{"start":{"line":17,"col":11,"byte":247},"end":{"line":17,"col":25,"byte":261}},"conditions\u001fcondition_description":{"start":{"line":18,"col":11,"byte":272},"end":{"line":18,"col":32,"byte":293}},"conditions\u001fcondition_end_date":{"start":{"line":16,"col":11,"byte":218},"end":{"line":16,"col":29,"byte":236}},"conditions\u001fcondition_start_date":{"start":{"line":15,"col":11,"byte":187},"end":{"line":15,"col":31,"byte":207}},"conditions\u001fpatient_id":{"start":{"line":14,"col":9,"byte":166},"end":{"line":14,"col":19,"byte":176}},"final\u001factive_conditions":{"start":{"line":67,"col":11,"byte":1800},"end":{"line":67,"col":65,"byte":1854}},"final\u001fchronic_condition_count":{"start":{"line":73,"col":11,"byte":2178},"end":{"line":73,"col":77,"byte":2244}},"final\u001ffirst_condition_date":{"start":{"line":68,"col":11,"byte":1865},"end":{"line":68,"col":34,"byte":1888}},"final\u001fhas_diabetes":{"start":{"line":70,"col":11,"byte":1932},"end":{"line":70,"col":76,"byte":1997}},"final\u001fhas_heart_disease":{"start":{"line":72,"col":11,"byte":2092},"end":{"line":72,"col":86,"byte":2167}},"final\u001fhas_hypertension":{"start":{"line":71,"col":11,"byte":2008},"end":{"line":71,"col":84,"byte":2081}},"final\u001flast_condition_date":{"start":{"line":69,"col":11,"byte":1899},"end":{"line":69,"col":33,"byte":1921}},"final\u001fpatient_id":{"start":{"line":65,"col":9,"byte":1714},"end":{"line":65,"col":21,"byte":1726}},"final\u001ftotal_conditions":{"start":{"line":66,"col":11,"byte":1737},"end":{"line":66,"col":63,"byte":1789}},"patients\u001fpatient_id":{"start":{"line":7,"col":9,"byte":42},"end":{"line":7,"col":19,"byte":52}}}},"column_lineage":{"context":{"active_conditions":{"description":"Number of currently active conditions (no end date)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"active_conditions","min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_active_conditions__0.4537ef7532"},{"kind":"not_null","kwargs":{"column_name":"active_conditions","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_active_conditions.4aeeebebb6"}],"documented":true},"chronic_condition_count":{"description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"chronic_condition_count","max_value":3,"min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_chronic_condition_count__3__0.b747d1158d"},{"kind":"not_null","kwargs":{"column_name":"chronic_condition_count","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_chronic_condition_count.9d5583fe6d"}],"documented":true},"first_condition_date":{"description":"Date of earliest condition diagnosis for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"first_condition_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","row_condition":"first_condition_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_first_condition_date__current_date___1900_01_01_date__first_condition_date_is_not_null.bb01167c58"}],"documented":true},"has_diabetes":{"description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_diabetes__False__True__False.d905d84361"},{"kind":"not_null","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_diabetes.bdb31b80da"}],"documented":true},"has_heart_disease":{"description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_heart_disease","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_heart_disease__False__True__False.37ed4a68fe"},{"kind":"not_null","kwargs":{"column_name":"has_heart_disease","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_heart_disease.12051b0631"}],"documented":true},"has_hypertension":{"description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_hypertension","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_hypertension__False__True__False.189f22b6bf"},{"kind":"not_null","kwargs":{"column_name":"has_hypertension","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_hypertension.e101ed97a8"}],"documented":true},"last_condition_date":{"description":"Date of most recent condition diagnosis for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"last_condition_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","row_condition":"last_condition_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_last_condition_date__current_date___1900_01_01_date__last_condition_date_is_not_null.8d4e9038fa"}],"documented":true},"patient_id":{"description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_patient_id.a53ab7d4d1"},{"kind":"unique","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_int_patients__with_conditions_patient_id.dbb08a220b"}],"documented":true},"total_conditions":{"description":"Total number of conditions ever diagnosed for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_conditions","min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_total_conditions__0.b1764f2a6e"},{"kind":"not_null","kwargs":{"column_name":"total_conditions","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) 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materialized='view'\n    )\n}}\n\n{#\n    Patient condition history with chronic disease flags.\n\n    IMPORTANT: Chronic Condition Detection Limitations\n    --------------------------------------------------\n    The chronic condition flags (has_diabetes, has_hypertension, has_heart_disease)\n    use simplified text matching on condition_description. This is NOT clinical-grade\n    detection and should be used for analytics/reporting only, not clinical decisions.\n\n    Detection patterns:\n    - Diabetes: description contains 'diabetes'\n    - Hypertension: description contains 'hypertension' or 'high blood pressure'\n    - Heart disease: description contains 'heart disease', 'cardiac', 'coronary',\n                     or 'atrial fibrillation'\n\n    For production clinical use, consider:\n    1. Using SNOMED codes instead of text matching\n    2. Implementing ICD-10 code mappings\n    3. Consulting clinical informaticists for proper condition groupings\n    4. Moving patterns to a seed file or reference table for maintainability\n#}\n\nwith patients as (\n    select\n        patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from {{ ref('stg_synthea__conditions') }}\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.int_patients__with_conditions","construct":"left_join[final:condition_stats]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"final — LEFT JOIN condition_stats ON patients.patient_id = condition_stats.patient_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"final","sketches":["- input: ref('dim_patients')\n  rows:\n    - {patient_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__conditions')\n  rows:\n    - {patient_id: 1}\n# expect: the no-match row with NULL stg_synthea__conditions columns (or the intended fallback)"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final select)\u001f*":{"start":{"line":30,"col":8,"byte":542},"end":{"line":30,"col":9,"byte":543}},"grid\u001fmonth_actual":{"start":{"line":23,"col":11,"byte":423},"end":{"line":23,"col":30,"byte":442}},"grid\u001fmonth_key":{"start":{"line":21,"col":14,"byte":312},"end":{"line":21,"col":85,"byte":383}},"grid\u001fmonth_name":{"start":{"line":24,"col":11,"byte":453},"end":{"line":24,"col":28,"byte":470}},"grid\u001fstate":{"start":{"line":25,"col":11,"byte":481},"end":{"line":25,"col":23,"byte":493}},"grid\u001fyear_actual":{"start":{"line":22,"col":11,"byte":394},"end":{"line":22,"col":29,"byte":412}},"months\u001fmonth_actual":{"start":{"line":8,"col":11,"byte":71},"end":{"line":8,"col":23,"byte":83}},"months\u001fmonth_name":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":21,"byte":104}},"months\u001fyear_actual":{"start":{"line":7,"col":9,"byte":49},"end":{"line":7,"col":20,"byte":60}},"states\u001fstate":{"start":{"line":14,"col":21,"byte":183},"end":{"line":14,"col":26,"byte":188}}}},"column_lineage":{"context":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","rows":null,"format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv"}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. 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condition_end_date\n\n        -- condition details\n        , CODE as condition_code\n        , DESCRIPTION as condition_description\n    from source\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_start_date as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as condition_id\n        , *\n    from renamed\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","node_spans":{"(final 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as gender\n        , SPECIALITY as specialty\n\n        -- location\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , UTILIZATION as utilization\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_encounter_summary","path":"models/marts/analytics/views/v_encounter_summary.sql","description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full 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provider_name\n    from {{ ref('dim_providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_name\n    from {{ ref('dim_organizations') }}\n),\n\npayers as (\n    select\n        payer_key\n        , payer_name\n    from {{ ref('dim_payers') }}\n),\n\nfinal as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_patient_current_conditions","path":"models/marts/analytics/views/v_patient_current_conditions.sql","description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient 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patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from 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ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, 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Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","tests":[],"is_recursive":false}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). 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identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at <= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_dq_quarantine__medications":{"description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"end_after_1900_if_present","description":"Flag: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"no_future_medication_dates","description":"Flag: cast(medication_start_at as date) <= current_date"},{"name":"start_after_1900","description":"Flag: medication_start_at >= timestamp '1900-01-01'"},{"name":"valid_medication_dates","description":"Flag: medication_end_at is null or medication_end_at >= medication_start_at"}]},"int_encounters__enriched":{"description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","materialized":"view","columns":[{"name":"condition_count","description":"Number of conditions diagnosed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID), sourced from fct_encounters","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_count","description":"Number of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_count","description":"Number of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_condition_cost","description":"Total cost associated with conditions (currently always 0 per model logic)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_cost","description":"Total cost of all clinical events (conditions + medications + procedures). Note: Condition costs are currently set to 0 in the model.\n","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_count","description":"Total count of all clinical events (conditions + medications + procedures)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_medication_cost","description":"Total cost of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_procedure_cost","description":"Total cost of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"int_patients__with_conditions":{"description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","materialized":"view","columns":[{"name":"active_conditions","description":"Number of currently active conditions (no end date)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"chronic_condition_count","description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"first_condition_date","description":"Date of earliest condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"has_diabetes","description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_heart_disease","description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_hypertension","description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_condition_date","description":"Date of most recent condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_conditions","description":"Total number of conditions ever diagnosed for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__conditions":{"description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"condition_code","description":"SNOMED CT code for the condition","tests":[{"name":"not null"}]},{"name":"condition_description","description":"Description of the condition","tests":[{"name":"not null"}]},{"name":"condition_end_date","description":"Date when condition resolved (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"condition_id","description":"Surrogate key for the condition record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_start_date","description":"Date when condition was diagnosed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__observations":{"description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"encounter_id","description":"Foreign key to the encounter. Note: ~10% of observations have null encounter_id (from external sources or historical data imports).\n","tests":[{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"observation_at","description":"Timestamp when observation was recorded","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"observation_code","description":"LOINC code for the observation","tests":[{"name":"not null"}]},{"name":"observation_description","description":"Description of the observation","tests":[{"name":"not null"}]},{"name":"observation_id","description":"Surrogate key for the observation record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"observation_type","description":"Type of observation (numeric, text, etc.)"},{"name":"observation_value","description":"Value of the observation (stored as string)"},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"units","description":"Units of measurement"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__organizations":{"description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Unique organization identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__procedures":{"description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost of the procedure","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"procedure_at","description":"Timestamp when the procedure was performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_code","description":"SNOMED CT code for the procedure","tests":[{"name":"not null"}]},{"name":"procedure_description","description":"Description of the procedure","tests":[{"name":"not null"}]},{"name":"procedure_id","description":"Surrogate key for the procedure record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of the procedure"},{"name":"reason_description","description":"Description of the reason for the procedure"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__providers":{"description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Foreign key to the provider's organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"provider_id","description":"Unique provider identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
 <script>
 
 /* cute-dbt shared appearance engine v1 (cute-dbt#242)
diff --git a/examples/explore/tests.html b/examples/explore/tests.html
index 77a9eb0..8bb930c 100644
--- a/examples/explore/tests.html
+++ b/examples/explore/tests.html
@@ -1144,7 +1144,7 @@ <h2>v_provider_active_patients</h2>
 
 
 
-<script type="application/json" id="cute-dbt-data">{"baseline":"","models":[{"name":"dim_conditions","path":"models/marts/analytics/dim_conditions.sql","description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"condition_aggregates","role":"transform"},{"id":"deduped","role":"transform"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_conditions.sql","role":"final"}],"edges":[{"from":"conditions_source","to":"condition_aggregates","edge_type":"from"},{"from":"condition_aggregates","to":"deduped","edge_type":"from"},{"from":"deduped","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_aggregates":"condition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n)","conditions_source":"conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n)","deduped":"deduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n)","final":"final as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n)","unknown_member":"unknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n)"},"code_map":{"compiled":"\n\n\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":105,"col":1,"byte":2681},"end":{"line":105,"col":20,"byte":2700}},"condition_aggregates":{"start":{"line":18,"col":1,"byte":420},"end":{"line":28,"col":2,"byte":854}},"conditions_source":{"start":{"line":5,"col":6,"byte":9},"end":{"line":16,"col":2,"byte":417}},"deduped":{"start":{"line":31,"col":1,"byte":917},"end":{"line":44,"col":2,"byte":1295}},"final":{"start":{"line":79,"col":1,"byte":2118},"end":{"line":103,"col":2,"byte":2679}},"sequenced":{"start":{"line":46,"col":1,"byte":1298},"end":{"line":65,"col":2,"byte":1735}},"unknown_member":{"start":{"line":68,"col":1,"byte":1795},"end":{"line":77,"col":2,"byte":2115}}},"column_spans":{"(final select)\u001f*":{"start":{"line":105,"col":8,"byte":2688},"end":{"line":105,"col":9,"byte":2689}},"condition_aggregates\u001fcondition_code":{"start":{"line":20,"col":9,"byte":465},"end":{"line":20,"col":23,"byte":479}},"condition_aggregates\u001fcondition_description":{"start":{"line":21,"col":11,"byte":490},"end":{"line":21,"col":32,"byte":511}},"condition_aggregates\u001fencounter_count":{"start":{"line":25,"col":11,"byte":727},"end":{"line":25,"col":58,"byte":774}},"condition_aggregates\u001ffirst_occurrence_date":{"start":{"line":22,"col":11,"byte":522},"end":{"line":22,"col":61,"byte":572}},"condition_aggregates\u001flast_occurrence_date":{"start":{"line":23,"col":11,"byte":583},"end":{"line":23,"col":90,"byte":662}},"condition_aggregates\u001fpatient_count":{"start":{"line":24,"col":11,"byte":673},"end":{"line":24,"col":54,"byte":716}},"conditions_source\u001fcondition_code":{"start":{"line":8,"col":14,"byte":139},"end":{"line":8,"col":58,"byte":183}},"conditions_source\u001fcondition_description":{"start":{"line":9,"col":11,"byte":194},"end":{"line":9,"col":32,"byte":215}},"conditions_source\u001fcondition_end_date":{"start":{"line":13,"col":11,"byte":301},"end":{"line":13,"col":29,"byte":319}},"conditions_source\u001fcondition_start_date":{"start":{"line":12,"col":11,"byte":270},"end":{"line":12,"col":31,"byte":290}},"conditions_source\u001fencounter_id":{"start":{"line":11,"col":11,"byte":247},"end":{"line":11,"col":23,"byte":259}},"conditions_source\u001fpatient_id":{"start":{"line":10,"col":11,"byte":226},"end":{"line":10,"col":21,"byte":236}},"deduped\u001fcondition_code":{"start":{"line":33,"col":9,"byte":949},"end":{"line":33,"col":23,"byte":963}},"deduped\u001fcondition_description":{"start":{"line":34,"col":11,"byte":974},"end":{"line":34,"col":32,"byte":995}},"deduped\u001fencounter_count":{"start":{"line":38,"col":11,"byte":1093},"end":{"line":38,"col":26,"byte":1108}},"deduped\u001ffirst_occurrence_date":{"start":{"line":35,"col":11,"byte":1006},"end":{"line":35,"col":32,"byte":1027}},"deduped\u001flast_occurrence_date":{"start":{"line":36,"col":11,"byte":1038},"end":{"line":36,"col":31,"byte":1058}},"deduped\u001fpatient_count":{"start":{"line":37,"col":11,"byte":1069},"end":{"line":37,"col":24,"byte":1082}},"deduped\u001frn":{"start":{"line":39,"col":11,"byte":1119},"end":{"line":42,"col":16,"byte":1263}},"sequenced\u001fcondition_code":{"start":{"line":52,"col":11,"byte":1496},"end":{"line":52,"col":25,"byte":1510}},"sequenced\u001fcondition_description":{"start":{"line":55,"col":11,"byte":1544},"end":{"line":55,"col":32,"byte":1565}},"sequenced\u001fcondition_key":{"start":{"line":49,"col":9,"byte":1401},"end":{"line":49,"col":69,"byte":1461}},"sequenced\u001fencounter_count":{"start":{"line":61,"col":11,"byte":1683},"end":{"line":61,"col":26,"byte":1698}},"sequenced\u001ffirst_occurrence_date":{"start":{"line":56,"col":11,"byte":1576},"end":{"line":56,"col":32,"byte":1597}},"sequenced\u001flast_occurrence_date":{"start":{"line":57,"col":11,"byte":1608},"end":{"line":57,"col":31,"byte":1628}},"sequenced\u001fpatient_count":{"start":{"line":60,"col":11,"byte":1659},"end":{"line":60,"col":24,"byte":1672}},"unknown_member\u001fcondition_code":{"start":{"line":71,"col":11,"byte":1864},"end":{"line":71,"col":38,"byte":1891}},"unknown_member\u001fcondition_description":{"start":{"line":72,"col":11,"byte":1902},"end":{"line":72,"col":55,"byte":1946}},"unknown_member\u001fcondition_key":{"start":{"line":70,"col":10,"byte":1835},"end":{"line":70,"col":28,"byte":1853}},"unknown_member\u001fencounter_count":{"start":{"line":76,"col":11,"byte":2093},"end":{"line":76,"col":31,"byte":2113}},"unknown_member\u001ffirst_occurrence_date":{"start":{"line":73,"col":16,"byte":1962},"end":{"line":73,"col":54,"byte":2000}},"unknown_member\u001flast_occurrence_date":{"start":{"line":74,"col":16,"byte":2016},"end":{"line":74,"col":53,"byte":2053}},"unknown_member\u001fpatient_count":{"start":{"line":75,"col":11,"byte":2064},"end":{"line":75,"col":29,"byte":2082}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"condition_code":{"description":"ICD/SNOMED code (natural key)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_code.193c903fa2"},{"kind":"unique","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_code.99cd9f3457"}],"documented":true},"condition_description":{"description":"Full condition name/description","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_description","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_description.dc79ef5b8c"}],"documented":true},"condition_key":{"description":"Surrogate key (row_number)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_key.5e35e4562f"},{"kind":"unique","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"}],"documented":true},"encounter_count":{"description":"Total number of encounters with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_encounter_count__0.9a92420a5b"}],"documented":true},"first_occurrence_date":{"description":"Earliest date this condition was diagnosed in the dataset","documented":true},"last_occurrence_date":{"description":"Most recent date this condition was diagnosed","documented":true},"patient_count":{"description":"Number of unique patients with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_patient_count__0.4c1de369ac"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_start_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_start_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_end_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_end_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}]},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":409,"col":1,"byte":12325},"end":{"line":409,"col":20,"byte":12344}},"date_spine":{"start":{"line":5,"col":6,"byte":9},"end":{"line":333,"col":2,"byte":8986}},"final":{"start":{"line":335,"col":1,"byte":8989},"end":{"line":407,"col":2,"byte":12323}}},"column_spans":{"(final select)\u001f*":{"start":{"line":409,"col":8,"byte":12332},"end":{"line":409,"col":9,"byte":12333}},"date_spine\u001fdate_day":{"start":{"line":178,"col":9,"byte":3934},"end":{"line":178,"col":19,"byte":3944}},"date_spine\u001fday_of_month":{"start":{"line":193,"col":9,"byte":4584},"end":{"line":193,"col":53,"byte":4628}},"date_spine\u001fday_of_week":{"start":{"line":188,"col":14,"byte":4285},"end":{"line":188,"col":73,"byte":4344}},"date_spine\u001fday_of_week_iso":{"start":{"line":190,"col":14,"byte":4394},"end":{"line":190,"col":76,"byte":4456}},"date_spine\u001fday_of_week_name":{"start":{"line":191,"col":9,"byte":4466},"end":{"line":191,"col":48,"byte":4505}},"date_spine\u001fday_of_week_name_short":{"start":{"line":192,"col":16,"byte":4522},"end":{"line":192,"col":68,"byte":4574}},"date_spine\u001fday_of_year":{"start":{"line":194,"col":9,"byte":4638},"end":{"line":194,"col":58,"byte":4687}},"date_spine\u001fiso_week_end_date":{"start":{"line":239,"col":11,"byte":5901},"end":{"line":239,"col":118,"byte":6008}},"date_spine\u001fiso_week_of_year":{"start":{"line":247,"col":10,"byte":6319},"end":{"line":247,"col":71,"byte":6380}},"date_spine\u001fiso_week_start_date":{"start":{"line":236,"col":14,"byte":5812},"end":{"line":236,"col":76,"byte":5874}},"date_spine\u001fiso_year_week":{"start":{"line":248,"col":9,"byte":6390},"end":{"line":267,"col":25,"byte":7034}},"date_spine\u001fmonth_end_date":{"start":{"line":287,"col":6,"byte":7592},"end":{"line":288,"col":44,"byte":7746}},"date_spine\u001fmonth_name":{"start":{"line":278,"col":9,"byte":7373},"end":{"line":278,"col":44,"byte":7408}},"date_spine\u001fmonth_name_short":{"start":{"line":279,"col":16,"byte":7425},"end":{"line":279,"col":64,"byte":7473}},"date_spine\u001fmonth_of_year":{"start":{"line":276,"col":13,"byte":7295},"end":{"line":277,"col":27,"byte":7363}},"date_spine\u001fmonth_start_date":{"start":{"line":281,"col":14,"byte":7489},"end":{"line":281,"col":74,"byte":7549}},"date_spine\u001fnext_date_day":{"start":{"line":184,"col":6,"byte":4064},"end":{"line":184,"col":80,"byte":4138}},"date_spine\u001fprior_date_day":{"start":{"line":181,"col":6,"byte":3966},"end":{"line":181,"col":82,"byte":4042}},"date_spine\u001fprior_year_date_day":{"start":{"line":185,"col":9,"byte":4148},"end":{"line":185,"col":53,"byte":4192}},"date_spine\u001fprior_year_iso_week_end_date":{"start":{"line":244,"col":11,"byte":6152},"end":{"line":245,"col":40,"byte":6299}},"date_spine\u001fprior_year_iso_week_of_year":{"start":{"line":272,"col":10,"byte":7165},"end":{"line":273,"col":39,"byte":7266}},"date_spine\u001fprior_year_iso_week_start_date":{"start":{"line":240,"col":14,"byte":6023},"end":{"line":241,"col":42,"byte":6125}},"date_spine\u001fprior_year_month_end_date":{"start":{"line":299,"col":6,"byte":7922},"end":{"line":301,"col":39,"byte":8107}},"date_spine\u001fprior_year_month_start_date":{"start":{"line":291,"col":13,"byte":7775},"end":{"line":292,"col":41,"byte":7866}},"date_spine\u001fprior_year_over_year_date_day":{"start":{"line":186,"col":9,"byte":4202},"end":{"line":186,"col":40,"byte":4233}},"date_spine\u001fprior_year_week_end_date":{"start":{"line":229,"col":6,"byte":5494},"end":{"line":233,"col":36,"byte":5726}},"date_spine\u001fprior_year_week_of_year":{"start":{"line":269,"col":19,"byte":7055},"end":{"line":270,"col":35,"byte":7145}},"date_spine\u001fprior_year_week_start_date":{"start":{"line":218,"col":6,"byte":5231},"end":{"line":222,"col":38,"byte":5418}},"date_spine\u001fquarter_end_date":{"start":{"line":315,"col":6,"byte":8409},"end":{"line":316,"col":46,"byte":8567}},"date_spine\u001fquarter_of_year":{"start":{"line":304,"col":13,"byte":8136},"end":{"line":305,"col":29,"byte":8208}},"date_spine\u001fquarter_start_date":{"start":{"line":307,"col":13,"byte":8236},"end":{"line":308,"col":32,"byte":8309}},"date_spine\u001fweek_end_date":{"start":{"line":210,"col":6,"byte":4975},"end":{"line":213,"col":70,"byte":5167}},"date_spine\u001fweek_of_year":{"start":{"line":234,"col":19,"byte":5746},"end":{"line":234,"col":69,"byte":5796}},"date_spine\u001fweek_start_date":{"start":{"line":200,"col":6,"byte":4752},"end":{"line":203,"col":25,"byte":4899}},"date_spine\u001fyear_end_date":{"start":{"line":327,"col":6,"byte":8777},"end":{"line":328,"col":43,"byte":8928}},"date_spine\u001fyear_number":{"start":{"line":319,"col":13,"byte":8596},"end":{"line":320,"col":25,"byte":8661}},"date_spine\u001fyear_start_date":{"start":{"line":321,"col":14,"byte":8676},"end":{"line":321,"col":72,"byte":8734}},"final\u001fdate_actual":{"start":{"line":341,"col":11,"byte":9213},"end":{"line":341,"col":34,"byte":9236}},"final\u001fdate_key":{"start":{"line":338,"col":14,"byte":9098},"end":{"line":338,"col":94,"byte":9178}},"final\u001fday_name":{"start":{"line":345,"col":11,"byte":9355},"end":{"line":345,"col":39,"byte":9383}},"final\u001fday_name_short":{"start":{"line":346,"col":11,"byte":9394},"end":{"line":346,"col":51,"byte":9434}},"final\u001fday_of_month":{"sta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actual date value","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"date_actual","max_value":"'2025-12-31'::date","min_value":"'1909-01-01'::date","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_date_date_actual___2025_12_31_date___1909_01_01_date.f3b5c5d47b"},{"kind":"not_null","kwargs":{"column_name":"date_actual","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_date_actual.85284edc63"},{"kind":"unique","kwargs":{"column_name":"date_actual","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.unique_dim_date_date_actual.107fe1b6a2"}],"documented":true},"date_key":{"description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"date_key","max_value":20251231,"min_value":19090101,"model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_date_date_key__20251231__19090101.ddd369949a"},{"kind":"not_null","kwargs":{"column_name":"date_key","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_date_key.881d0a31b6"},{"kind":"unique","kwargs":{"column_name":"date_key","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.unique_dim_date_date_key.0869fd48f9"}],"documented":true},"day_name":{"description":"Full day name (e.g., Monday, Tuesday)","tests":[{"kind":"not_null","kwargs":{"column_name":"day_name","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_day_name.3a968349a4"}],"documented":true},"day_name_short":{"description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"kind":"not_null","kwargs":{"column_name":"day_name_short","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_day_name_short.ddec7c56fd"}],"documented":true},"day_of_month":{"description":"Day of the month (1-31)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"day_of_month","max_value":31,"min_value":1,"model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_date_day_of_month__31__1.95bef47e40"},{"kind":"not_null","kwargs":{"column_name":"day_of_month","model":"{{ get_where_subquery(ref('dim_date')) 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)\n}}\n\n{#\n    Calendar dimension covering 1909-01-01 to 2025-12-31.\n\n    Note on date_key: Uses YYYYMMDD integer format. 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organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n  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as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final 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organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from 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as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , 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      -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as 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Each row represents a\n    specific version of a patient's record.\n\n    SCD Type 2 Columns:\n    - patient_key: Surrogate key (unique per version)\n    - patient_id: Natural key (same across versions)\n    - valid_from: When this version became active\n    - valid_to: When this version was superseded (null = current)\n    - is_current: True for the active version\n\n    To query current patients only:\n        WHERE is_current = true\n\n    To query patient state at a point in time:\n        WHERE valid_from <= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_patients","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__patients')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}]},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as 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      -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total 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get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme 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Health"}},{"display":"555-0100","key":{"t":"str","v":"555-0100"}},{"display":"1250.0","key":{"t":"number","v":"1250"}},{"display":"MA","key":{"t":"str","v":"MA"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"8","key":{"t":"number","v":"8"}},{"display":"02101","key":{"t":"str","v":"02101"}}]}]},"rows":[{"address":"1 Acme Way","amount_covered":1000.0,"amount_uncovered":250.0,"average_quality_of_life_score":0.85,"city":"Boston","covered_encounters":10,"covered_immunizations":4,"covered_medications":5,"covered_procedures":3,"member_months":96,"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_name":"Acme Health","phone":"555-0100","revenue":1250.0,"state":"MA","uncovered_encounters":2,"uncovered_immunizations":0,"uncovered_medications":1,"uncovered_procedures":0,"unique_customers":8,"zip_code":"02101"}],"format":"dict","column_meta":{"address":{"description":"Street address"},"amount_covered":{"description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"amount_uncovered":{"description":"Total amount not covered by payer"},"average_quality_of_life_score":{"description":"Average quality of life score for members"},"city":{"description":"City"},"covered_encounters":{"description":"Number of encounters covered"},"covered_immunizations":{"description":"Number of immunizations covered"},"covered_medications":{"description":"Number of medications covered"},"covered_procedures":{"description":"Number of procedures covered"},"member_months":{"description":"Total member months"},"payer_id":{"description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]},"phone":{"description":"Phone number"},"revenue":{"description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"state":{"description":"State where headquartered"},"uncovered_encounters":{"description":"Number of encounters not covered"},"uncovered_immunizations":{"description":"Number of immunizations not covered"},"uncovered_medications":{"description":"Number of medications not covered"},"uncovered_procedures":{"description":"Number of procedures not covered"},"unique_customers":{"description":"Number of unique customers"},"zip_code":{"description":"ZIP code (stored as numeric, 5 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\"memory\".\"main_marts\".\"dim_organizations\"\n)","providers":"providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n)","unknown_member":"unknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='provider_key'\n    )\n}}\n\nwith providers as (\n    select * from {{ ref('stg_synthea__providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from {{ ref('dim_organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_providers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"]},"evidence":[{"label":"unique_key","value":"provider_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_providers","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('dim_organizations'), ref('stg_synthea__providers')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. 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cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_encounter_id.5c743b1ef7"}],"documented":true},"encounter_key":{"description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_encounter_key.cc0e40eeb8"},{"kind":"relationships","kwargs":{"column_name":"encounter_key","field":"encounter_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","to":"ref('fct_encounters')"},"node_id":"test.healthcare_analytics.relationships_fct_clinical_events_encounter_key__encounter_key__ref_fct_encounters_.7a30909f42"}],"documented":true},"event_cost":{"description":"Cost of the clinical event","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"event_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","row_condition":"event_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_clinical_events_event_cost__0__event_cost_is_not_null.2c2582a25b"}],"documented":true},"event_date_key":{"description":"Foreign key to dim_date for event start","tests":[{"kind":"not_null","kwargs":{"column_name":"event_date_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_event_date_key.d605213965"},{"kind":"relationships","kwargs":{"column_name":"event_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_clinical_events_event_date_key__date_key__ref_dim_date_.0995b8993f"}],"documented":true},"event_end_date":{"description":"End date of the clinical event (null if ongoing or 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unique_key='clinical_event_key'\n    )\n}}\n\nwith valid_encounters as (\n    select encounter_id\n    from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from {{ ref('stg_synthea__conditions') }} as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from {{ ref('stg_synthea__medications') }} as med\n    {{ quarantine_filter() }}\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from {{ ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as 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mod","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_condition_cohorts","path":"models/marts/analytics/fct_condition_cohorts.sql","description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"patients","role":"import"},{"id":"dim_conditions","role":"import"},{"id":"condition_windows","role":"transform"},{"id":"with_dimensions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final 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end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n)","conditions_source":"conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n)","dim_conditions":"dim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","with_dimensions":"with_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n)"},"code_map":{"compiled":"\n\n\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","node_spans":{"(final 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when record was loaded","documented":true},"condition_code":{"description":"ICD/SNOMED condition code","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_condition_cohorts_condition_code.180142995a"}],"documented":true},"condition_cohort_key":{"description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_cohort_key","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_condition_cohorts_condition_cohort_key.1edb245ae1"},{"kind":"unique","kwargs":{"column_name":"condition_cohort_key","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"}],"documented":true},"condition_description":{"description":"Full condition 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unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}]},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as 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encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","node_spans":{"(final 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when record was loaded","documented":true},"cost_analysis_key":{"description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"kind":"not_null","kwargs":{"column_name":"cost_analysis_key","model":"{{ get_where_subquery(ref('fct_cost_analysis')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_cost_analysis_cost_analysis_key.a72ad66418"},{"kind":"unique","kwargs":{"column_name":"cost_analysis_key","model":"{{ get_where_subquery(ref('fct_cost_analysis')) }}"},"node_id":"test.healthcare_analytics.unique_fct_cost_analysis_cost_analysis_key.2aecf62159"}],"documented":true},"cost_per_procedure":{"description":"Average cost per procedure (null if no procedures)","documented":true},"coverage_pct":{"description":"Percentage covered by insurance (0-100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('fct_cost_analysis')) 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unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end 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This finding's evidence carries a copy-pasteable given sketch.","pin_node":"encounters_enriched","sketches":["- input: ref('fct_encounters')\n  rows:\n    - {encounter_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__procedures')\n  rows:\n    - {encounter_id: 1}\n# expect: the no-match row with NULL stg_synthea__procedures columns (or the intended fallback)"]}]},{"name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. 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timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final select)\u001f*":{"start":{"line":109,"col":8,"byte":2736},"end":{"line":109,"col":9,"byte":2737}},"dim_date\u001fdate_actual":{"start":{"line":45,"col":11,"byte":721},"end":{"line":45,"col":22,"byte":732}},"dim_date\u001fdate_key":{"start":{"line":44,"col":9,"byte":702},"end":{"line":44,"col":17,"byte":710}},"encounters\u001f*":{"start":{"line":4,"col":12,"byte":34},"end":{"line":4,"col":13,"byte":35}},"final\u001f_loaded_at":{"start":{"line":98,"col":11,"byte":2250},"end":{"line":98,"col":42,"byte":2281}},"final\u001fbase_encounter_cost":{"start":{"line":86,"col":11,"byte":1836},"end":{"line":86,"col":32,"byte":1857}},"final\u001fduration_minutes":{"start":{"line":83,"col":11,"byte":1726},"end":{"line":83,"col":92,"byte":1807}},"final\u001fencounter_class":{"start":{"line":72,"col":11,"byte":1470},"end":{"line":72,"col":28,"byte":1487}},"final\u001fencounter_code":{"start":{"line":73,"col":11,"byte":1498},"end":{"line":73,"col":27,"byte":1514}},"final\u001fencounter_description":{"start":{"line":74,"col":11,"byte":1525},"end":{"line":74,"col":34,"byte":1548}},"final\u001fencounter_end_at":{"start":{"line":80,"col":11,"byte":1668},"end":{"line":80,"col":29,"byte":1686}},"final\u001fencounter_end_date_key":{"start":{"line":69,"col":11,"byte":1385},"end":{"line":69,"col":62,"byte":1436}},"final\u001fencounter_id":{"start":{"line":55,"col":11,"byte":929},"end":{"line":55,"col":25,"byte":943}},"final\u001fencounter_key":{"start":{"line":52,"col":9,"byte":834},"end":{"line":52,"col":69,"byte":894}},"final\u001fencounter_start_at":{"start":{"line":79,"col":11,"byte":1637},"end":{"line":79,"col":31,"byte":1657}},"final\u001fencounter_start_date_key":{"start":{"line":68,"col":11,"byte":1321},"end":{"line":68,"col":64,"byte":1374}},"final\u001forganization_id":{"start":{"line":63,"col":11,"byte":1200},"end":{"line":63,"col":28,"byte":1217}},"final\u001forganization_key":{"start":{"line":62,"col":11,"byte":1137},"end":{"line":62,"col":63,"byte":1189}},"final\u001fpatient_age_at_encounter":{"start":{"line":92,"col":11,"byte":2047},"end":{"line":95,"col":40,"byte":2218}},"final\u001fpatient_id":{"start":{"line":59,"col":11,"byte":1034},"end":{"line":59,"col":23,"byte":1046}},"final\u001fpatient_key":{"start":{"line":58,"col":11,"byte":981},"end":{"line":58,"col":53,"byte":1023}},"final\u001fpatient_responsibility":{"start":{"line":89,"col":11,"byte":1924},"end":{"line":89,"col":87,"byte":2000}},"final\u001fpayer_coverage":{"start":{"line":88,"col":11,"byte":1897},"end":{"line":88,"col":27,"byte":1913}},"final\u001fpayer_id":{"start":{"line":65,"col":11,"byte":1278},"end":{"line":65,"col":21,"byte":1288}},"final\u001fpayer_key":{"start":{"line":64,"col":11,"byte":1228},"end":{"line":64,"col":50,"byte":1267}},"final\u001fprovider_id":{"start":{"line":61,"col":11,"byte":1113},"end":{"line":61,"col":24,"byte":1126}},"final\u001fprovider_key":{"start":{"line":60,"col":11,"byte":1057},"end":{"line":60,"col":56,"byte":1102}},"final\u001freason_code":{"start":{"line":75,"col":11,"byte":1559},"end":{"line":75,"col":24,"byte":1572}},"final\u001freason_description":{"start":{"line":76,"col":11,"byte":1583},"end":{"line":76,"col":31,"byte":1603}},"final\u001ftotal_claim_cost":{"start":{"line":87,"col":11,"byte":1868},"end":{"line":87,"col":29,"byte":1886}},"organizations\u001forganization_id":{"start":{"line":31,"col":11,"byte":490},"end":{"line":31,"col":26,"byte":505}},"organizations\u001forganization_key":{"start":{"line":30,"col":9,"byte":463},"end":{"line":30,"col":25,"byte":479}},"patients\u001fbirth_date":{"start":{"line":16,"col":11,"byte":216},"end":{"line":16,"col":21,"byte":226}},"patients\u001fpatient_id":{"start":{"line":15,"col":11,"byte":195},"end":{"line":15,"col":21,"byte":205}},"patients\u001fpatient_key":{"start":{"line":14,"col":9,"byte":173},"end":{"line":14,"col":20,"byte":184}},"payers\u001fpayer_id":{"start":{"line":38,"col":11,"byte":612},"end":{"line":38,"col":19,"byte":620}},"payers\u001fpayer_key":{"start":{"line":37,"col":9,"byte":592},"end":{"line":37,"col":18,"byte":601}},"providers\u001fprovider_id":{"start":{"line":24,"col":11,"byte":362},"end":{"line":24,"col":22,"byte":373}},"providers\u001fprovider_key":{"start":{"line":23,"col":9,"byte":339},"end":{"line":23,"col":21,"byte":351}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"base_encounter_cost":{"description":"Base cost of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"base_encounter_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"base_encounter_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_base_encounter_cost__0__base_encounter_cost_is_not_null.a096f214be"}],"documented":true},"duration_minutes":{"description":"Duration of encounter in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_duration_minutes__0__duration_minutes_is_not_null.ce51123f28"}],"documented":true},"encounter_class":{"description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.59f26de6a5"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_class.9419876296"}],"documented":true},"encounter_code":{"description":"SNOMED CT code for the encounter type","documented":true},"encounter_description":{"description":"Description of the encounter type","documented":true},"encounter_end_at":{"description":"End timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_end_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"encounter_end_at is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_end_at__current_timestamp___1900_01_01_timestamp__encounter_end_at_is_not_null.393dec12f4"}],"documented":true},"encounter_end_date_key":{"description":"Foreign key to dim_date for encounter end","documented":true},"encounter_id":{"description":"Natural encounter identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_id.f7111b4eea"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_id.ce88bae928"}],"documented":true},"encounter_key":{"description":"Surrogate key for the encounter","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_key.4fa2f03d5e"},{"kind":"unique","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"}],"documented":true},"encounter_start_at":{"description":"Start timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_start_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_start_at__current_timestamp___1900_01_01_timestamp.f32b839e27"},{"kind":"not_null","kwargs":{"column_name":"encounter_start_at","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_at.c47cc214ac"}],"documented":true},"encounter_start_date_key":{"description":"Foreign key to dim_date for encounter start","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_start_date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_date_key.73ce57ba43"},{"kind":"relationships","kwargs":{"column_name":"encounter_start_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_encounter_start_date_key__date_key__ref_dim_date_.4f1d88383c"}],"documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_id.acfb633ecb"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_key.815fa1363c"},{"kind":"relationships","kwargs":{"column_name":"organization_key","field":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_organizations')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_organization_key__organization_key__ref_dim_organizations_.5feb1b3ad6"}],"documented":true},"patient_age_at_encounter":{"description":"Patient's age at the time of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_age_at_encounter","max_value":150,"min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_age_at_encounter is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_age_at_encounter__150__0__patient_age_at_encounter_is_not_null.bbd237256e"}],"documented":true},"patient_id":{"description":"Natural patient identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_id.9e325e7ac6"}],"documented":true},"patient_key":{"description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_key.e5abbfbe29"},{"kind":"relationships","kwargs":{"column_name":"patient_key","field":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_patients')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_patient_key__patient_key__ref_dim_patients_.ad1c292e28"}],"documented":true},"patient_responsibility":{"description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_responsibility","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_responsibility is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_responsibility__0__patient_responsibility_is_not_null.7a61f00fd0"}],"documented":true},"payer_coverage":{"description":"Amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"payer_coverage","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"payer_coverage is not 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This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"fct_encounters_monthly","path":"models/marts/core/fct_encounters_monthly.sql","description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters_monthly.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"encounters_with_date","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":53,"col":1,"byte":1374},"end":{"line":53,"col":20,"byte":1393}},"dim_date":{"start":{"line":16,"col":1,"byte":301},"end":{"line":22,"col":2,"byte":431}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":298}},"encounters_with_date":{"start":{"line":24,"col":1,"byte":434},"end":{"line":34,"col":2,"byte":790}},"final":{"start":{"line":36,"col":1,"byte":793},"end":{"line":51,"col":2,"byte":1372}}},"column_spans":{"(final select)\u001f*":{"start":{"line":53,"col":8,"byte":1381},"end":{"line":53,"col":9,"byte":1382}},"dim_date\u001fdate_key":{"start":{"line":18,"col":9,"byte":334},"end":{"line":18,"col":17,"byte":342}},"dim_date\u001fmonth_actual":{"start":{"line":20,"col":11,"byte":375},"end":{"line":20,"col":23,"byte":387}},"dim_date\u001fyear_actual":{"start":{"line":19,"col":11,"byte":353},"end":{"line":19,"col":22,"byte":364}},"encounters\u001fduration_minutes":{"start":{"line":12,"col":11,"byte":232},"end":{"line":12,"col":27,"byte":248}},"encounters\u001fencounter_class":{"start":{"line":8,"col":11,"byte":121},"end":{"line":8,"col":26,"byte":136}},"encounters\u001fencounter_id":{"start":{"line":5,"col":9,"byte":42},"end":{"line":5,"col":21,"byte":54}},"encounters\u001fencounter_start_date_key":{"start":{"line":7,"col":11,"byte":86},"end":{"line":7,"col":35,"byte":110}},"encounters\u001fpatient_id":{"start":{"line":6,"col":11,"byte":65},"end":{"line":6,"col":21,"byte":75}},"encounters\u001fpatient_responsibility":{"start":{"line":11,"col":11,"byte":199},"end":{"line":11,"col":33,"byte":221}},"encounters\u001fpayer_coverage":{"start":{"line":10,"col":11,"byte":174},"end":{"line":10,"col":25,"byte":188}},"encounters\u001ftotal_claim_cost":{"start":{"line":9,"col":11,"byte":147},"end":{"line":9,"col":27,"byte":163}},"encounters_with_date\u001f*":{"start":{"line":26,"col":9,"byte":479},"end":{"line":26,"col":12,"byte":482}},"encounters_with_date\u001fmonth_actual":{"start":{"line":28,"col":11,"byte":517},"end":{"line":28,"col":25,"byte":531}},"encounters_with_date\u001fyear_actual":{"start":{"line":27,"col":11,"byte":493},"end":{"line":27,"col":24,"byte":506}},"encounters_with_date\u001fyear_month":{"start":{"line":29,"col":16,"byte":547},"end":{"line":30,"col":72,"byte":654}},"final\u001f_loaded_at":{"start":{"line":48,"col":11,"byte":1241},"end":{"line":48,"col":42,"byte":1272}},"final\u001favg_duration_minutes":{"start":{"line":47,"col":11,"byte":1185},"end":{"line":47,"col":56,"byte":1230}},"final\u001fencounter_class":{"start":{"line":41,"col":11,"byte":889},"end":{"line":41,"col":26,"byte":904}},"final\u001fencounter_count":{"start":{"line":42,"col":11,"byte":915},"end":{"line":42,"col":38,"byte":942}},"final\u001fmonth_actual":{"start":{"line":40,"col":11,"byte":866},"end":{"line":40,"col":23,"byte":878}},"final\u001ftotal_claim_cost":{"start":{"line":44,"col":11,"byte":1009},"end":{"line":44,"col":52,"byte":1050}},"final\u001ftotal_patient_responsibility":{"start":{"line":46,"col":11,"byte":1115},"end":{"line":46,"col":70,"byte":1174}},"final\u001ftotal_payer_coverage":{"start":{"line":45,"col":11,"byte":1061},"end":{"line":45,"col":54,"byte":1104}},"final\u001funique_patients":{"start":{"line":43,"col":11,"byte":953},"end":{"line":43,"col":56,"byte":998}},"final\u001fyear_actual":{"start":{"line":39,"col":11,"byte":844},"end":{"line":39,"col":22,"byte":855}},"final\u001fyear_month":{"start":{"line":38,"col":9,"byte":823},"end":{"line":38,"col":19,"byte":833}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"avg_duration_minutes":{"description":"Average encounter duration in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"avg_duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"avg_duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_avg_duration_minutes__0__avg_duration_minutes_is_not_null.0cea70c94f"}],"documented":true},"encounter_class":{"description":"Type of encounter","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_monthly_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.274b5b6532"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_encounter_class.d503677b5a"}],"documented":true},"encounter_count":{"description":"Number of encounters in the month","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_encounter_count__1.03132101e0"},{"kind":"not_null","kwargs":{"column_name":"encounter_count","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_encounter_count.5046b087e4"}],"documented":true},"month_actual":{"description":"Month number (1-12)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"month_actual","max_value":12,"min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_month_actual__12__1.f650fcabb6"},{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_month_actual.82083ad465"}],"documented":true},"total_claim_cost":{"description":"Sum of total claim costs","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_claim_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_claim_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_claim_cost__0__total_claim_cost_is_not_null.09195ba1c6"}],"documented":true},"total_patient_responsibility":{"description":"Sum of patient responsibility amounts","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_patient_responsibility","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_patient_responsibility is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_patient_responsibility__0__total_patient_responsibility_is_not_null.e824258649"}],"documented":true},"total_payer_coverage":{"description":"Sum of payer coverage amounts","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_payer_coverage","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_payer_coverage is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_payer_coverage__0__total_payer_coverage_is_not_null.1cae11e2af"}],"documented":true},"unique_patients":{"description":"Number of unique patients with encounters","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"unique_patients","min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_unique_patients__1.86f77befe3"},{"kind":"not_null","kwargs":{"column_name":"unique_patients","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) 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   where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , cast(count(*) as decimal(18, 2)) / nullif(count(distinct patient_id), 0) as encounters_per_patient\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_actual, encounter_class\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_yearly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_actual, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"}]},{"name":"fct_patient_summary","path":"models/marts/analytics/fct_patient_summary.sql","description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health 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\"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","conditions_per_patient_year":"conditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from 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e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct 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 select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","node_spans":{"(final 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select)\u001f*":{"start":{"line":143,"col":8,"byte":3621},"end":{"line":143,"col":9,"byte":3622}},"dim_date\u001fdate_key":{"start":{"line":26,"col":9,"byte":485},"end":{"line":26,"col":17,"byte":493}},"dim_date\u001fmonth_actual":{"start":{"line":28,"col":11,"byte":526},"end":{"line":28,"col":23,"byte":538}},"dim_date\u001fyear_actual":{"start":{"line":27,"col":11,"byte":504},"end":{"line":27,"col":22,"byte":515}},"encounters\u001fduration_minutes":{"start":{"line":18,"col":11,"byte":319},"end":{"line":18,"col":27,"byte":335}},"encounters\u001fencounter_id":{"start":{"line":8,"col":11,"byte":68},"end":{"line":8,"col":23,"byte":80}},"encounters\u001fencounter_key":{"start":{"line":7,"col":9,"byte":44},"end":{"line":7,"col":22,"byte":57}},"encounters\u001fencounter_start_date_key":{"start":{"line":15,"col":11,"byte":232},"end":{"line":15,"col":35,"byte":256}},"encounters\u001forganization_id":{"start":{"line":14,"col":11,"byte":206},"end":{"line":14,"col":26,"byte":221}},"encounters\u001forganization_key":{"start":{"line":13,"col":11,"byte":179},"end":{"line":13,"col":27,"byte":195}},"encounters\u001fpatient_age_at_encounter":{"start":{"line":19,"col":11,"byte":346},"end":{"line":19,"col":35,"byte":370}},"encounters\u001fpatient_id":{"start":{"line":10,"col":11,"byte":113},"end":{"line":10,"col":21,"byte":123}},"encounters\u001fpatient_key":{"start":{"line":9,"col":11,"byte":91},"end":{"line":9,"col":22,"byte":102}},"encounters\u001fpayer_coverage":{"start":{"line":17,"col":11,"byte":294},"end":{"line":17,"col":25,"byte":308}},"encounters\u001fprovider_id":{"start":{"line":12,"col":11,"byte":157},"end":{"line":12,"col":22,"byte":168}},"encounters\u001fprovider_key":{"start":{"line":11,"col":11,"byte":134},"end":{"line":11,"col":23,"byte":146}},"encounters\u001ftotal_claim_cost":{"start":{"line":16,"col":11,"byte":267},"end":{"line":16,"col":27,"byte":283}},"encounters_with_date\u001f*":{"start":{"line":43,"col":9,"byte":860},"end":{"line":43,"col":12,"byte":863}},"encounters_with_date\u001fmonth_actual":{"start":{"line":45,"col":11,"byte":898},"end":{"line":45,"col":25,"byte":912}},"encounters_with_date\u001fprocedure_count":{"start":{"line":49,"col":11,"byte":1062},"end":{"line":49,"col":61,"byte":1112}},"encounters_with_date\u001fyear_actual":{"start":{"line":44,"col":11,"byte":874},"end":{"line":44,"col":24,"byte":887}},"encounters_with_date\u001fyear_month_str":{"start":{"line":46,"col":16,"byte":928},"end":{"line":48,"col":79,"byte":1051}},"final\u001f_loaded_at":{"start":{"line":138,"col":11,"byte":3550},"end":{"line":138,"col":42,"byte":3581}},"final\u001favg_cost_per_encounter":{"start":{"line":125,"col":11,"byte":3162},"end":{"line":129,"col":38,"byte":3316}},"final\u001favg_encounter_duration_minutes":{"start":{"line":118,"col":11,"byte":2896},"end":{"line":118,"col":85,"byte":2970}},"final\u001favg_patient_age":{"start":{"line":117,"col":11,"byte":2841},"end":{"line":117,"col":55,"byte":2885}},"final\u001fencounter_count":{"start":{"line":112,"col":11,"byte":2726},"end":{"line":112,"col":26,"byte":2741}},"final\u001fencounters_per_patient_avg":{"start":{"line":131,"col":11,"byte":3328},"end":{"line":135,"col":42,"byte":3518}},"final\u001fmonth_actual":{"start":{"line":108,"col":11,"byte":2649},"end":{"line":108,"col":23,"byte":2661}},"final\u001forganization_id":{"start":{"line":104,"col":11,"byte":2578},"end":{"line":104,"col":26,"byte":2593}},"final\u001forganization_key":{"start":{"line":103,"col":11,"byte":2551},"end":{"line":103,"col":27,"byte":2567}},"final\u001fprovider_id":{"start":{"line":102,"col":11,"byte":2529},"end":{"line":102,"col":22,"byte":2540}},"final\u001fprovider_key":{"start":{"line":101,"col":11,"byte":2506},"end":{"line":101,"col":23,"byte":2518}},"final\u001fprovider_metrics_key":{"start":{"line":98,"col":9,"byte":2386},"end":{"line":98,"col":91,"byte":2468}},"final\u001ftotal_cost":{"start":{"line":121,"col":11,"byte":3009},"end":{"line":121,"col":48,"byte":3046}},"final\u001ftotal_payer_coverage":{"start":{"line":122,"col":11,"byte":3057},"end":{"line":122,"col":68,"byte":3114}},"final\u001ftotal_procedures":{"start":{"line":114,"col":11,"byte":2783},"end":{"line":114,"col":27,"byte":2799}},"final\u001funique_patient_count":{"start":{"line":113,"col":11,"byte":2752},"end":{"line":113,"col":31,"byte":2772}},"final\u001fyear_actual":{"start":{"line":107,"col":11,"byte":2627},"end":{"line":107,"col":22,"byte":2638}},"final\u001fyear_month_str":{"start":{"line":109,"col":11,"byte":2672},"end":{"line":109,"col":25,"byte":2686}},"procedures\u001fencounter_id":{"start":{"line":34,"col":9,"byte":620},"end":{"line":34,"col":21,"byte":632}},"procedures\u001fprocedure_count":{"start":{"line":35,"col":11,"byte":643},"end":{"line":35,"col":38,"byte":670}},"provider_month_agg\u001favg_encounter_duration_minutes":{"start":{"line":75,"col":11,"byte":1828},"end":{"line":75,"col":68,"byte":1885}},"provider_month_agg\u001favg_patient_age":{"start":{"line":72,"col":11,"byte":1737},"end":{"l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   materialized='view'\n    )\n}}\n\nwith encounters as (\n    select\n        encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nconditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from {{ ref('stg_synthea__conditions') }}\n    group by encounter_id\n),\n\nmedications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from {{ ref('stg_synthea__medications') }}\n    {{ quarantine_filter() }}\n    group by encounter_id\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nfinal as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"int_patients__with_conditions","path":"models/intermediate/healthcare/int_patients__with_conditions.sql","description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","dag":{"nodes":[{"id":"patients","role":"import"},{"id":"conditions","role":"import"},{"id":"condition_stats","role":"transform"},{"id":"chronic_flags","role":"transform"},{"id":"chronic_count","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_patients__with_conditions.sql","role":"final"}],"edges":[{"from":"conditions","to":"condition_stats","edge_type":"from"},{"from":"conditions","to":"chronic_flags","edge_type":"from"},{"from":"chronic_flags","to":"chronic_count","edge_type":"from"},{"from":"patients","to":"final","edge_type":"from"},{"from":"condition_stats","to":"final","edge_type":"left"},{"from":"chronic_flags","to":"final","edge_type":"left"},{"from":"chronic_count","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","chronic_count":"chronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n)","chronic_flags":"chronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n)","condition_stats":"condition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n)","conditions":"conditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","final":"final as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)","patients":"patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)"},"code_map":{"compiled":"\n\n\n\nwith patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":80,"col":1,"byte":2459},"end":{"line":80,"col":20,"byte":2478}},"chronic_count":{"start":{"line":56,"col":1,"byte":1519},"end":{"line":61,"col":2,"byte":1681}},"chronic_flags":{"start":{"line":33,"col":1,"byte":705},"end":{"line":54,"col":2,"byte":1516}},"condition_stats":{"start":{"line":22,"col":1,"byte":357},"end":{"line":31,"col":2,"byte":702}},"conditions":{"start":{"line":12,"col":1,"byte":131},"end":{"line":20,"col":2,"byte":354}},"final":{"start":{"line":63,"col":1,"byte":1684},"end":{"line":78,"col":2,"byte":2457}},"patients":{"start":{"line":5,"col":6,"byte":9},"end":{"line":10,"col":2,"byte":128}}},"column_spans":{"(final select)\u001f*":{"start":{"line":80,"col":8,"byte":2466},"end":{"line":80,"col":9,"byte":2467}},"chronic_count\u001fchronic_condition_count":{"start":{"line":59,"col":11,"byte":1578},"end":{"line":59,"col":89,"byte":1656}},"chronic_count\u001fpatient_id":{"start":{"line":58,"col":9,"byte":1557},"end":{"line":58,"col":19,"byte":1567}},"chronic_flags\u001fhas_diabetes":{"start":{"line":36,"col":11,"byte":764},"end":{"line":39,"col":29,"byte":891}},"chronic_flags\u001fhas_heart_disease":{"start":{"line":45,"col":11,"byte":1125},"end":{"line":51,"col":34,"byte":1470}},"chronic_flags\u001fhas_hypertension":{"start":{"line":40,"col":11,"byte":902},"end":{"line":44,"col":33,"byte":1114}},"chronic_flags\u001fpatient_id":{"start":{"line":35,"col":9,"byte":743},"end":{"line":35,"col":19,"byte":753}},"condition_stats\u001factive_conditions":{"start":{"line":26,"col":11,"byte":457},"end":{"line":26,"col":91,"byte":537}},"condition_stats\u001ffirst_condition_date":{"start":{"line":27,"col":11,"byte":548},"end":{"line":27,"col":60,"byte":597}},"condition_stats\u001flast_condition_date":{"start":{"line":28,"col":11,"byte":608},"end":{"line":28,"col":59,"byte":656}},"condition_stats\u001fpatient_id":{"start":{"line":24,"col":9,"byte":397},"end":{"line":24,"col":19,"byte":407}},"condition_stats\u001ftotal_conditions":{"start":{"line":25,"col":11,"byte":418},"end":{"line":25,"col":39,"byte":446}},"conditions\u001fcondition_code":{"start":{"line":17,"col":11,"byte":247},"end":{"line":17,"col":25,"byte":261}},"conditions\u001fcondition_description":{"start":{"line":18,"col":11,"byte":272},"end":{"line":18,"col":32,"byte":293}},"conditions\u001fcondition_end_date":{"start":{"line":16,"col":11,"byte":218},"end":{"line":16,"col":29,"byte":236}},"conditions\u001fcondition_start_date":{"start":{"line":15,"col":11,"byte":187},"end":{"line":15,"col":31,"byte":207}},"conditions\u001fpatient_id":{"start":{"line":14,"col":9,"byte":166},"end":{"line":14,"col":19,"byte":176}},"final\u001factive_conditions":{"start":{"line":67,"col":11,"byte":1800},"end":{"line":67,"col":65,"byte":1854}},"final\u001fchronic_condition_count":{"start":{"line":73,"col":11,"byte":2178},"end":{"line":73,"col":77,"byte":2244}},"final\u001ffirst_condition_date":{"start":{"line":68,"col":11,"byte":1865},"end":{"line":68,"col":34,"byte":1888}},"final\u001fhas_diabetes":{"start":{"line":70,"col":11,"byte":1932},"end":{"line":70,"col":76,"byte":1997}},"final\u001fhas_heart_disease":{"start":{"line":72,"col":11,"byte":2092},"end":{"line":72,"col":86,"byte":2167}},"final\u001fhas_hypertension":{"start":{"line":71,"col":11,"byte":2008},"end":{"line":71,"col":84,"byte":2081}},"final\u001flast_condition_date":{"start":{"line":69,"col":11,"byte":1899},"end":{"line":69,"col":33,"byte":1921}},"final\u001fpatient_id":{"start":{"line":65,"col":9,"byte":1714},"end":{"line":65,"col":21,"byte":1726}},"final\u001ftotal_conditions":{"start":{"line":66,"col":11,"byte":1737},"end":{"line":66,"col":63,"byte":1789}},"patients\u001fpatient_id":{"start":{"line":7,"col":9,"byte":42},"end":{"line":7,"col":19,"byte":52}}}},"column_lineage":{"context":{"active_conditions":{"description":"Number of currently active conditions (no end date)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"active_conditions","min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_active_conditions__0.4537ef7532"},{"kind":"not_null","kwargs":{"column_name":"active_conditions","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_active_conditions.4aeeebebb6"}],"documented":true},"chronic_condition_count":{"description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"chronic_condition_count","max_value":3,"min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_chronic_condition_count__3__0.b747d1158d"},{"kind":"not_null","kwargs":{"column_name":"chronic_condition_count","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_chronic_condition_count.9d5583fe6d"}],"documented":true},"first_condition_date":{"description":"Date of earliest condition diagnosis for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"first_condition_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","row_condition":"first_condition_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_first_condition_date__current_date___1900_01_01_date__first_condition_date_is_not_null.bb01167c58"}],"documented":true},"has_diabetes":{"description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_diabetes__False__True__False.d905d84361"},{"kind":"not_null","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_diabetes.bdb31b80da"}],"documented":true},"has_heart_disease":{"description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_heart_disease","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_heart_disease__False__True__False.37ed4a68fe"},{"kind":"not_null","kwargs":{"column_name":"has_heart_disease","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_heart_disease.12051b0631"}],"documented":true},"has_hypertension":{"description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_hypertension","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_hypertension__False__True__False.189f22b6bf"},{"kind":"not_null","kwargs":{"column_name":"has_hypertension","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_hypertension.e101ed97a8"}],"documented":true},"last_condition_date":{"description":"Date of most recent condition diagnosis for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"last_condition_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","row_condition":"last_condition_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_last_condition_date__current_date___1900_01_01_date__last_condition_date_is_not_null.8d4e9038fa"}],"documented":true},"patient_id":{"description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_patient_id.a53ab7d4d1"},{"kind":"unique","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_int_patients__with_conditions_patient_id.dbb08a220b"}],"documented":true},"total_conditions":{"description":"Total number of conditions ever diagnosed for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_conditions","min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_total_conditions__0.b1764f2a6e"},{"kind":"not_null","kwargs":{"column_name":"total_conditions","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) 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materialized='view'\n    )\n}}\n\n{#\n    Patient condition history with chronic disease flags.\n\n    IMPORTANT: Chronic Condition Detection Limitations\n    --------------------------------------------------\n    The chronic condition flags (has_diabetes, has_hypertension, has_heart_disease)\n    use simplified text matching on condition_description. This is NOT clinical-grade\n    detection and should be used for analytics/reporting only, not clinical decisions.\n\n    Detection patterns:\n    - Diabetes: description contains 'diabetes'\n    - Hypertension: description contains 'hypertension' or 'high blood pressure'\n    - Heart disease: description contains 'heart disease', 'cardiac', 'coronary',\n                     or 'atrial fibrillation'\n\n    For production clinical use, consider:\n    1. Using SNOMED codes instead of text matching\n    2. Implementing ICD-10 code mappings\n    3. Consulting clinical informaticists for proper condition groupings\n    4. Moving patterns to a seed file or reference table for maintainability\n#}\n\nwith patients as (\n    select\n        patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from {{ ref('stg_synthea__conditions') }}\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.int_patients__with_conditions","construct":"left_join[final:condition_stats]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"final — LEFT JOIN condition_stats ON patients.patient_id = condition_stats.patient_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"final","sketches":["- input: ref('dim_patients')\n  rows:\n    - {patient_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__conditions')\n  rows:\n    - {patient_id: 1}\n# expect: the no-match row with NULL stg_synthea__conditions columns (or the intended fallback)"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final select)\u001f*":{"start":{"line":30,"col":8,"byte":542},"end":{"line":30,"col":9,"byte":543}},"grid\u001fmonth_actual":{"start":{"line":23,"col":11,"byte":423},"end":{"line":23,"col":30,"byte":442}},"grid\u001fmonth_key":{"start":{"line":21,"col":14,"byte":312},"end":{"line":21,"col":85,"byte":383}},"grid\u001fmonth_name":{"start":{"line":24,"col":11,"byte":453},"end":{"line":24,"col":28,"byte":470}},"grid\u001fstate":{"start":{"line":25,"col":11,"byte":481},"end":{"line":25,"col":23,"byte":493}},"grid\u001fyear_actual":{"start":{"line":22,"col":11,"byte":394},"end":{"line":22,"col":29,"byte":412}},"months\u001fmonth_actual":{"start":{"line":8,"col":11,"byte":71},"end":{"line":8,"col":23,"byte":83}},"months\u001fmonth_name":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":21,"byte":104}},"months\u001fyear_actual":{"start":{"line":7,"col":9,"byte":49},"end":{"line":7,"col":20,"byte":60}},"states\u001fstate":{"start":{"line":14,"col":21,"byte":183},"end":{"line":14,"col":26,"byte":188}}}},"column_lineage":{"context":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","rows":null,"format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv"}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. 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condition_end_date\n\n        -- condition details\n        , CODE as condition_code\n        , DESCRIPTION as condition_description\n    from source\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_start_date as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as condition_id\n        , *\n    from renamed\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","node_spans":{"(final 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as gender\n        , SPECIALITY as specialty\n\n        -- location\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , UTILIZATION as utilization\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_encounter_summary","path":"models/marts/analytics/views/v_encounter_summary.sql","description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full 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provider_name\n    from {{ ref('dim_providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_name\n    from {{ ref('dim_organizations') }}\n),\n\npayers as (\n    select\n        payer_key\n        , payer_name\n    from {{ ref('dim_payers') }}\n),\n\nfinal as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_patient_current_conditions","path":"models/marts/analytics/views/v_patient_current_conditions.sql","description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient 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patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from 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ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, 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Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","tests":[],"is_recursive":false}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). 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identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at <= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_dq_quarantine__medications":{"description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"end_after_1900_if_present","description":"Flag: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"no_future_medication_dates","description":"Flag: cast(medication_start_at as date) <= current_date"},{"name":"start_after_1900","description":"Flag: medication_start_at >= timestamp '1900-01-01'"},{"name":"valid_medication_dates","description":"Flag: medication_end_at is null or medication_end_at >= medication_start_at"}]},"int_encounters__enriched":{"description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","materialized":"view","columns":[{"name":"condition_count","description":"Number of conditions diagnosed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID), sourced from fct_encounters","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_count","description":"Number of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_count","description":"Number of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_condition_cost","description":"Total cost associated with conditions (currently always 0 per model logic)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_cost","description":"Total cost of all clinical events (conditions + medications + procedures). Note: Condition costs are currently set to 0 in the model.\n","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_count","description":"Total count of all clinical events (conditions + medications + procedures)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_medication_cost","description":"Total cost of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_procedure_cost","description":"Total cost of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"int_patients__with_conditions":{"description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","materialized":"view","columns":[{"name":"active_conditions","description":"Number of currently active conditions (no end date)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"chronic_condition_count","description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"first_condition_date","description":"Date of earliest condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"has_diabetes","description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_heart_disease","description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_hypertension","description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_condition_date","description":"Date of most recent condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_conditions","description":"Total number of conditions ever diagnosed for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__conditions":{"description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"condition_code","description":"SNOMED CT code for the condition","tests":[{"name":"not null"}]},{"name":"condition_description","description":"Description of the condition","tests":[{"name":"not null"}]},{"name":"condition_end_date","description":"Date when condition resolved (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"condition_id","description":"Surrogate key for the condition record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_start_date","description":"Date when condition was diagnosed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__observations":{"description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"encounter_id","description":"Foreign key to the encounter. Note: ~10% of observations have null encounter_id (from external sources or historical data imports).\n","tests":[{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"observation_at","description":"Timestamp when observation was recorded","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"observation_code","description":"LOINC code for the observation","tests":[{"name":"not null"}]},{"name":"observation_description","description":"Description of the observation","tests":[{"name":"not null"}]},{"name":"observation_id","description":"Surrogate key for the observation record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"observation_type","description":"Type of observation (numeric, text, etc.)"},{"name":"observation_value","description":"Value of the observation (stored as string)"},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"units","description":"Units of measurement"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__organizations":{"description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Unique organization identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__procedures":{"description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost of the procedure","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"procedure_at","description":"Timestamp when the procedure was performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_code","description":"SNOMED CT code for the procedure","tests":[{"name":"not null"}]},{"name":"procedure_description","description":"Description of the procedure","tests":[{"name":"not null"}]},{"name":"procedure_id","description":"Surrogate key for the procedure record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of the procedure"},{"name":"reason_description","description":"Description of the reason for the procedure"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__providers":{"description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Foreign key to the provider's organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"provider_id","description":"Unique provider identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
+<script type="application/json" id="cute-dbt-data">{"baseline":"","models":[{"name":"dim_conditions","path":"models/marts/analytics/dim_conditions.sql","description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"condition_aggregates","role":"transform"},{"id":"deduped","role":"transform"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_conditions.sql","role":"final"}],"edges":[{"from":"conditions_source","to":"condition_aggregates","edge_type":"from"},{"from":"condition_aggregates","to":"deduped","edge_type":"from"},{"from":"deduped","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_aggregates":"condition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n)","conditions_source":"conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n)","deduped":"deduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n)","final":"final as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n)","unknown_member":"unknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n)"},"code_map":{"compiled":"\n\n\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":105,"col":1,"byte":2681},"end":{"line":105,"col":20,"byte":2700}},"condition_aggregates":{"start":{"line":18,"col":1,"byte":420},"end":{"line":28,"col":2,"byte":854}},"conditions_source":{"start":{"line":5,"col":6,"byte":9},"end":{"line":16,"col":2,"byte":417}},"deduped":{"start":{"line":31,"col":1,"byte":917},"end":{"line":44,"col":2,"byte":1295}},"final":{"start":{"line":79,"col":1,"byte":2118},"end":{"line":103,"col":2,"byte":2679}},"sequenced":{"start":{"line":46,"col":1,"byte":1298},"end":{"line":65,"col":2,"byte":1735}},"unknown_member":{"start":{"line":68,"col":1,"byte":1795},"end":{"line":77,"col":2,"byte":2115}}},"column_spans":{"(final 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when record was loaded","documented":true},"condition_code":{"description":"ICD/SNOMED code (natural key)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_code.193c903fa2"},{"kind":"unique","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_code.99cd9f3457"}],"documented":true},"condition_description":{"description":"Full condition name/description","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_description","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_description.dc79ef5b8c"}],"documented":true},"condition_key":{"description":"Surrogate key (row_number)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_key.5e35e4562f"},{"kind":"unique","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"}],"documented":true},"encounter_count":{"description":"Total number of encounters with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_encounter_count__0.9a92420a5b"}],"documented":true},"first_occurrence_date":{"description":"Earliest date this condition was diagnosed in the dataset","documented":true},"last_occurrence_date":{"description":"Most recent date this condition was diagnosed","documented":true},"patient_count":{"description":"Number of unique patients with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_patient_count__0.4c1de369ac"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_start_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_start_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_end_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_end_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}]},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":409,"col":1,"byte":12325},"end":{"line":409,"col":20,"byte":12344}},"date_spine":{"start":{"line":5,"col":6,"byte":9},"end":{"line":333,"col":2,"byte":8986}},"final":{"start":{"line":335,"col":1,"byte":8989},"end":{"line":407,"col":2,"byte":12323}}},"column_spans":{"(final select)\u001f*":{"start":{"line":409,"col":8,"byte":12332},"end":{"line":409,"col":9,"byte":12333}},"date_spine\u001fdate_day":{"start":{"line":178,"col":9,"byte":3934},"end":{"line":178,"col":19,"byte":3944}},"date_spine\u001fday_of_month":{"start":{"line":193,"col":9,"byte":4584},"end":{"line":193,"col":53,"byte":4628}},"date_spine\u001fday_of_week":{"start":{"line":188,"col":14,"byte":4285},"end":{"line":188,"col":73,"byte":4344}},"date_spine\u001fday_of_week_iso":{"start":{"line":190,"col":14,"byte":4394},"end":{"line":190,"col":76,"byte":4456}},"date_spine\u001fday_of_week_name":{"start":{"line":191,"col":9,"byte":4466},"end":{"line":191,"col":48,"byte":4505}},"date_spine\u001fday_of_week_name_short":{"start":{"line":192,"col":16,"byte":4522},"end":{"line":192,"col":68,"byte":4574}},"date_spine\u001fday_of_year":{"start":{"line":194,"col":9,"byte":4638},"end":{"line":194,"col":58,"byte":4687}},"date_spine\u001fiso_week_end_date":{"start":{"line":239,"col":11,"byte":5901},"end":{"line":239,"col":118,"byte":6008}},"date_spine\u001fiso_week_of_year":{"start":{"line":247,"col":10,"byte":6319},"end":{"line":247,"col":71,"byte":6380}},"date_spine\u001fiso_week_start_date":{"start":{"line":236,"col":14,"byte":5812},"end":{"line":236,"col":76,"byte":5874}},"date_spine\u001fiso_year_week":{"start":{"line":248,"col":9,"byte":6390},"end":{"line":267,"col":25,"byte":7034}},"date_spine\u001fmonth_end_date":{"start":{"line":287,"col":6,"byte":7592},"end":{"line":288,"col":44,"byte":7746}},"date_spine\u001fmonth_name":{"start":{"line":278,"col":9,"byte":7373},"end":{"line":278,"col":44,"byte":7408}},"date_spine\u001fmonth_name_short":{"start":{"line":279,"col":16,"byte":7425},"end":{"line":279,"col":64,"byte":7473}},"date_spine\u001fmonth_of_year":{"start":{"line":276,"col":13,"byte":7295},"end":{"line":277,"col":27,"byte":7363}},"date_spine\u001fmonth_start_date":{"start":{"line":281,"col":14,"byte":7489},"end":{"line":281,"col":74,"byte":7549}},"date_spine\u001fnext_date_day":{"start":{"line":184,"col":6,"byte":4064},"end":{"line":184,"col":80,"byte":4138}},"date_spine\u001fprior_date_day":{"start":{"line":181,"col":6,"byte":3966},"end":{"line":181,"col":82,"byte":4042}},"date_spine\u001fprior_year_date_day":{"start":{"line":185,"col":9,"byte":4148},"end":{"line":185,"col":53,"byte":4192}},"date_spine\u001fprior_year_iso_week_end_date":{"start":{"line":244,"col":11,"byte":6152},"end":{"line":245,"col":40,"byte":6299}},"date_spine\u001fprior_year_iso_week_of_year":{"start":{"line":272,"col":10,"byte":7165},"end":{"line":273,"col":39,"byte":7266}},"date_spine\u001fprior_year_iso_week_start_date":{"start":{"line":240,"col":14,"byte":6023},"end":{"line":241,"col":42,"byte":6125}},"date_spine\u001fprior_year_month_end_date":{"start":{"line":299,"col":6,"byte":7922},"end":{"line":301,"col":39,"byte":8107}},"date_spine\u001fprior_year_month_start_date":{"start":{"line":291,"col":13,"byte":7775},"end":{"line":292,"col":41,"byte":7866}},"date_spine\u001fprior_year_over_year_date_day":{"start":{"line":186,"col":9,"byte":4202},"end":{"line":186,"col":40,"byte":4233}},"date_spine\u001fprior_year_week_end_date":{"start":{"line":229,"col":6,"byte":5494},"end":{"line":233,"col":36,"byte":5726}},"date_spine\u001fprior_year_week_of_year":{"start":{"line":269,"col":19,"byte":7055},"end":{"line":270,"col":35,"byte":7145}},"date_spine\u001fprior_year_week_start_date":{"start":{"line":218,"col":6,"byte":5231},"end":{"line":222,"col":38,"byte":5418}},"date_spine\u001fquarter_end_date":{"start":{"line":315,"col":6,"byte":8409},"end":{"line":316,"col":46,"byte":8567}},"date_spine\u001fquarter_of_year":{"start":{"line":304,"col":13,"byte":8136},"end":{"line":305,"col":29,"byte":8208}},"date_spine\u001fquarter_start_date":{"start":{"line":307,"col":13,"byte":8236},"end":{"line":308,"col":32,"byte":8309}},"date_spine\u001fweek_end_date":{"start":{"line":210,"col":6,"byte":4975},"end":{"line":213,"col":70,"byte":5167}},"date_spine\u001fweek_of_year":{"start":{"line":234,"col":19,"byte":5746},"end":{"line":234,"col":69,"byte":5796}},"date_spine\u001fweek_start_date":{"start":{"line":200,"col":6,"byte":4752},"end":{"line":203,"col":25,"byte":4899}},"date_spine\u001fyear_end_date":{"start":{"line":327,"col":6,"byte":8777},"end":{"line":328,"col":43,"byte":8928}},"date_spine\u001fyear_number":{"start":{"line":319,"col":13,"byte":8596},"end":{"line":320,"col":25,"byte":8661}},"date_spine\u001fyear_start_date":{"start":{"line":321,"col":14,"byte":8676},"end":{"line":321,"col":72,"byte":8734}},"final\u001fdate_actual":{"start":{"line":341,"col":11,"byte":9213},"end":{"line":341,"col":34,"byte":9236}},"final\u001fdate_key":{"start":{"line":338,"col":14,"byte":9098},"end":{"line":338,"col":94,"byte":9178}},"final\u001fday_name":{"start":{"line":345,"col":11,"byte":9355},"end":{"line":345,"col":39,"byte":9383}},"final\u001fday_name_short":{"start":{"line":346,"col":11,"byte":9394},"end":{"line":346,"col":51,"byte":9434}},"final\u001fday_of_month":{"sta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actual date value","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"date_actual","max_value":"'2025-12-31'::date","min_value":"'1909-01-01'::date","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_date_date_actual___2025_12_31_date___1909_01_01_date.f3b5c5d47b"},{"kind":"not_null","kwargs":{"column_name":"date_actual","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_date_actual.85284edc63"},{"kind":"unique","kwargs":{"column_name":"date_actual","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.unique_dim_date_date_actual.107fe1b6a2"}],"documented":true},"date_key":{"description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"date_key","max_value":20251231,"min_value":19090101,"model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_date_date_key__20251231__19090101.ddd369949a"},{"kind":"not_null","kwargs":{"column_name":"date_key","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_date_key.881d0a31b6"},{"kind":"unique","kwargs":{"column_name":"date_key","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.unique_dim_date_date_key.0869fd48f9"}],"documented":true},"day_name":{"description":"Full day name (e.g., Monday, Tuesday)","tests":[{"kind":"not_null","kwargs":{"column_name":"day_name","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_day_name.3a968349a4"}],"documented":true},"day_name_short":{"description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"kind":"not_null","kwargs":{"column_name":"day_name_short","model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_date_day_name_short.ddec7c56fd"}],"documented":true},"day_of_month":{"description":"Day of the month (1-31)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"day_of_month","max_value":31,"min_value":1,"model":"{{ get_where_subquery(ref('dim_date')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_date_day_of_month__31__1.95bef47e40"},{"kind":"not_null","kwargs":{"column_name":"day_of_month","model":"{{ get_where_subquery(ref('dim_date')) 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)\n}}\n\n{#\n    Calendar dimension covering 1909-01-01 to 2025-12-31.\n\n    Note on date_key: Uses YYYYMMDD integer format. 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organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n  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as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final 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organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from 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as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , 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      -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as 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Each row represents a\n    specific version of a patient's record.\n\n    SCD Type 2 Columns:\n    - patient_key: Surrogate key (unique per version)\n    - patient_id: Natural key (same across versions)\n    - valid_from: When this version became active\n    - valid_to: When this version was superseded (null = current)\n    - is_current: True for the active version\n\n    To query current patients only:\n        WHERE is_current = true\n\n    To query patient state at a point in time:\n        WHERE valid_from <= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_patients","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__patients')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}]},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as 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      -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total 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get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme 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Health"}},{"display":"555-0100","key":{"t":"str","v":"555-0100"}},{"display":"1250.0","key":{"t":"number","v":"1250"}},{"display":"MA","key":{"t":"str","v":"MA"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"8","key":{"t":"number","v":"8"}},{"display":"02101","key":{"t":"str","v":"02101"}}]}]},"rows":[{"address":"1 Acme Way","amount_covered":1000.0,"amount_uncovered":250.0,"average_quality_of_life_score":0.85,"city":"Boston","covered_encounters":10,"covered_immunizations":4,"covered_medications":5,"covered_procedures":3,"member_months":96,"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_name":"Acme Health","phone":"555-0100","revenue":1250.0,"state":"MA","uncovered_encounters":2,"uncovered_immunizations":0,"uncovered_medications":1,"uncovered_procedures":0,"unique_customers":8,"zip_code":"02101"}],"format":"dict","column_meta":{"address":{"description":"Street address"},"amount_covered":{"description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"amount_uncovered":{"description":"Total amount not covered by payer"},"average_quality_of_life_score":{"description":"Average quality of life score for members"},"city":{"description":"City"},"covered_encounters":{"description":"Number of encounters covered"},"covered_immunizations":{"description":"Number of immunizations covered"},"covered_medications":{"description":"Number of medications covered"},"covered_procedures":{"description":"Number of procedures covered"},"member_months":{"description":"Total member months"},"payer_id":{"description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]},"phone":{"description":"Phone number"},"revenue":{"description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"state":{"description":"State where headquartered"},"uncovered_encounters":{"description":"Number of encounters not covered"},"uncovered_immunizations":{"description":"Number of immunizations not covered"},"uncovered_medications":{"description":"Number of medications not covered"},"uncovered_procedures":{"description":"Number of procedures not covered"},"unique_customers":{"description":"Number of unique customers"},"zip_code":{"description":"ZIP code (stored as numeric, 5 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\"memory\".\"main_marts\".\"dim_organizations\"\n)","providers":"providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n)","unknown_member":"unknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='provider_key'\n    )\n}}\n\nwith providers as (\n    select * from {{ ref('stg_synthea__providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from {{ ref('dim_organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_providers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"]},"evidence":[{"label":"unique_key","value":"provider_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_providers","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('dim_organizations'), ref('stg_synthea__providers')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. 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cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_encounter_id.5c743b1ef7"}],"documented":true},"encounter_key":{"description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_encounter_key.cc0e40eeb8"},{"kind":"relationships","kwargs":{"column_name":"encounter_key","field":"encounter_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","to":"ref('fct_encounters')"},"node_id":"test.healthcare_analytics.relationships_fct_clinical_events_encounter_key__encounter_key__ref_fct_encounters_.7a30909f42"}],"documented":true},"event_cost":{"description":"Cost of the clinical event","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"event_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","row_condition":"event_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_clinical_events_event_cost__0__event_cost_is_not_null.2c2582a25b"}],"documented":true},"event_date_key":{"description":"Foreign key to dim_date for event start","tests":[{"kind":"not_null","kwargs":{"column_name":"event_date_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_event_date_key.d605213965"},{"kind":"relationships","kwargs":{"column_name":"event_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_clinical_events_event_date_key__date_key__ref_dim_date_.0995b8993f"}],"documented":true},"event_end_date":{"description":"End date of the clinical event (null if ongoing or 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unique_key='clinical_event_key'\n    )\n}}\n\nwith valid_encounters as (\n    select encounter_id\n    from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from {{ ref('stg_synthea__conditions') }} as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from {{ ref('stg_synthea__medications') }} as med\n    {{ quarantine_filter() }}\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from {{ ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as 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mod","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_condition_cohorts","path":"models/marts/analytics/fct_condition_cohorts.sql","description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"patients","role":"import"},{"id":"dim_conditions","role":"import"},{"id":"condition_windows","role":"transform"},{"id":"with_dimensions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final 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end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n)","conditions_source":"conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n)","dim_conditions":"dim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","with_dimensions":"with_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n)"},"code_map":{"compiled":"\n\n\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","node_spans":{"(final 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when record was loaded","documented":true},"condition_code":{"description":"ICD/SNOMED condition code","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_condition_cohorts_condition_code.180142995a"}],"documented":true},"condition_cohort_key":{"description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_cohort_key","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_condition_cohorts_condition_cohort_key.1edb245ae1"},{"kind":"unique","kwargs":{"column_name":"condition_cohort_key","model":"{{ get_where_subquery(ref('fct_condition_cohorts')) }}"},"node_id":"test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"}],"documented":true},"condition_description":{"description":"Full condition 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unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}]},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as 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encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","node_spans":{"(final 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when record was loaded","documented":true},"cost_analysis_key":{"description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"kind":"not_null","kwargs":{"column_name":"cost_analysis_key","model":"{{ get_where_subquery(ref('fct_cost_analysis')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_cost_analysis_cost_analysis_key.a72ad66418"},{"kind":"unique","kwargs":{"column_name":"cost_analysis_key","model":"{{ get_where_subquery(ref('fct_cost_analysis')) }}"},"node_id":"test.healthcare_analytics.unique_fct_cost_analysis_cost_analysis_key.2aecf62159"}],"documented":true},"cost_per_procedure":{"description":"Average cost per procedure (null if no procedures)","documented":true},"coverage_pct":{"description":"Percentage covered by insurance (0-100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('fct_cost_analysis')) 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unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end 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This finding's evidence carries a copy-pasteable given sketch.","pin_node":"encounters_enriched","sketches":["- input: ref('fct_encounters')\n  rows:\n    - {encounter_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__procedures')\n  rows:\n    - {encounter_id: 1}\n# expect: the no-match row with NULL stg_synthea__procedures columns (or the intended fallback)"]}]},{"name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. 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timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final select)\u001f*":{"start":{"line":109,"col":8,"byte":2736},"end":{"line":109,"col":9,"byte":2737}},"dim_date\u001fdate_actual":{"start":{"line":45,"col":11,"byte":721},"end":{"line":45,"col":22,"byte":732}},"dim_date\u001fdate_key":{"start":{"line":44,"col":9,"byte":702},"end":{"line":44,"col":17,"byte":710}},"encounters\u001f*":{"start":{"line":4,"col":12,"byte":34},"end":{"line":4,"col":13,"byte":35}},"final\u001f_loaded_at":{"start":{"line":98,"col":11,"byte":2250},"end":{"line":98,"col":42,"byte":2281}},"final\u001fbase_encounter_cost":{"start":{"line":86,"col":11,"byte":1836},"end":{"line":86,"col":32,"byte":1857}},"final\u001fduration_minutes":{"start":{"line":83,"col":11,"byte":1726},"end":{"line":83,"col":92,"byte":1807}},"final\u001fencounter_class":{"start":{"line":72,"col":11,"byte":1470},"end":{"line":72,"col":28,"byte":1487}},"final\u001fencounter_code":{"start":{"line":73,"col":11,"byte":1498},"end":{"line":73,"col":27,"byte":1514}},"final\u001fencounter_description":{"start":{"line":74,"col":11,"byte":1525},"end":{"line":74,"col":34,"byte":1548}},"final\u001fencounter_end_at":{"start":{"line":80,"col":11,"byte":1668},"end":{"line":80,"col":29,"byte":1686}},"final\u001fencounter_end_date_key":{"start":{"line":69,"col":11,"byte":1385},"end":{"line":69,"col":62,"byte":1436}},"final\u001fencounter_id":{"start":{"line":55,"col":11,"byte":929},"end":{"line":55,"col":25,"byte":943}},"final\u001fencounter_key":{"start":{"line":52,"col":9,"byte":834},"end":{"line":52,"col":69,"byte":894}},"final\u001fencounter_start_at":{"start":{"line":79,"col":11,"byte":1637},"end":{"line":79,"col":31,"byte":1657}},"final\u001fencounter_start_date_key":{"start":{"line":68,"col":11,"byte":1321},"end":{"line":68,"col":64,"byte":1374}},"final\u001forganization_id":{"start":{"line":63,"col":11,"byte":1200},"end":{"line":63,"col":28,"byte":1217}},"final\u001forganization_key":{"start":{"line":62,"col":11,"byte":1137},"end":{"line":62,"col":63,"byte":1189}},"final\u001fpatient_age_at_encounter":{"start":{"line":92,"col":11,"byte":2047},"end":{"line":95,"col":40,"byte":2218}},"final\u001fpatient_id":{"start":{"line":59,"col":11,"byte":1034},"end":{"line":59,"col":23,"byte":1046}},"final\u001fpatient_key":{"start":{"line":58,"col":11,"byte":981},"end":{"line":58,"col":53,"byte":1023}},"final\u001fpatient_responsibility":{"start":{"line":89,"col":11,"byte":1924},"end":{"line":89,"col":87,"byte":2000}},"final\u001fpayer_coverage":{"start":{"line":88,"col":11,"byte":1897},"end":{"line":88,"col":27,"byte":1913}},"final\u001fpayer_id":{"start":{"line":65,"col":11,"byte":1278},"end":{"line":65,"col":21,"byte":1288}},"final\u001fpayer_key":{"start":{"line":64,"col":11,"byte":1228},"end":{"line":64,"col":50,"byte":1267}},"final\u001fprovider_id":{"start":{"line":61,"col":11,"byte":1113},"end":{"line":61,"col":24,"byte":1126}},"final\u001fprovider_key":{"start":{"line":60,"col":11,"byte":1057},"end":{"line":60,"col":56,"byte":1102}},"final\u001freason_code":{"start":{"line":75,"col":11,"byte":1559},"end":{"line":75,"col":24,"byte":1572}},"final\u001freason_description":{"start":{"line":76,"col":11,"byte":1583},"end":{"line":76,"col":31,"byte":1603}},"final\u001ftotal_claim_cost":{"start":{"line":87,"col":11,"byte":1868},"end":{"line":87,"col":29,"byte":1886}},"organizations\u001forganization_id":{"start":{"line":31,"col":11,"byte":490},"end":{"line":31,"col":26,"byte":505}},"organizations\u001forganization_key":{"start":{"line":30,"col":9,"byte":463},"end":{"line":30,"col":25,"byte":479}},"patients\u001fbirth_date":{"start":{"line":16,"col":11,"byte":216},"end":{"line":16,"col":21,"byte":226}},"patients\u001fpatient_id":{"start":{"line":15,"col":11,"byte":195},"end":{"line":15,"col":21,"byte":205}},"patients\u001fpatient_key":{"start":{"line":14,"col":9,"byte":173},"end":{"line":14,"col":20,"byte":184}},"payers\u001fpayer_id":{"start":{"line":38,"col":11,"byte":612},"end":{"line":38,"col":19,"byte":620}},"payers\u001fpayer_key":{"start":{"line":37,"col":9,"byte":592},"end":{"line":37,"col":18,"byte":601}},"providers\u001fprovider_id":{"start":{"line":24,"col":11,"byte":362},"end":{"line":24,"col":22,"byte":373}},"providers\u001fprovider_key":{"start":{"line":23,"col":9,"byte":339},"end":{"line":23,"col":21,"byte":351}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"base_encounter_cost":{"description":"Base cost of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"base_encounter_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"base_encounter_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_base_encounter_cost__0__base_encounter_cost_is_not_null.a096f214be"}],"documented":true},"duration_minutes":{"description":"Duration of encounter in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_duration_minutes__0__duration_minutes_is_not_null.ce51123f28"}],"documented":true},"encounter_class":{"description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.59f26de6a5"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_class.9419876296"}],"documented":true},"encounter_code":{"description":"SNOMED CT code for the encounter type","documented":true},"encounter_description":{"description":"Description of the encounter type","documented":true},"encounter_end_at":{"description":"End timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_end_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"encounter_end_at is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_end_at__current_timestamp___1900_01_01_timestamp__encounter_end_at_is_not_null.393dec12f4"}],"documented":true},"encounter_end_date_key":{"description":"Foreign key to dim_date for encounter end","documented":true},"encounter_id":{"description":"Natural encounter identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_id.f7111b4eea"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_id.ce88bae928"}],"documented":true},"encounter_key":{"description":"Surrogate key for the encounter","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_key.4fa2f03d5e"},{"kind":"unique","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"}],"documented":true},"encounter_start_at":{"description":"Start timestamp of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_start_at","max_value":"current_timestamp","min_value":"'1900-01-01'::timestamp","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_encounter_start_at__current_timestamp___1900_01_01_timestamp.f32b839e27"},{"kind":"not_null","kwargs":{"column_name":"encounter_start_at","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_at.c47cc214ac"}],"documented":true},"encounter_start_date_key":{"description":"Foreign key to dim_date for encounter start","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_start_date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_date_key.73ce57ba43"},{"kind":"relationships","kwargs":{"column_name":"encounter_start_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_encounter_start_date_key__date_key__ref_dim_date_.4f1d88383c"}],"documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_id.acfb633ecb"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_key.815fa1363c"},{"kind":"relationships","kwargs":{"column_name":"organization_key","field":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_organizations')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_organization_key__organization_key__ref_dim_organizations_.5feb1b3ad6"}],"documented":true},"patient_age_at_encounter":{"description":"Patient's age at the time of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_age_at_encounter","max_value":150,"min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_age_at_encounter is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_age_at_encounter__150__0__patient_age_at_encounter_is_not_null.bbd237256e"}],"documented":true},"patient_id":{"description":"Natural patient identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_id.9e325e7ac6"}],"documented":true},"patient_key":{"description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_patient_key.e5abbfbe29"},{"kind":"relationships","kwargs":{"column_name":"patient_key","field":"patient_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_patients')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_patient_key__patient_key__ref_dim_patients_.ad1c292e28"}],"documented":true},"patient_responsibility":{"description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_responsibility","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"patient_responsibility is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_patient_responsibility__0__patient_responsibility_is_not_null.7a61f00fd0"}],"documented":true},"payer_coverage":{"description":"Amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"payer_coverage","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"payer_coverage is not 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This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"fct_encounters_monthly","path":"models/marts/core/fct_encounters_monthly.sql","description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters_monthly.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"encounters_with_date","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)\n\nselect * from final","node_spans":{"(final 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select)\u001f*":{"start":{"line":53,"col":8,"byte":1381},"end":{"line":53,"col":9,"byte":1382}},"dim_date\u001fdate_key":{"start":{"line":18,"col":9,"byte":334},"end":{"line":18,"col":17,"byte":342}},"dim_date\u001fmonth_actual":{"start":{"line":20,"col":11,"byte":375},"end":{"line":20,"col":23,"byte":387}},"dim_date\u001fyear_actual":{"start":{"line":19,"col":11,"byte":353},"end":{"line":19,"col":22,"byte":364}},"encounters\u001fduration_minutes":{"start":{"line":12,"col":11,"byte":232},"end":{"line":12,"col":27,"byte":248}},"encounters\u001fencounter_class":{"start":{"line":8,"col":11,"byte":121},"end":{"line":8,"col":26,"byte":136}},"encounters\u001fencounter_id":{"start":{"line":5,"col":9,"byte":42},"end":{"line":5,"col":21,"byte":54}},"encounters\u001fencounter_start_date_key":{"start":{"line":7,"col":11,"byte":86},"end":{"line":7,"col":35,"byte":110}},"encounters\u001fpatient_id":{"start":{"line":6,"col":11,"byte":65},"end":{"line":6,"col":21,"byte":75}},"encounters\u001fpatient_responsibility":{"start":{"line":11,"col":11,"byte":199},"end":{"line":11,"col":33,"byte":221}},"encounters\u001fpayer_coverage":{"start":{"line":10,"col":11,"byte":174},"end":{"line":10,"col":25,"byte":188}},"encounters\u001ftotal_claim_cost":{"start":{"line":9,"col":11,"byte":147},"end":{"line":9,"col":27,"byte":163}},"encounters_with_date\u001f*":{"start":{"line":26,"col":9,"byte":479},"end":{"line":26,"col":12,"byte":482}},"encounters_with_date\u001fmonth_actual":{"start":{"line":28,"col":11,"byte":517},"end":{"line":28,"col":25,"byte":531}},"encounters_with_date\u001fyear_actual":{"start":{"line":27,"col":11,"byte":493},"end":{"line":27,"col":24,"byte":506}},"encounters_with_date\u001fyear_month":{"start":{"line":29,"col":16,"byte":547},"end":{"line":30,"col":72,"byte":654}},"final\u001f_loaded_at":{"start":{"line":48,"col":11,"byte":1241},"end":{"line":48,"col":42,"byte":1272}},"final\u001favg_duration_minutes":{"start":{"line":47,"col":11,"byte":1185},"end":{"line":47,"col":56,"byte":1230}},"final\u001fencounter_class":{"start":{"line":41,"col":11,"byte":889},"end":{"line":41,"col":26,"byte":904}},"final\u001fencounter_count":{"start":{"line":42,"col":11,"byte":915},"end":{"line":42,"col":38,"byte":942}},"final\u001fmonth_actual":{"start":{"line":40,"col":11,"byte":866},"end":{"line":40,"col":23,"byte":878}},"final\u001ftotal_claim_cost":{"start":{"line":44,"col":11,"byte":1009},"end":{"line":44,"col":52,"byte":1050}},"final\u001ftotal_patient_responsibility":{"start":{"line":46,"col":11,"byte":1115},"end":{"line":46,"col":70,"byte":1174}},"final\u001ftotal_payer_coverage":{"start":{"line":45,"col":11,"byte":1061},"end":{"line":45,"col":54,"byte":1104}},"final\u001funique_patients":{"start":{"line":43,"col":11,"byte":953},"end":{"line":43,"col":56,"byte":998}},"final\u001fyear_actual":{"start":{"line":39,"col":11,"byte":844},"end":{"line":39,"col":22,"byte":855}},"final\u001fyear_month":{"start":{"line":38,"col":9,"byte":823},"end":{"line":38,"col":19,"byte":833}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"avg_duration_minutes":{"description":"Average encounter duration in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"avg_duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"avg_duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_avg_duration_minutes__0__avg_duration_minutes_is_not_null.0cea70c94f"}],"documented":true},"encounter_class":{"description":"Type of encounter","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_monthly_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.274b5b6532"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_encounter_class.d503677b5a"}],"documented":true},"encounter_count":{"description":"Number of encounters in the month","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_encounter_count__1.03132101e0"},{"kind":"not_null","kwargs":{"column_name":"encounter_count","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_encounter_count.5046b087e4"}],"documented":true},"month_actual":{"description":"Month number (1-12)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"month_actual","max_value":12,"min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_month_actual__12__1.f650fcabb6"},{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_month_actual.82083ad465"}],"documented":true},"total_claim_cost":{"description":"Sum of total claim costs","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_claim_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_claim_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_claim_cost__0__total_claim_cost_is_not_null.09195ba1c6"}],"documented":true},"total_patient_responsibility":{"description":"Sum of patient responsibility amounts","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_patient_responsibility","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_patient_responsibility is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_patient_responsibility__0__total_patient_responsibility_is_not_null.e824258649"}],"documented":true},"total_payer_coverage":{"description":"Sum of payer coverage amounts","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_payer_coverage","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_payer_coverage is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_payer_coverage__0__total_payer_coverage_is_not_null.1cae11e2af"}],"documented":true},"unique_patients":{"description":"Number of unique patients with encounters","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"unique_patients","min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_unique_patients__1.86f77befe3"},{"kind":"not_null","kwargs":{"column_name":"unique_patients","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) 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   where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , cast(count(*) as decimal(18, 2)) / nullif(count(distinct patient_id), 0) as encounters_per_patient\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_actual, encounter_class\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_yearly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_actual, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"}]},{"name":"fct_patient_summary","path":"models/marts/analytics/fct_patient_summary.sql","description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health 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\"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","conditions_per_patient_year":"conditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from 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e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct 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 select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","node_spans":{"(final 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select)\u001f*":{"start":{"line":143,"col":8,"byte":3621},"end":{"line":143,"col":9,"byte":3622}},"dim_date\u001fdate_key":{"start":{"line":26,"col":9,"byte":485},"end":{"line":26,"col":17,"byte":493}},"dim_date\u001fmonth_actual":{"start":{"line":28,"col":11,"byte":526},"end":{"line":28,"col":23,"byte":538}},"dim_date\u001fyear_actual":{"start":{"line":27,"col":11,"byte":504},"end":{"line":27,"col":22,"byte":515}},"encounters\u001fduration_minutes":{"start":{"line":18,"col":11,"byte":319},"end":{"line":18,"col":27,"byte":335}},"encounters\u001fencounter_id":{"start":{"line":8,"col":11,"byte":68},"end":{"line":8,"col":23,"byte":80}},"encounters\u001fencounter_key":{"start":{"line":7,"col":9,"byte":44},"end":{"line":7,"col":22,"byte":57}},"encounters\u001fencounter_start_date_key":{"start":{"line":15,"col":11,"byte":232},"end":{"line":15,"col":35,"byte":256}},"encounters\u001forganization_id":{"start":{"line":14,"col":11,"byte":206},"end":{"line":14,"col":26,"byte":221}},"encounters\u001forganization_key":{"start":{"line":13,"col":11,"byte":179},"end":{"line":13,"col":27,"byte":195}},"encounters\u001fpatient_age_at_encounter":{"start":{"line":19,"col":11,"byte":346},"end":{"line":19,"col":35,"byte":370}},"encounters\u001fpatient_id":{"start":{"line":10,"col":11,"byte":113},"end":{"line":10,"col":21,"byte":123}},"encounters\u001fpatient_key":{"start":{"line":9,"col":11,"byte":91},"end":{"line":9,"col":22,"byte":102}},"encounters\u001fpayer_coverage":{"start":{"line":17,"col":11,"byte":294},"end":{"line":17,"col":25,"byte":308}},"encounters\u001fprovider_id":{"start":{"line":12,"col":11,"byte":157},"end":{"line":12,"col":22,"byte":168}},"encounters\u001fprovider_key":{"start":{"line":11,"col":11,"byte":134},"end":{"line":11,"col":23,"byte":146}},"encounters\u001ftotal_claim_cost":{"start":{"line":16,"col":11,"byte":267},"end":{"line":16,"col":27,"byte":283}},"encounters_with_date\u001f*":{"start":{"line":43,"col":9,"byte":860},"end":{"line":43,"col":12,"byte":863}},"encounters_with_date\u001fmonth_actual":{"start":{"line":45,"col":11,"byte":898},"end":{"line":45,"col":25,"byte":912}},"encounters_with_date\u001fprocedure_count":{"start":{"line":49,"col":11,"byte":1062},"end":{"line":49,"col":61,"byte":1112}},"encounters_with_date\u001fyear_actual":{"start":{"line":44,"col":11,"byte":874},"end":{"line":44,"col":24,"byte":887}},"encounters_with_date\u001fyear_month_str":{"start":{"line":46,"col":16,"byte":928},"end":{"line":48,"col":79,"byte":1051}},"final\u001f_loaded_at":{"start":{"line":138,"col":11,"byte":3550},"end":{"line":138,"col":42,"byte":3581}},"final\u001favg_cost_per_encounter":{"start":{"line":125,"col":11,"byte":3162},"end":{"line":129,"col":38,"byte":3316}},"final\u001favg_encounter_duration_minutes":{"start":{"line":118,"col":11,"byte":2896},"end":{"line":118,"col":85,"byte":2970}},"final\u001favg_patient_age":{"start":{"line":117,"col":11,"byte":2841},"end":{"line":117,"col":55,"byte":2885}},"final\u001fencounter_count":{"start":{"line":112,"col":11,"byte":2726},"end":{"line":112,"col":26,"byte":2741}},"final\u001fencounters_per_patient_avg":{"start":{"line":131,"col":11,"byte":3328},"end":{"line":135,"col":42,"byte":3518}},"final\u001fmonth_actual":{"start":{"line":108,"col":11,"byte":2649},"end":{"line":108,"col":23,"byte":2661}},"final\u001forganization_id":{"start":{"line":104,"col":11,"byte":2578},"end":{"line":104,"col":26,"byte":2593}},"final\u001forganization_key":{"start":{"line":103,"col":11,"byte":2551},"end":{"line":103,"col":27,"byte":2567}},"final\u001fprovider_id":{"start":{"line":102,"col":11,"byte":2529},"end":{"line":102,"col":22,"byte":2540}},"final\u001fprovider_key":{"start":{"line":101,"col":11,"byte":2506},"end":{"line":101,"col":23,"byte":2518}},"final\u001fprovider_metrics_key":{"start":{"line":98,"col":9,"byte":2386},"end":{"line":98,"col":91,"byte":2468}},"final\u001ftotal_cost":{"start":{"line":121,"col":11,"byte":3009},"end":{"line":121,"col":48,"byte":3046}},"final\u001ftotal_payer_coverage":{"start":{"line":122,"col":11,"byte":3057},"end":{"line":122,"col":68,"byte":3114}},"final\u001ftotal_procedures":{"start":{"line":114,"col":11,"byte":2783},"end":{"line":114,"col":27,"byte":2799}},"final\u001funique_patient_count":{"start":{"line":113,"col":11,"byte":2752},"end":{"line":113,"col":31,"byte":2772}},"final\u001fyear_actual":{"start":{"line":107,"col":11,"byte":2627},"end":{"line":107,"col":22,"byte":2638}},"final\u001fyear_month_str":{"start":{"line":109,"col":11,"byte":2672},"end":{"line":109,"col":25,"byte":2686}},"procedures\u001fencounter_id":{"start":{"line":34,"col":9,"byte":620},"end":{"line":34,"col":21,"byte":632}},"procedures\u001fprocedure_count":{"start":{"line":35,"col":11,"byte":643},"end":{"line":35,"col":38,"byte":670}},"provider_month_agg\u001favg_encounter_duration_minutes":{"start":{"line":75,"col":11,"byte":1828},"end":{"line":75,"col":68,"byte":1885}},"provider_month_agg\u001favg_patient_age":{"start":{"line":72,"col":11,"byte":1737},"end":{"l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   materialized='view'\n    )\n}}\n\nwith encounters as (\n    select\n        encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nconditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from {{ ref('stg_synthea__conditions') }}\n    group by encounter_id\n),\n\nmedications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from {{ ref('stg_synthea__medications') }}\n    {{ quarantine_filter() }}\n    group by encounter_id\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nfinal as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"int_patients__with_conditions","path":"models/intermediate/healthcare/int_patients__with_conditions.sql","description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","dag":{"nodes":[{"id":"patients","role":"import"},{"id":"conditions","role":"import"},{"id":"condition_stats","role":"transform"},{"id":"chronic_flags","role":"transform"},{"id":"chronic_count","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_patients__with_conditions.sql","role":"final"}],"edges":[{"from":"conditions","to":"condition_stats","edge_type":"from"},{"from":"conditions","to":"chronic_flags","edge_type":"from"},{"from":"chronic_flags","to":"chronic_count","edge_type":"from"},{"from":"patients","to":"final","edge_type":"from"},{"from":"condition_stats","to":"final","edge_type":"left"},{"from":"chronic_flags","to":"final","edge_type":"left"},{"from":"chronic_count","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","chronic_count":"chronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n)","chronic_flags":"chronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n)","condition_stats":"condition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n)","conditions":"conditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","final":"final as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)","patients":"patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)"},"code_map":{"compiled":"\n\n\n\nwith patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":80,"col":1,"byte":2459},"end":{"line":80,"col":20,"byte":2478}},"chronic_count":{"start":{"line":56,"col":1,"byte":1519},"end":{"line":61,"col":2,"byte":1681}},"chronic_flags":{"start":{"line":33,"col":1,"byte":705},"end":{"line":54,"col":2,"byte":1516}},"condition_stats":{"start":{"line":22,"col":1,"byte":357},"end":{"line":31,"col":2,"byte":702}},"conditions":{"start":{"line":12,"col":1,"byte":131},"end":{"line":20,"col":2,"byte":354}},"final":{"start":{"line":63,"col":1,"byte":1684},"end":{"line":78,"col":2,"byte":2457}},"patients":{"start":{"line":5,"col":6,"byte":9},"end":{"line":10,"col":2,"byte":128}}},"column_spans":{"(final select)\u001f*":{"start":{"line":80,"col":8,"byte":2466},"end":{"line":80,"col":9,"byte":2467}},"chronic_count\u001fchronic_condition_count":{"start":{"line":59,"col":11,"byte":1578},"end":{"line":59,"col":89,"byte":1656}},"chronic_count\u001fpatient_id":{"start":{"line":58,"col":9,"byte":1557},"end":{"line":58,"col":19,"byte":1567}},"chronic_flags\u001fhas_diabetes":{"start":{"line":36,"col":11,"byte":764},"end":{"line":39,"col":29,"byte":891}},"chronic_flags\u001fhas_heart_disease":{"start":{"line":45,"col":11,"byte":1125},"end":{"line":51,"col":34,"byte":1470}},"chronic_flags\u001fhas_hypertension":{"start":{"line":40,"col":11,"byte":902},"end":{"line":44,"col":33,"byte":1114}},"chronic_flags\u001fpatient_id":{"start":{"line":35,"col":9,"byte":743},"end":{"line":35,"col":19,"byte":753}},"condition_stats\u001factive_conditions":{"start":{"line":26,"col":11,"byte":457},"end":{"line":26,"col":91,"byte":537}},"condition_stats\u001ffirst_condition_date":{"start":{"line":27,"col":11,"byte":548},"end":{"line":27,"col":60,"byte":597}},"condition_stats\u001flast_condition_date":{"start":{"line":28,"col":11,"byte":608},"end":{"line":28,"col":59,"byte":656}},"condition_stats\u001fpatient_id":{"start":{"line":24,"col":9,"byte":397},"end":{"line":24,"col":19,"byte":407}},"condition_stats\u001ftotal_conditions":{"start":{"line":25,"col":11,"byte":418},"end":{"line":25,"col":39,"byte":446}},"conditions\u001fcondition_code":{"start":{"line":17,"col":11,"byte":247},"end":{"line":17,"col":25,"byte":261}},"conditions\u001fcondition_description":{"start":{"line":18,"col":11,"byte":272},"end":{"line":18,"col":32,"byte":293}},"conditions\u001fcondition_end_date":{"start":{"line":16,"col":11,"byte":218},"end":{"line":16,"col":29,"byte":236}},"conditions\u001fcondition_start_date":{"start":{"line":15,"col":11,"byte":187},"end":{"line":15,"col":31,"byte":207}},"conditions\u001fpatient_id":{"start":{"line":14,"col":9,"byte":166},"end":{"line":14,"col":19,"byte":176}},"final\u001factive_conditions":{"start":{"line":67,"col":11,"byte":1800},"end":{"line":67,"col":65,"byte":1854}},"final\u001fchronic_condition_count":{"start":{"line":73,"col":11,"byte":2178},"end":{"line":73,"col":77,"byte":2244}},"final\u001ffirst_condition_date":{"start":{"line":68,"col":11,"byte":1865},"end":{"line":68,"col":34,"byte":1888}},"final\u001fhas_diabetes":{"start":{"line":70,"col":11,"byte":1932},"end":{"line":70,"col":76,"byte":1997}},"final\u001fhas_heart_disease":{"start":{"line":72,"col":11,"byte":2092},"end":{"line":72,"col":86,"byte":2167}},"final\u001fhas_hypertension":{"start":{"line":71,"col":11,"byte":2008},"end":{"line":71,"col":84,"byte":2081}},"final\u001flast_condition_date":{"start":{"line":69,"col":11,"byte":1899},"end":{"line":69,"col":33,"byte":1921}},"final\u001fpatient_id":{"start":{"line":65,"col":9,"byte":1714},"end":{"line":65,"col":21,"byte":1726}},"final\u001ftotal_conditions":{"start":{"line":66,"col":11,"byte":1737},"end":{"line":66,"col":63,"byte":1789}},"patients\u001fpatient_id":{"start":{"line":7,"col":9,"byte":42},"end":{"line":7,"col":19,"byte":52}}}},"column_lineage":{"context":{"active_conditions":{"description":"Number of currently active conditions (no end date)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"active_conditions","min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_active_conditions__0.4537ef7532"},{"kind":"not_null","kwargs":{"column_name":"active_conditions","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_active_conditions.4aeeebebb6"}],"documented":true},"chronic_condition_count":{"description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"chronic_condition_count","max_value":3,"min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_chronic_condition_count__3__0.b747d1158d"},{"kind":"not_null","kwargs":{"column_name":"chronic_condition_count","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_chronic_condition_count.9d5583fe6d"}],"documented":true},"first_condition_date":{"description":"Date of earliest condition diagnosis for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"first_condition_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","row_condition":"first_condition_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_first_condition_date__current_date___1900_01_01_date__first_condition_date_is_not_null.bb01167c58"}],"documented":true},"has_diabetes":{"description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_diabetes__False__True__False.d905d84361"},{"kind":"not_null","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_diabetes.bdb31b80da"}],"documented":true},"has_heart_disease":{"description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_heart_disease","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_heart_disease__False__True__False.37ed4a68fe"},{"kind":"not_null","kwargs":{"column_name":"has_heart_disease","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_heart_disease.12051b0631"}],"documented":true},"has_hypertension":{"description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_hypertension","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_hypertension__False__True__False.189f22b6bf"},{"kind":"not_null","kwargs":{"column_name":"has_hypertension","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_hypertension.e101ed97a8"}],"documented":true},"last_condition_date":{"description":"Date of most recent condition diagnosis for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"last_condition_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","row_condition":"last_condition_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_last_condition_date__current_date___1900_01_01_date__last_condition_date_is_not_null.8d4e9038fa"}],"documented":true},"patient_id":{"description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_patient_id.a53ab7d4d1"},{"kind":"unique","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_int_patients__with_conditions_patient_id.dbb08a220b"}],"documented":true},"total_conditions":{"description":"Total number of conditions ever diagnosed for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_conditions","min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_total_conditions__0.b1764f2a6e"},{"kind":"not_null","kwargs":{"column_name":"total_conditions","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) 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materialized='view'\n    )\n}}\n\n{#\n    Patient condition history with chronic disease flags.\n\n    IMPORTANT: Chronic Condition Detection Limitations\n    --------------------------------------------------\n    The chronic condition flags (has_diabetes, has_hypertension, has_heart_disease)\n    use simplified text matching on condition_description. This is NOT clinical-grade\n    detection and should be used for analytics/reporting only, not clinical decisions.\n\n    Detection patterns:\n    - Diabetes: description contains 'diabetes'\n    - Hypertension: description contains 'hypertension' or 'high blood pressure'\n    - Heart disease: description contains 'heart disease', 'cardiac', 'coronary',\n                     or 'atrial fibrillation'\n\n    For production clinical use, consider:\n    1. Using SNOMED codes instead of text matching\n    2. Implementing ICD-10 code mappings\n    3. Consulting clinical informaticists for proper condition groupings\n    4. Moving patterns to a seed file or reference table for maintainability\n#}\n\nwith patients as (\n    select\n        patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from {{ ref('stg_synthea__conditions') }}\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.int_patients__with_conditions","construct":"left_join[final:condition_stats]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"final — LEFT JOIN condition_stats ON patients.patient_id = condition_stats.patient_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"final","sketches":["- input: ref('dim_patients')\n  rows:\n    - {patient_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__conditions')\n  rows:\n    - {patient_id: 1}\n# expect: the no-match row with NULL stg_synthea__conditions columns (or the intended fallback)"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final select)\u001f*":{"start":{"line":30,"col":8,"byte":542},"end":{"line":30,"col":9,"byte":543}},"grid\u001fmonth_actual":{"start":{"line":23,"col":11,"byte":423},"end":{"line":23,"col":30,"byte":442}},"grid\u001fmonth_key":{"start":{"line":21,"col":14,"byte":312},"end":{"line":21,"col":85,"byte":383}},"grid\u001fmonth_name":{"start":{"line":24,"col":11,"byte":453},"end":{"line":24,"col":28,"byte":470}},"grid\u001fstate":{"start":{"line":25,"col":11,"byte":481},"end":{"line":25,"col":23,"byte":493}},"grid\u001fyear_actual":{"start":{"line":22,"col":11,"byte":394},"end":{"line":22,"col":29,"byte":412}},"months\u001fmonth_actual":{"start":{"line":8,"col":11,"byte":71},"end":{"line":8,"col":23,"byte":83}},"months\u001fmonth_name":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":21,"byte":104}},"months\u001fyear_actual":{"start":{"line":7,"col":9,"byte":49},"end":{"line":7,"col":20,"byte":60}},"states\u001fstate":{"start":{"line":14,"col":21,"byte":183},"end":{"line":14,"col":26,"byte":188}}}},"column_lineage":{"context":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","rows":null,"format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv"}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. 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condition_end_date\n\n        -- condition details\n        , CODE as condition_code\n        , DESCRIPTION as condition_description\n    from source\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_start_date as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as condition_id\n        , *\n    from renamed\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","node_spans":{"(final 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as gender\n        , SPECIALITY as specialty\n\n        -- location\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , UTILIZATION as utilization\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_encounter_summary","path":"models/marts/analytics/views/v_encounter_summary.sql","description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full 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provider_name\n    from {{ ref('dim_providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_name\n    from {{ ref('dim_organizations') }}\n),\n\npayers as (\n    select\n        payer_key\n        , payer_name\n    from {{ ref('dim_payers') }}\n),\n\nfinal as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_patient_current_conditions","path":"models/marts/analytics/views/v_patient_current_conditions.sql","description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient 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patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from 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ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, 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Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","tests":[],"is_recursive":false}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). 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identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at <= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_dq_quarantine__medications":{"description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"end_after_1900_if_present","description":"Flag: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"no_future_medication_dates","description":"Flag: cast(medication_start_at as date) <= current_date"},{"name":"start_after_1900","description":"Flag: medication_start_at >= timestamp '1900-01-01'"},{"name":"valid_medication_dates","description":"Flag: medication_end_at is null or medication_end_at >= medication_start_at"}]},"int_encounters__enriched":{"description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","materialized":"view","columns":[{"name":"condition_count","description":"Number of conditions diagnosed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID), sourced from fct_encounters","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_count","description":"Number of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_count","description":"Number of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_condition_cost","description":"Total cost associated with conditions (currently always 0 per model logic)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_cost","description":"Total cost of all clinical events (conditions + medications + procedures). Note: Condition costs are currently set to 0 in the model.\n","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_count","description":"Total count of all clinical events (conditions + medications + procedures)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_medication_cost","description":"Total cost of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_procedure_cost","description":"Total cost of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"int_patients__with_conditions":{"description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","materialized":"view","columns":[{"name":"active_conditions","description":"Number of currently active conditions (no end date)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"chronic_condition_count","description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"first_condition_date","description":"Date of earliest condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"has_diabetes","description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_heart_disease","description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_hypertension","description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_condition_date","description":"Date of most recent condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_conditions","description":"Total number of conditions ever diagnosed for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__conditions":{"description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"condition_code","description":"SNOMED CT code for the condition","tests":[{"name":"not null"}]},{"name":"condition_description","description":"Description of the condition","tests":[{"name":"not null"}]},{"name":"condition_end_date","description":"Date when condition resolved (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"condition_id","description":"Surrogate key for the condition record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_start_date","description":"Date when condition was diagnosed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__observations":{"description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"encounter_id","description":"Foreign key to the encounter. Note: ~10% of observations have null encounter_id (from external sources or historical data imports).\n","tests":[{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"observation_at","description":"Timestamp when observation was recorded","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"observation_code","description":"LOINC code for the observation","tests":[{"name":"not null"}]},{"name":"observation_description","description":"Description of the observation","tests":[{"name":"not null"}]},{"name":"observation_id","description":"Surrogate key for the observation record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"observation_type","description":"Type of observation (numeric, text, etc.)"},{"name":"observation_value","description":"Value of the observation (stored as string)"},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"units","description":"Units of measurement"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__organizations":{"description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Unique organization identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__procedures":{"description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost of the procedure","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"procedure_at","description":"Timestamp when the procedure was performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_code","description":"SNOMED CT code for the procedure","tests":[{"name":"not null"}]},{"name":"procedure_description","description":"Description of the procedure","tests":[{"name":"not null"}]},{"name":"procedure_id","description":"Surrogate key for the procedure record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of the procedure"},{"name":"reason_description","description":"Description of the reason for the procedure"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__providers":{"description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Foreign key to the provider's organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"provider_id","description":"Unique provider identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
 <script>
 
 /* cute-dbt shared appearance engine v1 (cute-dbt#242)
diff --git a/examples/playground-report.html b/examples/playground-report.html
index beec41a..d86dba6 100644
--- a/examples/playground-report.html
+++ b/examples/playground-report.html
@@ -2580,7 +2580,7 @@ <h2>Expected</h2>
 <div class="lens-panel" role="tabpanel" id="lens-panel-project" aria-labelledby="lens-tab-project" data-lens="project" data-testid="lens-panel-project" hidden>
 </div>
 
-<script type="application/json" id="cute-dbt-data">{"baseline":"tests/fixtures/playground-baseline.json","models":[{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final select)\u001f*":{"start":{"line":91,"col":8,"byte":2540},"end":{"line":91,"col":9,"byte":2541}},"sequenced\u001f_loaded_at":{"start":{"line":51,"col":11,"byte":1159},"end":{"line":51,"col":42,"byte":1190}},"sequenced\u001faddress":{"start":{"line":17,"col":11,"byte":338},"end":{"line":17,"col":18,"byte":345}},"sequenced\u001famount_covered":{"start":{"line":24,"col":11,"byte":452},"end":{"line":24,"col":25,"byte":466}},"sequenced\u001famount_uncovered":{"start":{"line":25,"col":11,"byte":477},"end":{"line":25,"col":27,"byte":493}},"sequenced\u001faverage_quality_of_life_score":{"start":{"line":40,"col":11,"byte":856},"end":{"line":40,"col":40,"byte":885}},"sequenced\u001fcity":{"start":{"line":18,"col":11,"byte":356},"end":{"line":18,"col":15,"byte":360}},"sequenced\u001fcoverage_rate":{"start":{"line":44,"col":11,"byte":948},"end":{"line":48,"col":29,"byte":1127}},"sequenced\u001fcovered_encounters":{"start":{"line":29,"col":11,"byte":554},"end":{"line":29,"col":29,"byte":572}},"sequenced\u001fcovered_immunizations":{"start":{"line":35,"col":11,"byte":736},"end":{"line":35,"col":32,"byte":757}},"sequenced\u001fcovered_medications":{"start":{"line":31,"col":11,"byte":614},"end":{"line":31,"col":30,"byte":633}},"sequenced\u001fcovered_procedures":{"start":{"line":33,"col":11,"byte":676},"end":{"line":33,"col":29,"byte":694}},"sequenced\u001fmember_months":{"start":{"line":41,"col":11,"byte":896},"end":{"line":41,"col":24,"byte":909}},"sequenced\u001fpayer_id":{"start":{"line":13,"col":11,"byte":275},"end":{"line":13,"col":19,"byte":283}},"sequenced\u001fpayer_key":{"start":{"line":10,"col":9,"byte":190},"end":{"line":10,"col":59,"byte":240}},"sequenced\u001fpayer_name":{"start":{"line":16,"col":11,"byte":317},"end":{"line":16,"col":21,"byte":327}},"sequenced\u001fphone":{"start":{"line":21,"col":11,"byte":406},"end":{"line":21,"col":16,"byte":411}},"sequenced\u001frevenue":{"start":{"line":26,"col":11,"byte":504},"end":{"line":26,"col":18,"byte":511}},"sequenced\u001fstate":{"start":{"line":19,"col":11,"byte":371},"end":{"line":19,"col":16,"byte":376}},"sequenced\u001funcovered_encounters":{"start":{"line":30,"col":11,"byte":583},"end":{"line":30,"col":31,"byte":603}},"sequenced\u001funcovered_immunizations":{"start":{"line":36,"col":11,"byte":768},"end":{"line":36,"col":34,"byte":791}},"sequenced\u001funcovered_medications":{"start":{"line":32,"col":11,"byte":644},"end":{"line":32,"col":32,"byte":665}},"sequenced\u001funcovered_procedures":{"start":{"line":34,"col":11,"byte":705},"end":{"line":34,"col":31,"byte":725}},"sequenced\u001funique_customers":{"start":{"line":39,"col":11,"byte":829},"end":{"line":39,"col":27,"byte":845}},"sequenced\u001fzip_code":{"start":{"line":20,"col":11,"byte":387},"end":{"line":20,"col":19,"byte":395}},"source\u001f*":{"start":{"line":4,"col":12,"byte":30},"end":{"line":4,"col":13,"byte":31}},"unknown_member\u001f_loaded_at":{"start":{"line":82,"col":11,"byte":2408},"end":{"line":82,"col":42,"byte":2439}},"unknown_member\u001faddress":{"start":{"line":62,"col":16,"byte":1411},"end":{"line":62,"col":43,"byte":1438}},"unknown_member\u001famount_covered":{"start":{"line":67,"col":16,"byte":1620},"end":{"line":67,"col":49,"byte":1653}},"unknown_member\u001famount_uncovered":{"start":{"line":68,"col":16,"byte":1669},"end":{"line":68,"col":51,"byte":1704}},"unknown_member\u001faverage_quality_of_life_score":{"start":{"line":79,"col":16,"byte":2253},"end":{"line":79,"col":64,"byte":2301}},"unknown_member\u001fcity":{"start":{"line":63,"col":16,"byte":1454},"end":{"line":63,"col":40,"byte":1478}},"unknown_member\u001fcoverage_rate":{"start":{"line":81,"col":16,"byte":2365},"end":{"line":81,"col":48,"byte":2397}},"unknown_member\u001fcovered_encounters":{"start":{"line":70,"col":16,"byte":1762},"end":{"line":70,"col":53,"byte":1799}},"unknown_member\u001fcovered_immunizations":{"start":{"line":76,"col":16,"byte":2088},"end":{"line":76,"col":56,"byte":2128}},"unknown_member\u001fcovered_medications":{"start":{"line":72,"col":16,"byte":1870},"end":{"line":72,"col":54,"byte":1908}},"unknown_member\u001fcovered_procedures":{"start":{"line":74,"col":16,"byte":1980},"end":{"line":74,"col":53,"byte":2017}},"unknown_member\u001fmember_months":{"start":{"line":80,"col":16,"byte":2317},"end":{"line":80,"col":48,"byte":2349}},"unknown_member\u001fpayer_id":{"start":{"line":60,"col":11,"byte":1334},"end":{"line":60,"col":32,"byte":1355}},"unknown_member\u001fpayer_key":{"start":{"line":59,"col":10,"byte":1309},"end":{"line":59,"col":24,"byte":1323}},"unknown_member\u001fpayer_name":{"start":{"line":61,"col":11,"byte":1366},"end":{"line":61,"col":40,"byte":1395}},"unknown_member\u001fphone":{"start":{"line":66,"col":16,"byte":1579},"end":{"line":66,"col":41,"byte":1604}},"unknown_member\u001frevenue":{"start":{"line":69,"col":16,"byte":1720},"end":{"line":69,"col":42,"byte":1746}},"unknown_member\u001fstate":{"start":{"line":64,"col":16,"byte":1494},"end":{"line":64,"col":41,"byte":1519}},"unknown_member\u001funcovered_encounters":{"start":{"line":71,"col":16,"byte":1815},"end":{"line":71,"col":55,"byte":1854}},"unknown_member\u001funcovered_immunizations":{"start":{"line":77,"col":16,"byte":2144},"end":{"line":77,"col":58,"byte":2186}},"unknown_member\u001funcovered_medications":{"start":{"line":73,"col":16,"byte":1924},"end":{"line":73,"col":56,"byte":1964}},"unknown_member\u001funcovered_procedures":{"start":{"line":75,"col":16,"byte":2033},"end":{"line":75,"col":55,"byte":2072}},"unknown_member\u001funique_customers":{"start":{"line":78,"col":16,"byte":2202},"end":{"line":78,"col":51,"byte":2237}},"unknown_member\u001fzip_code":{"start":{"line":65,"col":16,"byte":1535},"end":{"line":65,"col":44,"byte":1563}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total amount)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_rate","max_value":1,"min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"coverage_rate is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_coverage_rate__1__0__coverage_rate_is_not_null.8999cddc49"}],"documented":true},"covered_encounters":{"description":"Number of encounters covered","documented":true},"covered_immunizations":{"description":"Number of immunizations covered","documented":true},"covered_medications":{"description":"Number of medications covered","documented":true},"covered_procedures":{"description":"Number of procedures covered","documented":true},"member_months":{"description":"Total member months","documented":true},"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_id","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue collected","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"revenue","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"revenue is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_revenue__0__revenue_is_not_null.0bff623461"}],"documented":true},"state":{"description":"State","documented":true},"uncovered_encounters":{"description":"Number of encounters not covered","documented":true},"uncovered_immunizations":{"description":"Number of immunizations not covered","documented":true},"uncovered_medications":{"description":"Number of medications not covered","documented":true},"uncovered_procedures":{"description":"Number of procedures not covered","documented":true},"unique_customers":{"description":"Number of unique customers","documented":true},"zip_code":{"description":"ZIP code","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__payers"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"payer_id"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"payer_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"payer_name"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"payer_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"address"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"address"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"zip_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"zip_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"phone"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"phone"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"amount_covered"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"amount_covered"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"amount_uncovered"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"amount_uncovered"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"revenue"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"revenue"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_encounters"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_encounters"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_encounters"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_encounters"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_medications"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_medications"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_medications"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_medications"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_procedures"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_procedures"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_procedures"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_procedures"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_immunizations"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_immunizations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_immunizations"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_immunizations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"unique_customers"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"unique_customers"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"average_quality_of_life_score"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"average_quality_of_life_score"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"member_months"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"member_months"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_payers\n    description: >\n      Payer dimension containing insurance payers.\n      Includes an unknown member row (payer_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 5\n          config:\n            severity: warn\n    columns:\n      - name: payer_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: payer_id\n        description: Natural payer identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: payer_name\n        description: Name of the insurance payer\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: amount_covered\n        description: Total amount covered by payer\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"amount_covered is not null\"\n              config:\n                severity: warn\n\n      - name: amount_uncovered\n        description: Total amount not covered by payer\n\n      - name: revenue\n        description: Total revenue collected\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: covered_encounters\n        description: Number of encounters covered\n\n      - name: uncovered_encounters\n        description: Number of encounters not covered\n\n      - name: covered_medications\n        description: Number of medications covered\n\n      - name: uncovered_medications\n        description: Number of medications not covered\n\n      - name: covered_procedures\n        description: Number of procedures covered\n\n      - name: uncovered_procedures\n        description: Number of procedures not covered\n\n      - name: covered_immunizations\n        description: Number of immunizations covered\n\n      - name: uncovered_immunizations\n        description: Number of immunizations not covered\n\n      - name: unique_customers\n        description: Number of unique customers\n\n      - name: average_quality_of_life_score\n        description: Average quality of life score for members\n\n      - name: member_months\n        description: Total member months\n\n      - name: coverage_rate\n        description: Derived coverage rate (amount_covered / total amount)\n        data_tests:\n          # Coverage rate must be 0-1 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 1\n              row_condition: \"coverage_rate is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt's authoring-YAML drawer (since #69) attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test to the test's authored slice. This block demonstrates the\n  # LEADING bracket — useful for stating the test's intent before the\n  # reader sees the assertion (cute-dbt#73).\n  - name: test_dim_payers_injects_unknown_sentinel\n    description: |\n      dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\n      with the sequenced source rows. This test verifies the sentinel\n      survives the union and the sequenced rows are surrogate-keyed\n      starting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\n      dict format only compares listed columns, so non-deterministic\n      outputs (`_loaded_at` via `current_timestamp`) are excluded from\n      assertion automatically.\n    model: dim_payers\n    # The INSIDE bracket — comments at deeper indent than `- name:` ride\n    # along with the body slice as raw text. Cute-dbt preserves them\n    # verbatim in the Authoring YAML drawer (cute-dbt#73).\n    given:\n      - input: ref('stg_synthea__payers')\n        format: dict\n        rows:\n          - payer_id: 'a00000aa-0000-0000-0000-000000000001'\n            payer_name: 'Acme Health'\n            address: '1 Acme Way'\n            city: 'Boston'\n            state: 'MA'\n            zip_code: '02101'\n            phone: '555-0100'\n            amount_covered: 1000.0\n            amount_uncovered: 250.0\n            revenue: 1250.0\n            covered_encounters: 10\n            uncovered_encounters: 2\n            covered_medications: 5\n            uncovered_medications: 1\n            covered_procedures: 3\n            uncovered_procedures: 0\n            covered_immunizations: 4\n            uncovered_immunizations: 0\n            unique_customers: 8\n            average_quality_of_life_score: 0.85\n            member_months: 96\n    expect:\n      format: dict\n      rows:\n        - payer_key: -1\n          payer_id: 'UNKNOWN'\n          payer_name: 'Unknown Payer'\n        - payer_key: 1\n          payer_id: 'a00000aa-0000-0000-0000-000000000001'\n          payer_name: 'Acme Health'\n  # The TRAILING bracket — contiguous `#`-comment lines at the SAME\n  # indent as `- name:`, immediately following the test body and\n  # terminated by EOF or a blank line, are attached to this test's\n  # slice. Useful for cross-references and follow-up notes (cute-dbt#73).","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme Way"}},{"display":"1000.0","key":{"t":"number","v":"1000"}},{"display":"250.0","key":{"t":"number","v":"250"}},{"display":"0.85","key":{"t":"number","v":"0.85"}},{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"10","key":{"t":"number","v":"10"}},{"display":"4","key":{"t":"number","v":"4"}},{"display":"5","key":{"t":"number","v":"5"}},{"display":"3","key":{"t":"number","v":"3"}},{"display":"96","key":{"t":"number","v":"96"}},{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}},{"display":"555-0100","key":{"t":"str","v":"555-0100"}},{"display":"1250.0","key":{"t":"number","v":"1250"}},{"display":"MA","key":{"t":"str","v":"MA"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"8","key":{"t":"number","v":"8"}},{"display":"02101","key":{"t":"str","v":"02101"}}]}]},"rows":[{"address":"1 Acme Way","amount_covered":1000.0,"amount_uncovered":250.0,"average_quality_of_life_score":0.85,"city":"Boston","covered_encounters":10,"covered_immunizations":4,"covered_medications":5,"covered_procedures":3,"member_months":96,"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_name":"Acme Health","phone":"555-0100","revenue":1250.0,"state":"MA","uncovered_encounters":2,"uncovered_immunizations":0,"uncovered_medications":1,"uncovered_procedures":0,"unique_customers":8,"zip_code":"02101"}],"format":"dict","column_meta":{"address":{"description":"Street address"},"amount_covered":{"description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"amount_uncovered":{"description":"Total amount not covered by payer"},"average_quality_of_life_score":{"description":"Average quality of life score for members"},"city":{"description":"City"},"covered_encounters":{"description":"Number of encounters covered"},"covered_immunizations":{"description":"Number of immunizations covered"},"covered_medications":{"description":"Number of medications covered"},"covered_procedures":{"description":"Number of procedures covered"},"member_months":{"description":"Total member months"},"payer_id":{"description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]},"phone":{"description":"Phone number"},"revenue":{"description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"state":{"description":"State where headquartered"},"uncovered_encounters":{"description":"Number of encounters not covered"},"uncovered_immunizations":{"description":"Number of immunizations not covered"},"uncovered_medications":{"description":"Number of medications not covered"},"uncovered_procedures":{"description":"Number of procedures not covered"},"unique_customers":{"description":"Number of unique customers"},"zip_code":{"description":"ZIP code (stored as numeric, 5 digits)"}}}],"expected":{"table":{"columns":["payer_id","payer_key","payer_name"],"rows":[{"cells":[{"display":"UNKNOWN","key":{"t":"str","v":"UNKNOWN"}},{"display":"-1","key":{"t":"number","v":"-1"}},{"display":"Unknown Payer","key":{"t":"str","v":"Unknown Payer"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}}]}]},"rows":[{"payer_id":"UNKNOWN","payer_key":-1,"payer_name":"Unknown Payer"},{"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_key":1,"payer_name":"Acme Health"}],"format":"dict","column_meta":{"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_payers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"]},"evidence":[{"label":"unique_key","value":"payer_key"}],"degraded":[{"by":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2","causes":["severity: warn — a failing run warns instead of failing, so this backing is advisory, not gating"]}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_payers","construct":"union[final]","verdict":{"status":"unknown"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"suppressed":{"source":"pragma","reason":"unknown_member is a constant sentinel arm, exercised by design"},"pin_node":"final"}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","dag":{"nodes":[{"id":"valid_encounters","role":"import"},{"id":"conditions","role":"import"},{"id":"medications","role":"import"},{"id":"procedures","role":"import"},{"id":"all_events","role":"transform"},{"id":"patients","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_clinical_events.sql","role":"final"}],"edges":[{"from":"conditions","to":"all_events","edge_type":"union_all"},{"from":"medications","to":"all_events","edge_type":"union_all"},{"from":"procedures","to":"all_events","edge_type":"union_all"},{"from":"all_events","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"encounters","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_encounter_id.5c743b1ef7"}],"documented":true},"encounter_key":{"description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_encounter_key.cc0e40eeb8"},{"kind":"relationships","kwargs":{"column_name":"encounter_key","field":"encounter_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","to":"ref('fct_encounters')"},"node_id":"test.healthcare_analytics.relationships_fct_clinical_events_encounter_key__encounter_key__ref_fct_encounters_.7a30909f42"}],"documented":true},"event_cost":{"description":"Cost of the clinical event","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"event_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","row_condition":"event_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_clinical_events_event_cost__0__event_cost_is_not_null.2c2582a25b"}],"documented":true},"event_date_key":{"description":"Foreign key to dim_date for event start","tests":[{"kind":"not_null","kwargs":{"column_name":"event_date_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_clinical_events_event_date_key.d605213965"},{"kind":"relationships","kwargs":{"column_name":"event_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_clinical_events')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_clinical_events_event_date_key__date_key__ref_dim_date_.0995b8993f"}],"documented":true},"event_end_date":{"description":"End date of the clinical event (null if ongoing or 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unique_key='clinical_event_key'\n    )\n}}\n\nwith valid_encounters as (\n    select encounter_id\n    from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from {{ ref('stg_synthea__conditions') }} as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from {{ ref('stg_synthea__medications') }} as med\n    {{ quarantine_filter() }}\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from {{ ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_clinical_events\n    description: >\n      Unified clinical events fact table combining conditions, medications, and procedures.\n      Each row represents a single clinical event with standardized structure.\n      Links to patient and encounter dimensions.\n    data_tests:\n      # Row count validation - should be sum of conditions + medications + procedures (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 60000\n          config:\n            severity: warn\n    columns:\n      - name: clinical_event_key\n        description: Surrogate key for the clinical event\n        data_tests:\n          - unique\n          - not_null\n\n      - name: event_type\n        description: Type of clinical event (CONDITION, MEDICATION, PROCEDURE)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['CONDITION', 'MEDICATION', 'PROCEDURE']\n\n      - name: event_id\n        description: Natural event identifier (condition_id, medication_id, or procedure_id)\n        data_tests:\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('fct_encounters')\n              field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - not_null\n\n      - name: event_date_key\n        description: Foreign key to dim_date for event start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"event_date_key != -1\"\n\n      - name: event_start_date\n        description: Start date of the clinical event\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: event_end_date\n        description: End date of the clinical event (null if ongoing or one-time)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"event_end_date is not null\"\n              config:\n                severity: warn\n\n      - name: code\n        description: Clinical code (SNOMED-CT or RxNorm)\n        data_tests:\n          - not_null\n\n      - name: code_system\n        description: Code system (SNOMED-CT or RXNORM)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['SNOMED-CT', 'RXNORM']\n\n      - name: description\n        description: Description of the clinical event\n        data_tests:\n          - not_null\n\n      - name: reason_code\n        description: Clinical code for the reason of the event\n\n      - name: reason_description\n        description: Description of the reason for the event\n\n      - name: event_cost\n        description: Cost of the clinical event\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"event_cost is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_encounters_incremental","path":"models/marts/core/fct_encounters_incremental.sql","description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","dag":{"nodes":[{"id":"source","role":"import"},{"id":"(final select)","label":"fct_encounters_incremental.sql","role":"final"}],"edges":[{"from":"source","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","node_spans":{"(final select)":{"start":{"line":7,"col":1,"byte":90},"end":{"line":16,"col":1,"byte":299}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":88}}},"column_spans":{"(final select)\u001fencounter_id":{"start":{"line":8,"col":5,"byte":101},"end":{"line":8,"col":17,"byte":113}},"(final select)\u001fencounter_type":{"start":{"line":10,"col":7,"byte":137},"end":{"line":10,"col":21,"byte":151}},"(final select)\u001fpatient_id":{"start":{"line":9,"col":7,"byte":120},"end":{"line":9,"col":17,"byte":130}},"(final select)\u001fstart_time":{"start":{"line":11,"col":7,"byte":158},"end":{"line":11,"col":17,"byte":168}},"(final select)\u001ftotal_cost":{"start":{"line":12,"col":7,"byte":175},"end":{"line":12,"col":17,"byte":185}},"source\u001f*":{"start":{"line":4,"col":12,"byte":30},"end":{"line":4,"col":13,"byte":31}}}},"column_lineage":{"context":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_incremental_encounter_id.b165c01d02"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"}],"documented":true},"encounter_type":{"description":"Visit class (wellness, emergency, ...)","documented":true},"patient_id":{"description":"Patient the encounter belongs to","documented":true},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark","documented":true},"total_cost":{"description":"Total claim cost for the encounter","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"start_time"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"start_time"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"total_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_cost"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='incremental',\n        unique_key='encounter_id',\n        incremental_strategy='merge',\n        on_schema_change='append_new_columns'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__encounters') }}\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost\nfrom source\n{% if is_incremental() %}\nwhere start_time > (select max(start_time) from {{ this }})\n{% endif %}","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  # cute-dbt#165 — column-header tooltip dogfood on the incremental model:\n  # authored descriptions + unique/not_null on the merge key, so the\n  # playground golden surfaces the th tooltips on this model's `this`\n  # given, ref given, and expect tables.\n  - name: fct_encounters_incremental\n    description: >\n      Incremental encounter accumulator. On incremental runs it only merges\n      encounters newer than the loaded start_time high-water mark.\n    columns:\n      - name: encounter_id\n        description: Natural key of the encounter (unique per visit)\n        data_tests:\n          - unique\n          - not_null\n      - name: patient_id\n        description: Patient the encounter belongs to\n      - name: encounter_type\n        description: Visit class (wellness, emergency, ...)\n      - name: start_time\n        description: Encounter start timestamp - the incremental high-water mark\n      - name: total_cost\n        description: Total claim cost for the encounter"},"tests":[{"id":"unit_test.healthcare_analytics.fct_encounters_incremental.test_fct_encounters_incremental_appends_new_encounters","name":"test_fct_encounters_incremental_appends_new_encounters","target_model":"fct_encounters_incremental","changed":true,"description":"fct_encounters_incremental appends encounters newer than the latest loaded start_time. With is_incremental() true only the new encounter is merged; Expected is that single inserted row (the materialization delta), not the resulting table.","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt#145 — an incremental-model unit test. `overrides.macros.\n  # is_incremental: true` drives the model's is_incremental() branch,\n  # `given: - input: this` supplies the model's prior state, and Expected is\n  # the rows MERGED/INSERTED by the run — not the final table after the merge.\n  - name: test_fct_encounters_incremental_appends_new_encounters\n    description: |\n      fct_encounters_incremental appends encounters newer than the latest\n      already-loaded start_time. With is_incremental() true, only the new\n      encounter (e2) is merged; the prior row (e1, supplied via `this`) is\n      untouched, so Expected is the single inserted row — the materialization\n      delta, not the resulting table.\n    model: fct_encounters_incremental\n    overrides:\n      macros:\n        is_incremental: true\n    given:\n      - input: this\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n      - input: ref('stg_synthea__encounters')\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n          - encounter_id: 'e2'\n            patient_id: 'p2'\n            encounter_type: 'emergency'\n            start_time: '2025-02-01 14:30:00'\n            total_cost: 500.0\n    expect:\n      format: dict\n      rows:\n        - encounter_id: 'e2'\n          patient_id: 'p2'\n          encounter_type: 'emergency'\n          start_time: '2025-02-01 14:30:00'\n          total_cost: 500.0","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}},{"input":"ref('stg_synthea__encounters')","bound_to_node":"source","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]},{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0},{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"patient_id":{"description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}}}],"expected":{"table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"int_dq_quarantine__encounters","path":"models/intermediate/quarantine/int_dq_quarantine__encounters.sql","description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod"},"code_map":{"compiled":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":11,"col":40,"byte":193}}},"column_spans":{"(final select)\u001f*":{"start":{"line":7,"col":8,"byte":69},"end":{"line":7,"col":9,"byte":70}}}},"column_lineage":{"context":{"encounter_id":{"description":"Natural key for the encounter (from source system)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_encounter_id.b3a18316f7"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.unique_int_dq_quarantine__encounters_encounter_id.31321ef55a"}],"documented":true},"end_after_1900":{"description":"Flag: encounter_end_at >= timestamp '1900-01-01'","documented":true},"failed_dq_tests":{"description":"Array of failed validation test names","tests":[{"kind":"not_null","kwargs":{"column_name":"failed_dq_tests","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_failed_dq_tests.4bad065925"}],"documented":true},"is_dq_valid":{"description":"Data quality validity flag - should always be false in quarantine","tests":[{"kind":"accepted_values","kwargs":{"column_name":"is_dq_valid","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}","values":[false]},"node_id":"test.healthcare_analytics.accepted_values_int_dq_quarantine__encounters_is_dq_valid__False.be192883a9"},{"kind":"not_null","kwargs":{"column_name":"is_dq_valid","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_is_dq_valid.2c340f0519"}],"documented":true},"no_future_encounter_dates":{"description":"Flag: encounter_start_at \u003c= current_timestamp","documented":true},"start_after_1900":{"description":"Flag: encounter_start_at >= timestamp '1900-01-01'","documented":true},"valid_encounter_timestamps":{"description":"Flag: encounter_end_at >= encounter_start_at","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__encounters',\n    description='Encounters quarantined due to data quality violations'\n) }}\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/intermediate/quarantine/_quarantine__models.yml","missing":"Schema file models/intermediate/quarantine/_quarantine__models.yml was not found under the project root."},"tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","model_yaml":{"missing":"No schema file declares this model in the manifest — there is no authored models: entry to show."},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final select)\u001f*":{"start":{"line":30,"col":8,"byte":542},"end":{"line":30,"col":9,"byte":543}},"grid\u001fmonth_actual":{"start":{"line":23,"col":11,"byte":423},"end":{"line":23,"col":30,"byte":442}},"grid\u001fmonth_key":{"start":{"line":21,"col":14,"byte":312},"end":{"line":21,"col":85,"byte":383}},"grid\u001fmonth_name":{"start":{"line":24,"col":11,"byte":453},"end":{"line":24,"col":28,"byte":470}},"grid\u001fstate":{"start":{"line":25,"col":11,"byte":481},"end":{"line":25,"col":23,"byte":493}},"grid\u001fyear_actual":{"start":{"line":22,"col":11,"byte":394},"end":{"line":22,"col":29,"byte":412}},"months\u001fmonth_actual":{"start":{"line":8,"col":11,"byte":71},"end":{"line":8,"col":23,"byte":83}},"months\u001fmonth_name":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":21,"byte":104}},"months\u001fyear_actual":{"start":{"line":7,"col":9,"byte":49},"end":{"line":7,"col":20,"byte":60}},"states\u001fstate":{"start":{"line":14,"col":21,"byte":183},"end":{"line":14,"col":26,"byte":188}}}},"column_lineage":{"context":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_date_state_grid\n    description: |\n      Dense month x state reporting scaffold.\n      One row per (calendar month x patient state) combination — the full\n      cartesian product of dim_date months and the non-null states observed\n      in patient staging. Left-join sparse activity facts against this grid\n      so zero-activity cells render as explicit zeros.\n      Grain: month_key x state\n\n      Use cases:\n      - Dense time-series dashboards (no missing month/state cells)\n      - Zero-activity gap detection\n      - cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n\n    data_tests:\n      # New-style `arguments:` nesting (fusion-clean; dbt-core 1.10+).\n      # The legacy top-level-args form elsewhere in this file predates\n      # the planned dbt-autofix sweep.\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - month_key\n              - state\n\n    config:\n      tags: ['analytics', 'reporting', 'marts']\n      meta:\n        grain: month_key x state\n        owner: healthcare_analytics\n        uses:\n          - \"Dense reporting grids\"\n          - \"Zero-activity gap detection\"\n\n    columns:\n      - name: month_key\n        description: \"Month surrogate key in YYYYMM integer form (e.g., 202401)\"\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: \"Calendar year of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_actual\n        description: \"Calendar month number (1-12) of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_name\n        description: \"Full month name (e.g., January)\"\n\n      - name: state\n        description: \"Patient state, distinct and non-null from staging\"\n        data_tests:\n          - not_null"},"tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # Cross-join demo (cute-dbt#64): mart_date_state_grid's CROSS JOIN\n  # CTE gives cute-dbt's renderer a real cross-join edge to draw. This\n  # test also mixes given formats (dict + csv) in a single unit_test.\n  - name: test_mart_date_state_grid_cross_joins_months_and_states\n    description: |\n      mart_date_state_grid CROSS JOINs the distinct calendar months from\n      dim_date with the distinct non-null patient states from staging.\n      Three daily dim_date rows collapse to two months; four patient\n      rows collapse to two states (one NULL filtered out, one duplicate\n      deduplicated). The result is the full 2 x 2 cartesian product —\n      four grid rows. Mixes a dict given (dim_date) with a csv given\n      (patients) in one test.\n    model: mart_date_state_grid\n    given:\n      - input: ref('dim_date')\n        format: dict\n        rows:\n          - date_actual: 2024-01-15\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-01-16\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-02-01\n            year_actual: 2024\n            month_actual: 2\n            month_name: 'February'\n      - input: ref('stg_synthea__patients')\n        format: csv\n        fixture: mart_date_state_grid_patients\n    expect:\n      format: csv\n      rows: |\n        month_key,year_actual,month_actual,month_name,state\n        202401,2024,1,January,Massachusetts\n        202401,2024,1,January,Connecticut\n        202402,2024,2,February,Massachusetts\n        202402,2024,2,February,Connecticut","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","table":{"columns":["patient_id","state"],"rows":[{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},{"display":"","key":{"t":"null"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":"patient_id,state\na00000aa-0000-0000-0000-000000000001,Massachusetts\na00000aa-0000-0000-0000-000000000002,Connecticut\na00000aa-0000-0000-0000-000000000003,\na00000aa-0000-0000-0000-000000000004,Massachusetts\n","format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv","column_meta":{"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_dq_summary\n    description: |\n      Data quality summary metrics for monitoring quarantine rates and validation failures.\n      One row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\n      Grain: entity_type\n\n      Use cases:\n      - Data quality monitoring dashboards\n      - Quarantine rate trending\n      - Validation failure root cause analysis\n      - Data quality SLA tracking\n\n    config:\n      tags: ['analytics', 'data_quality', 'marts']\n    meta:\n      grain: entity_type\n      owner: healthcare_analytics\n      uses:\n        - \"Data quality dashboards\"\n        - \"Quarantine monitoring\"\n        - \"Data quality alerting\"\n\n    columns:\n      - name: entity_type\n        description: \"Type of entity being measured (encounters, medications)\"\n        tests:\n          - not_null\n          - accepted_values:\n              values: ['encounters', 'medications']\n\n      - name: quarantined_count\n        description: \"Number of records failing data quality validations\"\n        tests:\n          - not_null\n\n      - name: total_count\n        description: \"Total number of records (valid + quarantined)\"\n        tests:\n          - not_null\n\n      - name: quarantine_rate_pct\n        description: \"Percentage of records quarantined (quarantined_count / total_count * 100)\"\n        tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 100\n\n      - name: failed_timestamp_validations\n        description: \"Count of records failing timestamp/date sequence validations\"\n\n      - name: failed_future_date_validations\n        description: \"Count of records with future dates\"\n\n      - name: failed_historical_end_validations\n        description: \"Count of records with end dates before 1900\"\n\n      - name: failed_historical_start_validations\n        description: \"Count of records with start dates before 1900\"\n\n      - name: _generated_at\n        description: \"Timestamp when summary was generated\""},"tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 1 — the slicer attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test (no blank-line gap below) to this test's authored slice\n  # (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_combines_encounter_and_medication_metrics\n    description: |\n      mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\n      and stg_synthea__medications. This test mocks two encounter rows\n      (one invalid) and two medication rows (both invalid), then asserts\n      the per-entity counts + quarantine rate. Uses csv format for both\n      GIVEN and EXPECT — compact tabular form across many boolean\n      dq-flag columns.\n    model: mart_dq_summary\n    # INSIDE bracket for test 1 — comments at deeper indent than\n    # `- name:` ride along with the body slice as raw text (cute-dbt#69\n    # / #73).\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: csv\n        rows: |\n          is_dq_valid,valid_encounter_timestamps,no_future_encounter_dates,end_after_1900,start_after_1900\n          false,false,true,true,true\n          true,true,true,true,true\n      - input: ref(\"stg_synthea__medications\")\n        format: csv\n        rows: |\n          is_dq_valid,valid_medication_dates,no_future_medication_dates,start_after_1900,end_after_1900_if_present\n          false,false,true,true,true\n          false,true,false,true,true\n    expect:\n      format: csv\n      rows: |\n        entity_type,quarantined_count,total_count,quarantine_rate_pct,failed_timestamp_validations\n        encounters,1,2,50.00,1\n        medications,2,2,100.00,1\n  # TRAILING bracket for test 1 — the comment block butts against the\n  # body above (no blank-line gap) and is terminated below by the blank\n  # line separating the two tests (cute-dbt#69 / #73).","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":true,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. Uses sql format for the GIVEN mocks and dict format\nfor the EXPECT — dict expects only compare listed columns, so\nnon-deterministic outputs (`_generated_at` from\n`current_timestamp`) are excluded from comparison.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 2 — sits between the blank-line\n  # terminator above and `- name:` below with no further gap, so the\n  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_zero_quarantined_when_all_valid\n    description: |\n      mart_dq_summary should report zero quarantined_count and 0.00\n      quarantine_rate_pct when every upstream row passes data-quality\n      validations. Uses sql format for the GIVEN mocks and dict format\n      for the EXPECT — dict expects only compare listed columns, so\n      non-deterministic outputs (`_generated_at` from\n      `current_timestamp`) are excluded from comparison.\n    model: mart_dq_summary\n    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /\n    # #73). Demonstrates that the slicer ride-along applies to every\n    # unit_test, not just the first in the file.\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_encounter_timestamps\n            , true as no_future_encounter_dates\n            , true as end_after_1900\n            , true as start_after_1900\n      - input: ref(\"stg_synthea__medications\")\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_medication_dates\n            , true as no_future_medication_dates\n            , true as start_after_1900\n            , true as end_after_1900_if_present\n    expect:\n      format: dict\n      rows:\n        - entity_type: 'encounters'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n        - entity_type: 'medications'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n    overrides:\n      macros:\n        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"\n      vars:\n        dq_quarantine_threshold: 5\n      env_vars:\n        DBT_DQ_REGION: 'us-east-1'\n  # TRAILING bracket for test 2 — terminated by the blank line\n  # separating it from test 3 (cute-dbt#69 / #73).","overrides":{"env_vars":{"DBT_DQ_REGION":"us-east-1"},"macros":{"dbt.current_timestamp":"'2024-01-15 00:00:00'"},"vars":{"dq_quarantine_threshold":5}},"given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["is_dq_valid","valid_encounter_timestamps","no_future_encounter_dates","end_after_1900","start_after_1900"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_encounter_timestamps\n  , true as no_future_encounter_dates\n  , true as end_after_1900\n  , true as start_after_1900","format":"sql"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["is_dq_valid","valid_medication_dates","no_future_medication_dates","start_after_1900","end_after_1900_if_present"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_medication_dates\n  , true as no_future_medication_dates\n  , true as start_after_1900\n  , true as end_after_1900_if_present","format":"sql"}],"expected":{"table":{"columns":["entity_type","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]}]},"rows":[{"entity_type":"encounters","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1},{"entity_type":"medications","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1}],"format":"dict","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"entity_type"},{"label":"exists but disabled","value":"test.healthcare_analytics.unique_mart_dq_summary_entity_type.4f2a9c7d10 — a uniqueness test on (entity_type) is declared but disabled (config.enabled: false); it asserts nothing and never counts as coverage"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}]},{"name":"stg_synthea__patients","path":"models/staging/synthea/stg_synthea__patients.sql","description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__patients.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"patients\"\n)"},"code_map":{"compiled":"with source as (\n    select * from \"memory\".\"main\".\"patients\"\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":53,"col":1,"byte":1155},"end":{"line":55,"col":40,"byte":1215}},"final":{"start":{"line":46,"col":1,"byte":1061},"end":{"line":51,"col":2,"byte":1153}},"renamed":{"start":{"line":5,"col":1,"byte":66},"end":{"line":44,"col":2,"byte":1058}},"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":3,"col":2,"byte":63}}},"column_spans":{"(final select)\u001f*":{"start":{"line":53,"col":8,"byte":1162},"end":{"line":53,"col":9,"byte":1163}},"final\u001f*":{"start":{"line":48,"col":9,"byte":1091},"end":{"line":48,"col":10,"byte":1092}},"final\u001f_loaded_at":{"start":{"line":49,"col":11,"byte":1103},"end":{"line":49,"col":42,"byte":1134}},"renamed\u001faddress":{"start":{"line":32,"col":11,"byte":740},"end":{"line":32,"col":29,"byte":758}},"renamed\u001fbirth_date":{"start":{"line":22,"col":11,"byte":464},"end":{"line":22,"col":34,"byte":487}},"renamed\u001fbirth_place":{"start":{"line":31,"col":11,"byte":704},"end":{"line":31,"col":36,"byte":729}},"renamed\u001fcity":{"start":{"line":33,"col":11,"byte":769},"end":{"line":33,"col":23,"byte":781}},"renamed\u001fcounty":{"start":{"line":35,"col":11,"byte":817},"end":{"line":35,"col":27,"byte":833}},"renamed\u001fdeath_date":{"start":{"line":23,"col":11,"byte":498},"end":{"line":23,"col":34,"byte":521}},"renamed\u001fdrivers_license":{"start":{"line":27,"col":11,"byte":608},"end":{"line":27,"col":37,"byte":634}},"renamed\u001fethnicity":{"start":{"line":18,"col":11,"byte":377},"end":{"line":18,"col":33,"byte":399}},"renamed\u001ffirst_name":{"start":{"line":11,"col":11,"byte":173},"end":{"line":11,"col":30,"byte":192}},"renamed\u001fgender":{"start":{"line":16,"col":11,"byte":327},"end":{"line":16,"col":27,"byte":343}},"renamed\u001fhealthcare_coverage":{"start":{"line":42,"col":11,"byte":998},"end":{"line":42,"col":53,"byte":1040}},"renamed\u001fhealthcare_expenses":{"start":{"line":41,"col":11,"byte":945},"end":{"line":41,"col":53,"byte":987}},"renamed\u001flast_name":{"start":{"line":12,"col":11,"byte":203},"end":{"line":12,"col":28,"byte":220}},"renamed\u001flatitude":{"start":{"line":37,"col":11,"byte":870},"end":{"line":37,"col":26,"byte":885}},"renamed\u001flongitude":{"start":{"line":38,"col":11,"byte":896},"end":{"line":38,"col":27,"byte":912}},"renamed\u001fmaiden_name":{"start":{"line":15,"col":11,"byte":295},"end":{"line":15,"col":32,"byte":316}},"renamed\u001fmarital_status":{"start":{"line":19,"col":11,"byte":410},"end":{"line":19,"col":36,"byte":435}},"renamed\u001fname_prefix":{"start":{"line":13,"col":11,"byte":231},"end":{"line":13,"col":32,"byte":252}},"renamed\u001fname_suffix":{"start":{"line":14,"col":11,"byte":263},"end":{"line":14,"col":32,"byte":284}},"renamed\u001fpassport_number":{"start":{"line":28,"col":11,"byte":645},"end":{"line":28,"col":38,"byte":672}},"renamed\u001fpatient_id":{"start":{"line":8,"col":9,"byte":121},"end":{"line":8,"col":25,"byte":137}},"renamed\u001frace":{"start":{"line":17,"col":11,"byte":354},"end":{"line":17,"col":23,"byte":366}},"renamed\u001fssn_hash":{"start":{"line":26,"col":11,"byte":577},"end":{"line":26,"col":31,"byte":597}},"renamed\u001fstate":{"start":{"line":34,"col":11,"byte":792},"end":{"line":34,"col":25,"byte":806}},"renamed\u001fzip_code":{"start":{"line":36,"col":11,"byte":844},"end":{"line":36,"col":26,"byte":859}},"source\u001f*":{"start":{"line":2,"col":12,"byte":28},"end":{"line":2,"col":13,"byte":29}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded into staging","documented":true},"address":{"description":"Street address","documented":true},"birth_date":{"description":"Patient date of birth","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"birth_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_stg_synthea__patients_birth_date__current_date___1900_01_01_date.372fc16e5a"},{"kind":"not_null","kwargs":{"column_name":"birth_date","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}"},"node_id":"test.healthcare_analytics.not_null_stg_synthea__patients_birth_date.4b89a0c1a7"}],"documented":true},"birth_place":{"description":"Place of birth","documented":true},"city":{"description":"City of residence","documented":true},"county":{"description":"County of residence","documented":true},"death_date":{"description":"Patient date of death (null if alive)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"death_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}","row_condition":"death_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_stg_synthea__patients_death_date__current_date___1900_01_01_date__death_date_is_not_null.5398b32b08"}],"documented":true},"drivers_license":{"description":"Driver's license number","documented":true},"ethnicity":{"description":"Patient ethnicity (hispanic or nonhispanic)","documented":true},"first_name":{"description":"Patient's first name","tests":[{"kind":"not_null","kwargs":{"column_name":"first_name","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}"},"node_id":"test.healthcare_analytics.not_null_stg_synthea__patients_first_name.5e775e06c9"}],"documented":true},"gender":{"description":"Patient gender (M or F)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"gender","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}","values":["M","F"]},"node_id":"test.healthcare_analytics.accepted_values_stg_synthea__patients_gender__M__F.22ff5a22d0"},{"kind":"expect_column_values_to_match_regex","kwargs":{"column_name":"gender","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}","regex":"^[MF]$"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_match_regex_stg_synthea__patients_gender___MF_.2b1426dd70"},{"kind":"not_null","kwargs":{"column_name":"gender","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}"},"node_id":"test.healthcare_analytics.not_null_stg_synthea__patients_gender.8be34ab253"}],"documented":true},"healthcare_coverage":{"description":"Total 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null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_stg_synthea__patients_healthcare_expenses__0__healthcare_expenses_is_not_null.997d5bc9e9"}],"documented":true},"last_name":{"description":"Patient's last name","tests":[{"kind":"not_null","kwargs":{"column_name":"last_name","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}"},"node_id":"test.healthcare_analytics.not_null_stg_synthea__patients_last_name.b0fe30b72b"}],"documented":true},"latitude":{"description":"Latitude coordinate of residence","documented":true},"longitude":{"description":"Longitude coordinate of residence","documented":true},"maiden_name":{"description":"Maiden name (if applicable)","documented":true},"marital_status":{"description":"Marital status (M=Married, S=Single, or blank)","documented":true},"name_prefix":{"description":"Name prefix (Mr., Mrs., Ms., etc.)","documented":true},"name_suffix":{"description":"Name suffix (Jr., Sr., III, etc.)","documented":true},"passport_number":{"description":"Passport number","documented":true},"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}"},"node_id":"test.healthcare_analytics.not_null_stg_synthea__patients_patient_id.f342234f74"},{"kind":"unique","kwargs":{"column_name":"patient_id","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}"},"node_id":"test.healthcare_analytics.unique_stg_synthea__patients_patient_id.ee61748f4d"}],"documented":true},"race":{"description":"Patient race","documented":true},"ssn_hash":{"description":"MD5 hash of Social Security Number (masked for privacy)","documented":true},"state":{"description":"State of residence (full state name, e.g., Massachusetts)","documented":true},"zip_code":{"description":"ZIP 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-- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/staging/synthea/_synthea__models.yml","raw":"  - name: stg_synthea__patients\n    description: >\n      Staging model for patient demographics and identifiers.\n      One row per patient with personal information, location, and financial data.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 900\n          config:\n            severity: warn\n    columns:\n      - name: patient_id\n        description: Unique patient identifier (UUID)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: first_name\n        description: Patient's first name\n        data_tests:\n          - not_null\n\n      - name: last_name\n        description: Patient's last name\n        data_tests:\n          - not_null\n\n      - name: name_prefix\n        description: Name prefix (Mr., Mrs., Ms., etc.)\n\n      - name: name_suffix\n        description: Name suffix (Jr., Sr., III, etc.)\n\n      - name: maiden_name\n        description: Maiden name (if applicable)\n\n      - name: gender\n        description: Patient gender (M or F)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['M', 'F']\n          # String pattern validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_match_regex:\n              regex: \"^[MF]$\"\n              config:\n                severity: warn\n\n      - name: race\n        description: Patient race\n\n      - name: ethnicity\n        description: Patient ethnicity (hispanic or nonhispanic)\n\n      - name: marital_status\n        description: Marital status (M=Married, S=Single, or blank)\n\n      - name: birth_date\n        description: Patient date of birth\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          # Note: Uses year extraction to avoid date type casting issues with DuckDB\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: death_date\n        description: Patient date of death (null if alive)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"death_date is not null\"\n              config:\n                severity: warn\n\n      - name: ssn_hash\n        description: MD5 hash of Social Security Number (masked for privacy)\n\n      - name: drivers_license\n        description: Driver's license number\n\n      - name: passport_number\n        description: Passport number\n\n      - name: birth_place\n        description: Place of birth\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City of residence\n\n      - name: state\n        description: State of residence (full state name, e.g., Massachusetts)\n\n      - name: county\n        description: County of residence\n\n      - name: zip_code\n        description: ZIP code\n        data_tests:\n          # String pattern validation (v0.8 - dbt_expectations)\n          # ZIP code format: 5 digits or 5+4 format\n          - dbt_expectations.expect_column_values_to_match_regex:\n              regex: \"^\\\\d{5}(-\\\\d{4})?$\"\n              row_condition: \"zip_code is not null\"\n              config:\n                severity: warn\n\n      - name: latitude\n        description: Latitude coordinate of residence\n\n      - name: longitude\n        description: Longitude coordinate of residence\n\n      - name: healthcare_expenses\n        description: Total healthcare expenses incurred\n        data_tests:\n          # Value range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_expenses is not null\"\n              config:\n                severity: warn\n\n      - name: healthcare_coverage\n        description: Total healthcare coverage received\n        data_tests:\n          # Value range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded into staging"},"tests":[{"id":"unit_test.healthcare_analytics.stg_synthea__patients.test_stg_synthea__patients_renames_and_hashes_source_columns","name":"test_stg_synthea__patients_renames_and_hashes_source_columns","target_model":"stg_synthea__patients","changed":false,"description":"stg_synthea__patients renames the raw Synthea UPPERCASE columns to\nsnake_case and hashes the SSN with md5. The given mocks the\nsynthea_raw.patients SOURCE directly (inline dict rows) — the only\nunit test in this project whose input is a source() rather than a\nref() (cute-dbt#57). Dict expect compares listed columns only, so\nthe non-deterministic _loaded_at (current_timestamp) is excluded\nfrom assertion automatically.\n","tags":[],"meta":{},"defined_in":"models/staging/synthea/_synthea__models.yml","authoring_yaml":"  - name: test_stg_synthea__patients_renames_and_hashes_source_columns\n    description: |\n      stg_synthea__patients renames the raw Synthea UPPERCASE columns to\n      snake_case and hashes the SSN with md5. The given mocks the\n      synthea_raw.patients SOURCE directly (inline dict rows) — the only\n      unit test in this project whose input is a source() rather than a\n      ref() (cute-dbt#57). Dict expect compares listed columns only, so\n      the non-deterministic _loaded_at (current_timestamp) is excluded\n      from assertion automatically.\n    model: stg_synthea__patients\n    given:\n      - input: source('synthea_raw', 'patients')\n        format: dict\n        rows:\n          - Id: 'c0000000-0000-0000-0000-000000000001'\n            FIRST: 'Ada'\n            LAST: 'Lovelace'\n            GENDER: 'F'\n            CITY: 'Boston'\n            STATE: 'Massachusetts'\n            SSN: '999-99-9999'\n    expect:\n      format: dict\n      rows:\n        - patient_id: 'c0000000-0000-0000-0000-000000000001'\n          first_name: 'Ada'\n          last_name: 'Lovelace'\n          gender: 'F'\n          city: 'Boston'\n          state: 'Massachusetts'\n          ssn_hash: '257cf696294931925796925d89aa0afb'","given":[{"input":"source('synthea_raw', 'patients')","bound_to_node":"source","table":{"columns":["CITY","FIRST","GENDER","Id","LAST","SSN","STATE"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"999-99-9999","key":{"t":"str","v":"999-99-9999"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"CITY":"Boston","FIRST":"Ada","GENDER":"F","Id":"c0000000-0000-0000-0000-000000000001","LAST":"Lovelace","SSN":"999-99-9999","STATE":"Massachusetts"}],"format":"dict","column_meta":{"Id":{"description":"Unique patient identifier (UUID)"}}}],"expected":{"table":{"columns":["city","first_name","gender","last_name","patient_id","ssn_hash","state"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"257cf696294931925796925d89aa0afb","key":{"t":"str","v":"257cf696294931925796925d89aa0afb"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"city":"Boston","first_name":"Ada","gender":"F","last_name":"Lovelace","patient_id":"c0000000-0000-0000-0000-000000000001","ssn_hash":"257cf696294931925796925d89aa0afb","state":"Massachusetts"}],"format":"dict","column_meta":{"city":{"description":"City of residence"},"first_name":{"description":"Patient's first name","tests":[{"name":"not null"}]},"gender":{"description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},"last_name":{"description":"Patient's last name","tests":[{"name":"not null"}]},"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"ssn_hash":{"description":"MD5 hash of Social Security Number (masked for privacy)"},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}}],"is_recursive":false}],"manifest_nodes":{"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at \u003c= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
+<script type="application/json" id="cute-dbt-data">{"baseline":"tests/fixtures/playground-baseline.json","models":[{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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_member\u001funcovered_encounters":{"start":{"line":71,"col":16,"byte":1815},"end":{"line":71,"col":55,"byte":1854}},"unknown_member\u001funcovered_immunizations":{"start":{"line":77,"col":16,"byte":2144},"end":{"line":77,"col":58,"byte":2186}},"unknown_member\u001funcovered_medications":{"start":{"line":73,"col":16,"byte":1924},"end":{"line":73,"col":56,"byte":1964}},"unknown_member\u001funcovered_procedures":{"start":{"line":75,"col":16,"byte":2033},"end":{"line":75,"col":55,"byte":2072}},"unknown_member\u001funique_customers":{"start":{"line":78,"col":16,"byte":2202},"end":{"line":78,"col":51,"byte":2237}},"unknown_member\u001fzip_code":{"start":{"line":65,"col":16,"byte":1535},"end":{"line":65,"col":44,"byte":1563}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total amount)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_rate","max_value":1,"min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"coverage_rate is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_coverage_rate__1__0__coverage_rate_is_not_null.8999cddc49"}],"documented":true},"covered_encounters":{"description":"Number of encounters covered","documented":true},"covered_immunizations":{"description":"Number of immunizations covered","documented":true},"covered_medications":{"description":"Number of medications covered","documented":true},"covered_procedures":{"description":"Number of procedures covered","documented":true},"member_months":{"description":"Total member months","documented":true},"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_id","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue collected","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"revenue","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"revenue is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_revenue__0__revenue_is_not_null.0bff623461"}],"documented":true},"state":{"description":"State","documented":true},"uncovered_encounters":{"description":"Number of encounters not covered","documented":true},"uncovered_immunizations":{"description":"Number of immunizations not covered","documented":true},"uncovered_medications":{"description":"Number of medications not covered","documented":true},"uncovered_procedures":{"description":"Number of procedures not covered","documented":true},"unique_customers":{"description":"Number of unique customers","documented":true},"zip_code":{"description":"ZIP 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_payers\n    description: >\n      Payer dimension containing insurance payers.\n      Includes an unknown member row (payer_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 5\n          config:\n            severity: warn\n    columns:\n      - name: payer_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: payer_id\n        description: Natural payer identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: payer_name\n        description: Name of the insurance payer\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: amount_covered\n        description: Total amount covered by payer\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"amount_covered is not null\"\n              config:\n                severity: warn\n\n      - name: amount_uncovered\n        description: Total amount not covered by payer\n\n      - name: revenue\n        description: Total revenue collected\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: covered_encounters\n        description: Number of encounters covered\n\n      - name: uncovered_encounters\n        description: Number of encounters not covered\n\n      - name: covered_medications\n        description: Number of medications covered\n\n      - name: uncovered_medications\n        description: Number of medications not covered\n\n      - name: covered_procedures\n        description: Number of procedures covered\n\n      - name: uncovered_procedures\n        description: Number of procedures not covered\n\n      - name: covered_immunizations\n        description: Number of immunizations covered\n\n      - name: uncovered_immunizations\n        description: Number of immunizations not covered\n\n      - name: unique_customers\n        description: Number of unique customers\n\n      - name: average_quality_of_life_score\n        description: Average quality of life score for members\n\n      - name: member_months\n        description: Total member months\n\n      - name: coverage_rate\n        description: Derived coverage rate (amount_covered / total amount)\n        data_tests:\n          # Coverage rate must be 0-1 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 1\n              row_condition: \"coverage_rate is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt's authoring-YAML drawer (since #69) attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test to the test's authored slice. This block demonstrates the\n  # LEADING bracket — useful for stating the test's intent before the\n  # reader sees the assertion (cute-dbt#73).\n  - name: test_dim_payers_injects_unknown_sentinel\n    description: |\n      dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\n      with the sequenced source rows. This test verifies the sentinel\n      survives the union and the sequenced rows are surrogate-keyed\n      starting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\n      dict format only compares listed columns, so non-deterministic\n      outputs (`_loaded_at` via `current_timestamp`) are excluded from\n      assertion automatically.\n    model: dim_payers\n    # The INSIDE bracket — comments at deeper indent than `- name:` ride\n    # along with the body slice as raw text. Cute-dbt preserves them\n    # verbatim in the Authoring YAML drawer (cute-dbt#73).\n    given:\n      - input: ref('stg_synthea__payers')\n        format: dict\n        rows:\n          - payer_id: 'a00000aa-0000-0000-0000-000000000001'\n            payer_name: 'Acme Health'\n            address: '1 Acme Way'\n            city: 'Boston'\n            state: 'MA'\n            zip_code: '02101'\n            phone: '555-0100'\n            amount_covered: 1000.0\n            amount_uncovered: 250.0\n            revenue: 1250.0\n            covered_encounters: 10\n            uncovered_encounters: 2\n            covered_medications: 5\n            uncovered_medications: 1\n            covered_procedures: 3\n            uncovered_procedures: 0\n            covered_immunizations: 4\n            uncovered_immunizations: 0\n            unique_customers: 8\n            average_quality_of_life_score: 0.85\n            member_months: 96\n    expect:\n      format: dict\n      rows:\n        - payer_key: -1\n          payer_id: 'UNKNOWN'\n          payer_name: 'Unknown Payer'\n        - payer_key: 1\n          payer_id: 'a00000aa-0000-0000-0000-000000000001'\n          payer_name: 'Acme Health'\n  # The TRAILING bracket — contiguous `#`-comment lines at the SAME\n  # indent as `- name:`, immediately following the test body and\n  # terminated by EOF or a blank line, are attached to this test's\n  # slice. Useful for cross-references and follow-up notes (cute-dbt#73).","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme 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ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_clinical_events\n    description: >\n      Unified clinical events fact table combining conditions, medications, and procedures.\n      Each row represents a single clinical event with standardized structure.\n      Links to patient and encounter dimensions.\n    data_tests:\n      # Row count validation - should be sum of conditions + medications + procedures (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 60000\n          config:\n            severity: warn\n    columns:\n      - name: clinical_event_key\n        description: Surrogate key for the clinical event\n        data_tests:\n          - unique\n          - not_null\n\n      - name: event_type\n        description: Type of clinical event (CONDITION, MEDICATION, PROCEDURE)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['CONDITION', 'MEDICATION', 'PROCEDURE']\n\n      - name: event_id\n        description: Natural event identifier (condition_id, medication_id, or procedure_id)\n        data_tests:\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('fct_encounters')\n              field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - not_null\n\n      - name: event_date_key\n        description: Foreign key to dim_date for event start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"event_date_key != -1\"\n\n      - name: event_start_date\n        description: Start date of the clinical event\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: event_end_date\n        description: End date of the clinical event (null if ongoing or one-time)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"event_end_date is not null\"\n              config:\n                severity: warn\n\n      - name: code\n        description: Clinical code (SNOMED-CT or RxNorm)\n        data_tests:\n          - not_null\n\n      - name: code_system\n        description: Code system (SNOMED-CT or RXNORM)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['SNOMED-CT', 'RXNORM']\n\n      - name: description\n        description: Description of the clinical event\n        data_tests:\n          - not_null\n\n      - name: reason_code\n        description: Clinical code for the reason of the event\n\n      - name: reason_description\n        description: Description of the reason for the event\n\n      - name: event_cost\n        description: Cost of the clinical event\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"event_cost is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_encounters_incremental","path":"models/marts/core/fct_encounters_incremental.sql","description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","dag":{"nodes":[{"id":"source","role":"import"},{"id":"(final select)","label":"fct_encounters_incremental.sql","role":"final"}],"edges":[{"from":"source","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","node_spans":{"(final select)":{"start":{"line":7,"col":1,"byte":90},"end":{"line":16,"col":1,"byte":299}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":88}}},"raw_zones":[{"kind":"incremental_guard","start":{"line":21,"col":1,"byte":363},"end":{"line":23,"col":12,"byte":460},"presence":"structural","node_id":"(final select)"}],"column_spans":{"(final select)\u001fencounter_id":{"start":{"line":8,"col":5,"byte":101},"end":{"line":8,"col":17,"byte":113}},"(final select)\u001fencounter_type":{"start":{"line":10,"col":7,"byte":137},"end":{"line":10,"col":21,"byte":151}},"(final select)\u001fpatient_id":{"start":{"line":9,"col":7,"byte":120},"end":{"line":9,"col":17,"byte":130}},"(final select)\u001fstart_time":{"start":{"line":11,"col":7,"byte":158},"end":{"line":11,"col":17,"byte":168}},"(final select)\u001ftotal_cost":{"start":{"line":12,"col":7,"byte":175},"end":{"line":12,"col":17,"byte":185}},"source\u001f*":{"start":{"line":4,"col":12,"byte":30},"end":{"line":4,"col":13,"byte":31}}}},"column_lineage":{"context":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_incremental_encounter_id.b165c01d02"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"}],"documented":true},"encounter_type":{"description":"Visit class (wellness, emergency, ...)","documented":true},"patient_id":{"description":"Patient the encounter belongs to","documented":true},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark","documented":true},"total_cost":{"description":"Total claim cost for the encounter","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"start_time"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"start_time"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"total_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_cost"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='incremental',\n        unique_key='encounter_id',\n        incremental_strategy='merge',\n        on_schema_change='append_new_columns'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__encounters') }}\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost\nfrom source\n{% if is_incremental() %}\nwhere start_time > (select max(start_time) from {{ this }})\n{% endif %}","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  # cute-dbt#165 — column-header tooltip dogfood on the incremental model:\n  # authored descriptions + unique/not_null on the merge key, so the\n  # playground golden surfaces the th tooltips on this model's `this`\n  # given, ref given, and expect tables.\n  - name: fct_encounters_incremental\n    description: >\n      Incremental encounter accumulator. On incremental runs it only merges\n      encounters newer than the loaded start_time high-water mark.\n    columns:\n      - name: encounter_id\n        description: Natural key of the encounter (unique per visit)\n        data_tests:\n          - unique\n          - not_null\n      - name: patient_id\n        description: Patient the encounter belongs to\n      - name: encounter_type\n        description: Visit class (wellness, emergency, ...)\n      - name: start_time\n        description: Encounter start timestamp - the incremental high-water mark\n      - name: total_cost\n        description: Total claim cost for the encounter"},"tests":[{"id":"unit_test.healthcare_analytics.fct_encounters_incremental.test_fct_encounters_incremental_appends_new_encounters","name":"test_fct_encounters_incremental_appends_new_encounters","target_model":"fct_encounters_incremental","changed":true,"description":"fct_encounters_incremental appends encounters newer than the latest loaded start_time. With is_incremental() true only the new encounter is merged; Expected is that single inserted row (the materialization delta), not the resulting table.","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt#145 — an incremental-model unit test. `overrides.macros.\n  # is_incremental: true` drives the model's is_incremental() branch,\n  # `given: - input: this` supplies the model's prior state, and Expected is\n  # the rows MERGED/INSERTED by the run — not the final table after the merge.\n  - name: test_fct_encounters_incremental_appends_new_encounters\n    description: |\n      fct_encounters_incremental appends encounters newer than the latest\n      already-loaded start_time. With is_incremental() true, only the new\n      encounter (e2) is merged; the prior row (e1, supplied via `this`) is\n      untouched, so Expected is the single inserted row — the materialization\n      delta, not the resulting table.\n    model: fct_encounters_incremental\n    overrides:\n      macros:\n        is_incremental: true\n    given:\n      - input: this\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n      - input: ref('stg_synthea__encounters')\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n          - encounter_id: 'e2'\n            patient_id: 'p2'\n            encounter_type: 'emergency'\n            start_time: '2025-02-01 14:30:00'\n            total_cost: 500.0\n    expect:\n      format: dict\n      rows:\n        - encounter_id: 'e2'\n          patient_id: 'p2'\n          encounter_type: 'emergency'\n          start_time: '2025-02-01 14:30:00'\n          total_cost: 500.0","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}},{"input":"ref('stg_synthea__encounters')","bound_to_node":"source","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]},{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0},{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"patient_id":{"description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}}}],"expected":{"table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"int_dq_quarantine__encounters","path":"models/intermediate/quarantine/int_dq_quarantine__encounters.sql","description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod"},"code_map":{"compiled":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":11,"col":40,"byte":193}}},"column_spans":{"(final select)\u001f*":{"start":{"line":7,"col":8,"byte":69},"end":{"line":7,"col":9,"byte":70}}}},"column_lineage":{"context":{"encounter_id":{"description":"Natural key for the encounter (from source system)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_encounter_id.b3a18316f7"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.unique_int_dq_quarantine__encounters_encounter_id.31321ef55a"}],"documented":true},"end_after_1900":{"description":"Flag: encounter_end_at >= timestamp '1900-01-01'","documented":true},"failed_dq_tests":{"description":"Array of failed validation test names","tests":[{"kind":"not_null","kwargs":{"column_name":"failed_dq_tests","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_failed_dq_tests.4bad065925"}],"documented":true},"is_dq_valid":{"description":"Data quality validity flag - should always be false in quarantine","tests":[{"kind":"accepted_values","kwargs":{"column_name":"is_dq_valid","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}","values":[false]},"node_id":"test.healthcare_analytics.accepted_values_int_dq_quarantine__encounters_is_dq_valid__False.be192883a9"},{"kind":"not_null","kwargs":{"column_name":"is_dq_valid","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_is_dq_valid.2c340f0519"}],"documented":true},"no_future_encounter_dates":{"description":"Flag: encounter_start_at \u003c= current_timestamp","documented":true},"start_after_1900":{"description":"Flag: encounter_start_at >= timestamp '1900-01-01'","documented":true},"valid_encounter_timestamps":{"description":"Flag: encounter_end_at >= encounter_start_at","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__encounters',\n    description='Encounters quarantined due to data quality violations'\n) }}\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/intermediate/quarantine/_quarantine__models.yml","missing":"Schema file models/intermediate/quarantine/_quarantine__models.yml was not found under the project root."},"tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","model_yaml":{"missing":"No schema file declares this model in the manifest — there is no authored models: entry to show."},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final select)\u001f*":{"start":{"line":30,"col":8,"byte":542},"end":{"line":30,"col":9,"byte":543}},"grid\u001fmonth_actual":{"start":{"line":23,"col":11,"byte":423},"end":{"line":23,"col":30,"byte":442}},"grid\u001fmonth_key":{"start":{"line":21,"col":14,"byte":312},"end":{"line":21,"col":85,"byte":383}},"grid\u001fmonth_name":{"start":{"line":24,"col":11,"byte":453},"end":{"line":24,"col":28,"byte":470}},"grid\u001fstate":{"start":{"line":25,"col":11,"byte":481},"end":{"line":25,"col":23,"byte":493}},"grid\u001fyear_actual":{"start":{"line":22,"col":11,"byte":394},"end":{"line":22,"col":29,"byte":412}},"months\u001fmonth_actual":{"start":{"line":8,"col":11,"byte":71},"end":{"line":8,"col":23,"byte":83}},"months\u001fmonth_name":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":21,"byte":104}},"months\u001fyear_actual":{"start":{"line":7,"col":9,"byte":49},"end":{"line":7,"col":20,"byte":60}},"states\u001fstate":{"start":{"line":14,"col":21,"byte":183},"end":{"line":14,"col":26,"byte":188}}}},"column_lineage":{"context":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_date_state_grid\n    description: |\n      Dense month x state reporting scaffold.\n      One row per (calendar month x patient state) combination — the full\n      cartesian product of dim_date months and the non-null states observed\n      in patient staging. Left-join sparse activity facts against this grid\n      so zero-activity cells render as explicit zeros.\n      Grain: month_key x state\n\n      Use cases:\n      - Dense time-series dashboards (no missing month/state cells)\n      - Zero-activity gap detection\n      - cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n\n    data_tests:\n      # New-style `arguments:` nesting (fusion-clean; dbt-core 1.10+).\n      # The legacy top-level-args form elsewhere in this file predates\n      # the planned dbt-autofix sweep.\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - month_key\n              - state\n\n    config:\n      tags: ['analytics', 'reporting', 'marts']\n      meta:\n        grain: month_key x state\n        owner: healthcare_analytics\n        uses:\n          - \"Dense reporting grids\"\n          - \"Zero-activity gap detection\"\n\n    columns:\n      - name: month_key\n        description: \"Month surrogate key in YYYYMM integer form (e.g., 202401)\"\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: \"Calendar year of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_actual\n        description: \"Calendar month number (1-12) of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_name\n        description: \"Full month name (e.g., January)\"\n\n      - name: state\n        description: \"Patient state, distinct and non-null from staging\"\n        data_tests:\n          - not_null"},"tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # Cross-join demo (cute-dbt#64): mart_date_state_grid's CROSS JOIN\n  # CTE gives cute-dbt's renderer a real cross-join edge to draw. This\n  # test also mixes given formats (dict + csv) in a single unit_test.\n  - name: test_mart_date_state_grid_cross_joins_months_and_states\n    description: |\n      mart_date_state_grid CROSS JOINs the distinct calendar months from\n      dim_date with the distinct non-null patient states from staging.\n      Three daily dim_date rows collapse to two months; four patient\n      rows collapse to two states (one NULL filtered out, one duplicate\n      deduplicated). The result is the full 2 x 2 cartesian product —\n      four grid rows. Mixes a dict given (dim_date) with a csv given\n      (patients) in one test.\n    model: mart_date_state_grid\n    given:\n      - input: ref('dim_date')\n        format: dict\n        rows:\n          - date_actual: 2024-01-15\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-01-16\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-02-01\n            year_actual: 2024\n            month_actual: 2\n            month_name: 'February'\n      - input: ref('stg_synthea__patients')\n        format: csv\n        fixture: mart_date_state_grid_patients\n    expect:\n      format: csv\n      rows: |\n        month_key,year_actual,month_actual,month_name,state\n        202401,2024,1,January,Massachusetts\n        202401,2024,1,January,Connecticut\n        202402,2024,2,February,Massachusetts\n        202402,2024,2,February,Connecticut","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","table":{"columns":["patient_id","state"],"rows":[{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},{"display":"","key":{"t":"null"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":"patient_id,state\na00000aa-0000-0000-0000-000000000001,Massachusetts\na00000aa-0000-0000-0000-000000000002,Connecticut\na00000aa-0000-0000-0000-000000000003,\na00000aa-0000-0000-0000-000000000004,Massachusetts\n","format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv","column_meta":{"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_dq_summary\n    description: |\n      Data quality summary metrics for monitoring quarantine rates and validation failures.\n      One row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\n      Grain: entity_type\n\n      Use cases:\n      - Data quality monitoring dashboards\n      - Quarantine rate trending\n      - Validation failure root cause analysis\n      - Data quality SLA tracking\n\n    config:\n      tags: ['analytics', 'data_quality', 'marts']\n    meta:\n      grain: entity_type\n      owner: healthcare_analytics\n      uses:\n        - \"Data quality dashboards\"\n        - \"Quarantine monitoring\"\n        - \"Data quality alerting\"\n\n    columns:\n      - name: entity_type\n        description: \"Type of entity being measured (encounters, medications)\"\n        tests:\n          - not_null\n          - accepted_values:\n              values: ['encounters', 'medications']\n\n      - name: quarantined_count\n        description: \"Number of records failing data quality validations\"\n        tests:\n          - not_null\n\n      - name: total_count\n        description: \"Total number of records (valid + quarantined)\"\n        tests:\n          - not_null\n\n      - name: quarantine_rate_pct\n        description: \"Percentage of records quarantined (quarantined_count / total_count * 100)\"\n        tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 100\n\n      - name: failed_timestamp_validations\n        description: \"Count of records failing timestamp/date sequence validations\"\n\n      - name: failed_future_date_validations\n        description: \"Count of records with future dates\"\n\n      - name: failed_historical_end_validations\n        description: \"Count of records with end dates before 1900\"\n\n      - name: failed_historical_start_validations\n        description: \"Count of records with start dates before 1900\"\n\n      - name: _generated_at\n        description: \"Timestamp when summary was generated\""},"tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 1 — the slicer attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test (no blank-line gap below) to this test's authored slice\n  # (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_combines_encounter_and_medication_metrics\n    description: |\n      mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\n      and stg_synthea__medications. This test mocks two encounter rows\n      (one invalid) and two medication rows (both invalid), then asserts\n      the per-entity counts + quarantine rate. Uses csv format for both\n      GIVEN and EXPECT — compact tabular form across many boolean\n      dq-flag columns.\n    model: mart_dq_summary\n    # INSIDE bracket for test 1 — comments at deeper indent than\n    # `- name:` ride along with the body slice as raw text (cute-dbt#69\n    # / #73).\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: csv\n        rows: |\n          is_dq_valid,valid_encounter_timestamps,no_future_encounter_dates,end_after_1900,start_after_1900\n          false,false,true,true,true\n          true,true,true,true,true\n      - input: ref(\"stg_synthea__medications\")\n        format: csv\n        rows: |\n          is_dq_valid,valid_medication_dates,no_future_medication_dates,start_after_1900,end_after_1900_if_present\n          false,false,true,true,true\n          false,true,false,true,true\n    expect:\n      format: csv\n      rows: |\n        entity_type,quarantined_count,total_count,quarantine_rate_pct,failed_timestamp_validations\n        encounters,1,2,50.00,1\n        medications,2,2,100.00,1\n  # TRAILING bracket for test 1 — the comment block butts against the\n  # body above (no blank-line gap) and is terminated below by the blank\n  # line separating the two tests (cute-dbt#69 / #73).","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":true,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. Uses sql format for the GIVEN mocks and dict format\nfor the EXPECT — dict expects only compare listed columns, so\nnon-deterministic outputs (`_generated_at` from\n`current_timestamp`) are excluded from comparison.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 2 — sits between the blank-line\n  # terminator above and `- name:` below with no further gap, so the\n  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_zero_quarantined_when_all_valid\n    description: |\n      mart_dq_summary should report zero quarantined_count and 0.00\n      quarantine_rate_pct when every upstream row passes data-quality\n      validations. Uses sql format for the GIVEN mocks and dict format\n      for the EXPECT — dict expects only compare listed columns, so\n      non-deterministic outputs (`_generated_at` from\n      `current_timestamp`) are excluded from comparison.\n    model: mart_dq_summary\n    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /\n    # #73). Demonstrates that the slicer ride-along applies to every\n    # unit_test, not just the first in the file.\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_encounter_timestamps\n            , true as no_future_encounter_dates\n            , true as end_after_1900\n            , true as start_after_1900\n      - input: ref(\"stg_synthea__medications\")\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_medication_dates\n            , true as no_future_medication_dates\n            , true as start_after_1900\n            , true as end_after_1900_if_present\n    expect:\n      format: dict\n      rows:\n        - entity_type: 'encounters'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n        - entity_type: 'medications'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n    overrides:\n      macros:\n        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"\n      vars:\n        dq_quarantine_threshold: 5\n      env_vars:\n        DBT_DQ_REGION: 'us-east-1'\n  # TRAILING bracket for test 2 — terminated by the blank line\n  # separating it from test 3 (cute-dbt#69 / #73).","overrides":{"env_vars":{"DBT_DQ_REGION":"us-east-1"},"macros":{"dbt.current_timestamp":"'2024-01-15 00:00:00'"},"vars":{"dq_quarantine_threshold":5}},"given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["is_dq_valid","valid_encounter_timestamps","no_future_encounter_dates","end_after_1900","start_after_1900"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_encounter_timestamps\n  , true as no_future_encounter_dates\n  , true as end_after_1900\n  , true as start_after_1900","format":"sql"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["is_dq_valid","valid_medication_dates","no_future_medication_dates","start_after_1900","end_after_1900_if_present"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_medication_dates\n  , true as no_future_medication_dates\n  , true as start_after_1900\n  , true as end_after_1900_if_present","format":"sql"}],"expected":{"table":{"columns":["entity_type","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]}]},"rows":[{"entity_type":"encounters","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1},{"entity_type":"medications","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1}],"format":"dict","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"entity_type"},{"label":"exists but disabled","value":"test.healthcare_analytics.unique_mart_dq_summary_entity_type.4f2a9c7d10 — a uniqueness test on (entity_type) is declared but disabled (config.enabled: false); it asserts nothing and never counts as coverage"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}]},{"name":"stg_synthea__patients","path":"models/staging/synthea/stg_synthea__patients.sql","description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__patients.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"patients\"\n)"},"code_map":{"compiled":"with source as (\n    select * from \"memory\".\"main\".\"patients\"\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":53,"col":1,"byte":1155},"end":{"line":55,"col":40,"byte":1215}},"final":{"start":{"line":46,"col":1,"byte":1061},"end":{"line":51,"col":2,"byte":1153}},"renamed":{"start":{"line":5,"col":1,"byte":66},"end":{"line":44,"col":2,"byte":1058}},"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":3,"col":2,"byte":63}}},"column_spans":{"(final select)\u001f*":{"start":{"line":53,"col":8,"byte":1162},"end":{"line":53,"col":9,"byte":1163}},"final\u001f*":{"start":{"line":48,"col":9,"byte":1091},"end":{"line":48,"col":10,"byte":1092}},"final\u001f_loaded_at":{"start":{"line":49,"col":11,"byte":1103},"end":{"line":49,"col":42,"byte":1134}},"renamed\u001faddress":{"start":{"line":32,"col":11,"byte":740},"end":{"line":32,"col":29,"byte":758}},"renamed\u001fbirth_date":{"start":{"line":22,"col":11,"byte":464},"end":{"line":22,"col":34,"byte":487}},"renamed\u001fbirth_place":{"start":{"line":31,"col":11,"byte":704},"end":{"line":31,"col":36,"byte":729}},"renamed\u001fcity":{"start":{"line":33,"col":11,"byte":769},"end":{"line":33,"col":23,"byte":781}},"renamed\u001fcounty":{"start":{"line":35,"col":11,"byte":817},"end":{"line":35,"col":27,"byte":833}},"renamed\u001fdeath_date":{"start":{"line":23,"col":11,"byte":498},"end":{"line":23,"col":34,"byte":521}},"renamed\u001fdrivers_license":{"start":{"line":27,"col":11,"byte":608},"end":{"line":27,"col":37,"byte":634}},"renamed\u001fethnicity":{"start":{"line":18,"col":11,"byte":377},"end":{"line":18,"col":33,"byte":399}},"renamed\u001ffirst_name":{"start":{"line":11,"col":11,"byte":173},"end":{"line":11,"col":30,"byte":192}},"renamed\u001fgender":{"start":{"line":16,"col":11,"byte":327},"end":{"line":16,"col":27,"byte":343}},"renamed\u001fhealthcare_coverage":{"start":{"line":42,"col":11,"byte":998},"end":{"line":42,"col":53,"byte":1040}},"renamed\u001fhealthcare_expenses":{"start":{"line":41,"col":11,"byte":945},"end":{"line":41,"col":53,"byte":987}},"renamed\u001flast_name":{"start":{"line":12,"col":11,"byte":203},"end":{"line":12,"col":28,"byte":220}},"renamed\u001flatitude":{"start":{"line":37,"col":11,"byte":870},"end":{"line":37,"col":26,"byte":885}},"renamed\u001flongitude":{"start":{"line":38,"col":11,"byte":896},"end":{"line":38,"col":27,"byte":912}},"renamed\u001fmaiden_name":{"start":{"line":15,"col":11,"byte":295},"end":{"line":15,"col":32,"byte":316}},"renamed\u001fmarital_status":{"start":{"line":19,"col":11,"byte":410},"end":{"line":19,"col":36,"byte":435}},"renamed\u001fname_prefix":{"start":{"line":13,"col":11,"byte":231},"end":{"line":13,"col":32,"byte":252}},"renamed\u001fname_suffix":{"start":{"line":14,"col":11,"byte":263},"end":{"line":14,"col":32,"byte":284}},"renamed\u001fpassport_number":{"start":{"line":28,"col":11,"byte":645},"end":{"line":28,"col":38,"byte":672}},"renamed\u001fpatient_id":{"start":{"line":8,"col":9,"byte":121},"end":{"line":8,"col":25,"byte":137}},"renamed\u001frace":{"start":{"line":17,"col":11,"byte":354},"end":{"line":17,"col":23,"byte":366}},"renamed\u001fssn_hash":{"start":{"line":26,"col":11,"byte":577},"end":{"line":26,"col":31,"byte":597}},"renamed\u001fstate":{"start":{"line":34,"col":11,"byte":792},"end":{"line":34,"col":25,"byte":806}},"renamed\u001fzip_code":{"start":{"line":36,"col":11,"byte":844},"end":{"line":36,"col":26,"byte":859}},"source\u001f*":{"start":{"line":2,"col":12,"byte":28},"end":{"line":2,"col":13,"byte":29}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded into staging","documented":true},"address":{"description":"Street address","documented":true},"birth_date":{"description":"Patient date of birth","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"birth_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_stg_synthea__patients_birth_date__current_date___1900_01_01_date.372fc16e5a"},{"kind":"not_null","kwargs":{"column_name":"birth_date","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}"},"node_id":"test.healthcare_analytics.not_null_stg_synthea__patients_birth_date.4b89a0c1a7"}],"documented":true},"birth_place":{"description":"Place of birth","documented":true},"city":{"description":"City of residence","documented":true},"county":{"description":"County of residence","documented":true},"death_date":{"description":"Patient date of death (null if alive)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"death_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('stg_synthea__patients')) }}","row_condition":"death_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_stg_synthea__patients_death_date__current_date___1900_01_01_date__death_date_is_not_null.5398b32b08"}],"documented":true},"drivers_license":{"description":"Driver's license number","documented":true},"ethnicity":{"description":"Patient ethnicity (hispanic or nonhispanic)","documented":true},"first_name":{"description":"Patient's first name","tests":[{"kind":"not_null","kwargs":{"column_name":"first_name","model":"{{ get_where_subquery(ref('stg_synthea__patients')) 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 One row per patient with personal information, location, and financial data.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 900\n          config:\n            severity: warn\n    columns:\n      - name: patient_id\n        description: Unique patient identifier (UUID)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: first_name\n        description: Patient's first name\n        data_tests:\n          - not_null\n\n      - name: last_name\n        description: Patient's last name\n        data_tests:\n          - not_null\n\n      - name: name_prefix\n        description: Name prefix (Mr., Mrs., Ms., etc.)\n\n      - name: name_suffix\n        description: Name suffix (Jr., Sr., III, etc.)\n\n      - name: maiden_name\n        description: Maiden name (if applicable)\n\n      - name: gender\n        description: Patient gender (M or F)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['M', 'F']\n          # String pattern validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_match_regex:\n              regex: \"^[MF]$\"\n              config:\n                severity: warn\n\n      - name: race\n        description: Patient race\n\n      - name: ethnicity\n        description: Patient ethnicity (hispanic or nonhispanic)\n\n      - name: marital_status\n        description: Marital status (M=Married, S=Single, or blank)\n\n      - name: birth_date\n        description: Patient date of birth\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          # Note: Uses year extraction to avoid date type casting issues with DuckDB\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: death_date\n        description: Patient date of death (null if alive)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"death_date is not null\"\n              config:\n                severity: warn\n\n      - name: ssn_hash\n        description: MD5 hash of Social Security Number (masked for privacy)\n\n      - name: drivers_license\n        description: Driver's license number\n\n      - name: passport_number\n        description: Passport number\n\n      - name: birth_place\n        description: Place of birth\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City of residence\n\n      - name: state\n        description: State of residence (full state name, e.g., Massachusetts)\n\n      - name: county\n        description: County of residence\n\n      - name: zip_code\n        description: ZIP code\n        data_tests:\n          # String pattern validation (v0.8 - dbt_expectations)\n          # ZIP code format: 5 digits or 5+4 format\n          - dbt_expectations.expect_column_values_to_match_regex:\n              regex: \"^\\\\d{5}(-\\\\d{4})?$\"\n              row_condition: \"zip_code is not null\"\n              config:\n                severity: warn\n\n      - name: latitude\n        description: Latitude coordinate of residence\n\n      - name: longitude\n        description: Longitude coordinate of residence\n\n      - name: healthcare_expenses\n        description: Total healthcare expenses incurred\n        data_tests:\n          # Value range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_expenses is not null\"\n              config:\n                severity: warn\n\n      - name: healthcare_coverage\n        description: Total healthcare coverage received\n        data_tests:\n          # Value range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded into staging"},"tests":[{"id":"unit_test.healthcare_analytics.stg_synthea__patients.test_stg_synthea__patients_renames_and_hashes_source_columns","name":"test_stg_synthea__patients_renames_and_hashes_source_columns","target_model":"stg_synthea__patients","changed":false,"description":"stg_synthea__patients renames the raw Synthea UPPERCASE columns to\nsnake_case and hashes the SSN with md5. The given mocks the\nsynthea_raw.patients SOURCE directly (inline dict rows) — the only\nunit test in this project whose input is a source() rather than a\nref() (cute-dbt#57). Dict expect compares listed columns only, so\nthe non-deterministic _loaded_at (current_timestamp) is excluded\nfrom assertion automatically.\n","tags":[],"meta":{},"defined_in":"models/staging/synthea/_synthea__models.yml","authoring_yaml":"  - name: test_stg_synthea__patients_renames_and_hashes_source_columns\n    description: |\n      stg_synthea__patients renames the raw Synthea UPPERCASE columns to\n      snake_case and hashes the SSN with md5. The given mocks the\n      synthea_raw.patients SOURCE directly (inline dict rows) — the only\n      unit test in this project whose input is a source() rather than a\n      ref() (cute-dbt#57). Dict expect compares listed columns only, so\n      the non-deterministic _loaded_at (current_timestamp) is excluded\n      from assertion automatically.\n    model: stg_synthea__patients\n    given:\n      - input: source('synthea_raw', 'patients')\n        format: dict\n        rows:\n          - Id: 'c0000000-0000-0000-0000-000000000001'\n            FIRST: 'Ada'\n            LAST: 'Lovelace'\n            GENDER: 'F'\n            CITY: 'Boston'\n            STATE: 'Massachusetts'\n            SSN: '999-99-9999'\n    expect:\n      format: dict\n      rows:\n        - patient_id: 'c0000000-0000-0000-0000-000000000001'\n          first_name: 'Ada'\n          last_name: 'Lovelace'\n          gender: 'F'\n          city: 'Boston'\n          state: 'Massachusetts'\n          ssn_hash: '257cf696294931925796925d89aa0afb'","given":[{"input":"source('synthea_raw', 'patients')","bound_to_node":"source","table":{"columns":["CITY","FIRST","GENDER","Id","LAST","SSN","STATE"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"999-99-9999","key":{"t":"str","v":"999-99-9999"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"CITY":"Boston","FIRST":"Ada","GENDER":"F","Id":"c0000000-0000-0000-0000-000000000001","LAST":"Lovelace","SSN":"999-99-9999","STATE":"Massachusetts"}],"format":"dict","column_meta":{"Id":{"description":"Unique patient identifier (UUID)"}}}],"expected":{"table":{"columns":["city","first_name","gender","last_name","patient_id","ssn_hash","state"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"257cf696294931925796925d89aa0afb","key":{"t":"str","v":"257cf696294931925796925d89aa0afb"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"city":"Boston","first_name":"Ada","gender":"F","last_name":"Lovelace","patient_id":"c0000000-0000-0000-0000-000000000001","ssn_hash":"257cf696294931925796925d89aa0afb","state":"Massachusetts"}],"format":"dict","column_meta":{"city":{"description":"City of residence"},"first_name":{"description":"Patient's first name","tests":[{"name":"not null"}]},"gender":{"description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},"last_name":{"description":"Patient's last name","tests":[{"name":"not null"}]},"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"ssn_hash":{"description":"MD5 hash of Social Security Number (masked for privacy)"},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}}],"is_recursive":false}],"manifest_nodes":{"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at \u003c= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
 
 <script>
 /*! jQuery v3.7.1 | (c) OpenJS Foundation and other contributors | jquery.org/license */
diff --git a/src/adapters/render.rs b/src/adapters/render.rs
index a5e257a..d8a040f 100644
--- a/src/adapters/render.rs
+++ b/src/adapters/render.rs
@@ -73,11 +73,11 @@ use crate::domain::{
     HookChangeFacts, HookManifestPresence, InScopeSet, Instrument, MacroIdentity, Manifest,
     ModelInScopeSet, ModelState, ModelYamlOutcome, Node, NodeId, NormalizedDiffIndex, PrDagGraph,
     PrRef, ProjectChange, ProjectChangeCategory, ProjectChangePanel, ProjectDefinition,
-    ProjectFacts, ProjectFallbackReason, SeedCard, SourceMap, SourceNode, SourceSpan, SpanRole,
-    TestMetadata, Tier, UnitTest, UnitTestDataDiff, UnitTestGiven, UnitTestOverrides,
-    UnitTestYamlBlock, VarAttribution, VarChangeFacts, VarReference, VarScanFootprint, ZoneKind,
-    apply_check_policy, column_contexts, macro_blast_radius, model_findings,
-    reconstruct_macro_sql_diff, resolve_target_model, resolve_tested_model,
+    ProjectFacts, ProjectFallbackReason, SeedCard, SourceMap, SourceMapEntry, SourceNode,
+    SourcePos, SourceSpan, SpanRole, TestMetadata, Tier, UnitTest, UnitTestDataDiff, UnitTestGiven,
+    UnitTestOverrides, UnitTestYamlBlock, VarAttribution, VarChangeFacts, VarReference,
+    VarScanFootprint, ZoneKind, apply_check_policy, column_contexts, macro_blast_radius,
+    model_findings, reconstruct_macro_sql_diff, resolve_target_model, resolve_tested_model,
     table_from_manifest_rows,
 };
 
@@ -299,21 +299,34 @@ impl From<ChangeAxes> for AxesPayload {
     }
 }
 
-/// One raw-Jinja zone projection (cute-dbt#445 reserved shape; filled by the
-/// deferred `RawZoneSync` slice, S4/S5). In core S2 the source map carries no
-/// `Zone` entries, so `CodeMapPayload.raw_zones` is always empty and never
-/// serializes (`skip_serializing_if`). Kept now so the wire surface is stable
-/// when zones land.
+/// One raw-Jinja zone projection (cute-dbt#448, Z2 — CORE). Projected once from
+/// a `SpanRole::Zone` [`SourceMapEntry`] by `gather_raw_zones`: the zone's raw
+/// `start`/`end` source positions, the 3-state `presence` verdict DERIVED ONCE
+/// in Rust and emitted as a string the prototype renders verbatim (L6), and the
+/// owning `node_id` back-ref DERIVED via `contains_range` — `null` (omitted)
+/// when the zone compiled OUT (type-incapable of an edge, never-a-false-claim,
+/// honesty principle 1). In a zone-free model `CodeMapPayload.raw_zones` is
+/// empty and never serializes (`skip_serializing_if`), so pre-#448 goldens stay
+/// byte-stable (L7).
 #[derive(Debug, Clone, Serialize)]
 pub struct RawZonePayload {
-    /// Which control-flow construct this zone is.
+    /// Which control-flow construct this zone is (`incremental_guard` /
+    /// `for_loop` on the wire).
     pub kind: ZoneKind,
-    /// Region in the raw Jinja source, when located.
+    /// The zone's raw-source start position (`raw.start` — a zone always has a
+    /// raw span).
+    pub start: SourcePos,
+    /// The zone's raw-source end position (`raw.end`).
+    pub end: SourcePos,
+    /// The 3-state presence verdict, DERIVED ONCE in Rust via
+    /// [`SourceMapEntry::presence`] at this projection boundary and serialized
+    /// as the `snake_case` string the prototype renders verbatim (`compiled_in` /
+    /// `compiled_out` / `structural`). NEVER recomputed in JS (L6).
+    pub presence: &'static str,
+    /// The owning node, DERIVED via `contains_range`; `null` (omitted) when the
+    /// zone compiled OUT — type-incapable of an edge (honesty principle 1).
     #[serde(skip_serializing_if = "Option::is_none")]
-    pub raw: Option<SourceSpan>,
-    /// Region in the compiled output. `None` ⇒ pruned from this build.
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub compiled: Option<SourceSpan>,
+    pub node_id: Option<String>,
 }
 
 /// The per-model source-map render projection (cute-dbt#445) — Serialize-only,
@@ -348,18 +361,7 @@ impl CodeMapPayload {
     /// recomputed.
     fn from_source_map(sm: &SourceMap) -> Self {
         let node_spans = sm.node_spans();
-        let raw_zones = sm
-            .entries
-            .iter()
-            .filter_map(|e| match &e.role {
-                SpanRole::Zone { kind } => Some(RawZonePayload {
-                    kind: *kind,
-                    raw: e.raw,
-                    compiled: e.compiled,
-                }),
-                _ => None,
-            })
-            .collect();
+        let raw_zones = gather_raw_zones(sm, &node_spans);
         // cute-dbt#447 (CLL-2) — flatten the `SpanRole::Column` entries into a
         // string-keyed map for the JS column-anchor lookup. The unit-separator
         // join keeps the (node_id, column) pair addressable in JS without a
@@ -378,6 +380,187 @@ impl CodeMapPayload {
     }
 }
 
+/// Project the `SpanRole::Zone` entries of a [`SourceMap`] into
+/// [`RawZonePayload`]s (cute-dbt#448, Z2) — the single localized `gather_<feat>`
+/// fn. For each zone entry: read `kind` + the raw `start`/`end`; DERIVE the
+/// 3-state `presence` verdict ONCE via [`SourceMapEntry::presence`] over the
+/// model's `CteBody` compiled spans, and serialize it as a string (L6); DERIVE
+/// the owning `node_id` back-ref via `contains_range` against those same spans.
+///
+/// Never-a-false-claim (honesty principle 1): a `compiled: None` zone is
+/// `compiled_out` with `node_id: None` — type-incapable of an edge. A zone with
+/// no raw span is skipped (a zone ALWAYS has a raw origin; a raw-less Zone entry
+/// is not a thing the scanner produces, but the projection degrades to absence
+/// rather than fabricating a `(0,0)` span).
+fn gather_raw_zones(
+    sm: &SourceMap,
+    node_spans: &BTreeMap<String, SourceSpan>,
+) -> Vec<RawZonePayload> {
+    // The CteBody compiled spans of THIS model — the presence/back-ref basis.
+    let cte_spans: Vec<SourceSpan> = node_spans.values().copied().collect();
+    sm.entries
+        .iter()
+        .filter_map(|e| {
+            let SpanRole::Zone { kind } = &e.role else {
+                return None;
+            };
+            let raw = e.raw?;
+            let presence = e.presence(&cte_spans);
+            // The owning node, DERIVED via contains_range — only when the zone
+            // compiled IN. `compiled_out` ⇒ no edge (None), by construction.
+            let node_id = e.compiled.and_then(|c| owning_node_id(node_spans, &c));
+            Some(RawZonePayload {
+                kind: *kind,
+                start: raw.start,
+                end: raw.end,
+                presence: presence.to_wire(),
+                node_id,
+            })
+        })
+        .collect()
+}
+
+/// The smallest `CteBody` node whose compiled span CONTAINS `compiled` — the
+/// zone's owning node back-ref (cute-dbt#448). "Smallest" so a Structural zone
+/// nested inside several enclosing bodies binds to the TIGHTEST one (the
+/// containing CTE, not the whole terminal select). Returns `None` if no node
+/// span contains the zone's compiled span (an honest no-edge, never fabricated).
+fn owning_node_id(
+    node_spans: &BTreeMap<String, SourceSpan>,
+    compiled: &SourceSpan,
+) -> Option<String> {
+    node_spans
+        .iter()
+        .filter(|(_, span)| span.contains_range(compiled))
+        .min_by_key(|(_, span)| span.end.byte.saturating_sub(span.start.byte))
+        .map(|(node_id, _)| node_id.clone())
+}
+
+/// Scan `raw` for control zones ([`locate_raw_zones`]) and append one
+/// `SpanRole::Zone` [`SourceMapEntry`] per located zone to `sm.entries`
+/// (cute-dbt#448, Z2). Each entry's `compiled` span is resolved by HONEST
+/// token-location in `compiled`: the zone's longest literal SQL fragment, found
+/// in the compiled text ⇒ `Some(span)`; absent (e.g. an `is_incremental()`
+/// guard pruned this build) ⇒ `None` — the honest "pruned this build" verdict,
+/// NEVER fabricated. The cute-dbt#40 adapter-parses-domain-holds pattern.
+fn append_zone_entries(sm: &mut SourceMap, raw: &str, compiled: &str) {
+    for zone in locate_raw_zones(raw) {
+        let compiled_span = resolve_zone_compiled(raw, &zone.raw_span, compiled);
+        sm.entries.push(SourceMapEntry {
+            role: SpanRole::Zone { kind: zone.kind },
+            raw: Some(zone.raw_span),
+            compiled: compiled_span,
+        });
+    }
+}
+
+/// Resolve a zone's compiled span by honest token-location (cute-dbt#448, §6).
+/// Extract the zone body's literal SQL fragments (Jinja `{%…%}`/`{{…}}`/`{#…#}`
+/// stripped, longest first), then locate the BEST UNAMBIGUOUS fragment in
+/// `compiled`. A fragment is usable only when it occurs EXACTLY ONCE in
+/// `compiled`: a fragment that appears MULTIPLY (e.g. a pruned guard whose
+/// literal also lives outside the zone region) could bind the FIRST occurrence
+/// — a region OUTSIDE the actual zone — and fabricate a false
+/// `Some`/`CompiledIn`. The honest verdict there is absence: try the next-best
+/// fragment, and if NONE is unambiguous, return `None` (`CompiledOut`). Degrade,
+/// never guess (never-a-false-claim). Raw-text matching is the LAST-RESORT
+/// mechanism the shaping permits; this fix makes it ambiguity-safe so it cannot
+/// lie about presence.
+fn resolve_zone_compiled(raw: &str, raw_span: &SourceSpan, compiled: &str) -> Option<SourceSpan> {
+    let body = raw.get(raw_span.byte_range())?;
+    // Candidates longest-first; bind the first that resolves UNAMBIGUOUSLY.
+    for anchor in literal_fragments(body) {
+        // The anchor must be a meaningful token run, not a stray symbol — a
+        // single char/whitespace match would over-bind. Require ≥3
+        // non-whitespace bytes.
+        if anchor.chars().filter(|c| !c.is_whitespace()).count() < 3 {
+            continue;
+        }
+        // UNAMBIGUOUS ONLY: bind iff the anchor occurs exactly once. A multiply
+        // occurring anchor cannot tell the zone's region from a coincidental
+        // twin elsewhere in `compiled`, so binding it would risk a false claim.
+        let mut occurrences = compiled.match_indices(anchor);
+        let first = occurrences.next();
+        let unique = occurrences.next().is_none();
+        if let Some((at, _)) = first.filter(|_| unique) {
+            // Exactly one occurrence — an honest, unambiguous bind.
+            return byte_span(compiled, at, at + anchor.len());
+        }
+        // 0 occurrences (absent) or ≥2 (ambiguous): try the next-best candidate.
+    }
+    // No unambiguous literal anchor present ⇒ honest absence (CompiledOut).
+    None
+}
+
+/// Every contiguous literal (non-Jinja) text fragment in a zone body, ORDERED
+/// LONGEST-FIRST (cute-dbt#448) — the token-location anchor candidates. Strips
+/// `{%…%}` / `{{…}}` / `{#…#}` constructs (each is a boundary) and trims each
+/// remaining run. Empty when the body is all-Jinja / whitespace. The
+/// ambiguity-safe [`resolve_zone_compiled`] walks these in order, binding the
+/// first that occurs EXACTLY ONCE in the compiled text.
+fn literal_fragments(body: &str) -> Vec<&str> {
+    let bytes = body.as_bytes();
+    let n = bytes.len();
+    let mut frags: Vec<(usize, usize)> = Vec::new();
+    let mut seg_start = 0usize;
+    let mut i = 0usize;
+    let push_seg = |start: usize, end: usize, frags: &mut Vec<(usize, usize)>| {
+        let frag = body[start..end].trim();
+        if !frag.is_empty() {
+            // Map the trimmed fragment back to byte offsets within `body`.
+            let lead = body[start..end].len() - body[start..end].trim_start().len();
+            let fs = start + lead;
+            let fe = fs + frag.len();
+            frags.push((fs, fe));
+        }
+    };
+    while i < n {
+        if bytes[i] == b'{' && i + 1 < n && matches!(bytes[i + 1], b'%' | b'{' | b'#') {
+            // Close the current literal segment, then skip the Jinja construct.
+            push_seg(seg_start, i, &mut frags);
+            let close_delim = match bytes[i + 1] {
+                b'%' => b'%',
+                b'{' => b'}',
+                _ => b'#',
+            };
+            // Find the construct's closer (literal scan — anchors don't need
+            // string-literal fidelity, only a boundary; on no closer, stop).
+            let mut j = i + 2;
+            let mut closed = None;
+            while j + 1 < n {
+                if bytes[j] == close_delim && bytes[j + 1] == b'}' {
+                    closed = Some(j + 2);
+                    break;
+                }
+                j += 1;
+            }
+            let Some(after) = closed else {
+                // Unterminated construct: nothing literal beyond here.
+                seg_start = n;
+                break;
+            };
+            i = after;
+            seg_start = after;
+        } else {
+            i += 1;
+        }
+    }
+    push_seg(seg_start, n, &mut frags);
+    // Longest-first: the most specific anchor is tried before shorter ones.
+    frags.sort_by_key(|(s, e)| std::cmp::Reverse(e - s));
+    frags.into_iter().map(|(s, e)| &body[s..e]).collect()
+}
+
+/// The longest contiguous literal (non-Jinja) text fragment in a zone body —
+/// the single best token-location anchor (cute-dbt#448). Thin wrapper over the
+/// longest-first [`literal_fragments`]. `None` when the body is all-Jinja /
+/// whitespace. Test-only since the ambiguity-safe [`resolve_zone_compiled`]
+/// walks the full [`literal_fragments`] candidate list, not just the head.
+#[cfg(test)]
+fn longest_literal_fragment(body: &str) -> Option<&str> {
+    literal_fragments(body).into_iter().next()
+}
+
 /// The per-model column-lineage render projection (cute-dbt#446, CLL-1) —
 /// Serialize-only. In CLL-1 it carries ONLY the Tier-2 `context` half (the
 /// pure manifest fold: per-column definition + tested-by); the Tier-1 `edges`
@@ -3195,6 +3378,429 @@ fn skip_ws_bytes(bytes: &[u8], mut i: usize) -> usize {
     i
 }
 
+/// One raw-Jinja control zone located in `raw_code` — the ADAPTER-internal
+/// intermediate emitted by [`locate_raw_zones`], NOT a domain type. Mirrors
+/// dbt-fusion's `JinjaLayoutEvent` shape (kind + source span + a depth-stack
+/// `block_id` pairing start↔end) but owns ZERO dependency (cute-dbt#448, L1).
+/// `raw_span` covers the FULL construct: the opener `{%`/`{%-` through the
+/// closer `%}`/`-%}` of the matching `endif`/`endfor`.
+#[derive(Debug, Clone, PartialEq, Eq)]
+struct ZoneFact {
+    kind: ZoneKind,
+    raw_span: SourceSpan,
+    /// The depth-stack pairing id (start↔end). Distinguishes nested
+    /// constructs the way fusion's `block_id` linker does; carried for fact
+    /// completeness even though resolution keys on `raw_span`.
+    block_id: u32,
+}
+
+/// A `{%…%}` block-tag boundary parsed out of `raw_code` by [`scan_block_tags`]:
+/// the half-open byte range `[open, close)` of the WHOLE tag (delimiters
+/// included, whitespace-control dashes included) and its classified leading
+/// keyword. Variable tags `{{…}}` and comments `{#…#}` are skipped at the scan
+/// layer and never surface as a `BlockTag`.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+struct BlockTag {
+    open: usize,
+    close: usize,
+    role: TagRole,
+}
+
+/// The block-pairing kind tracked on the depth-stack. SUPERSET of the public
+/// [`ZoneKind`] (which is `#[non_exhaustive]` and carries only the EMITTED
+/// kinds): adds `PlainIf` so a non-incremental `{% if %}…{% endif %}` still
+/// PAIRS correctly (preserving nesting) without emitting a v0.1 zone (L9).
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+enum BlockKind {
+    /// `{% if is_incremental() %}` — emits an [`ZoneKind::IncrementalGuard`].
+    IncrementalGuard,
+    /// `{% for … %}` — emits an [`ZoneKind::ForLoop`].
+    ForLoop,
+    /// Any other `{% if … %}` — paired (so nesting is honored) but NOT a zone.
+    PlainIf,
+}
+
+impl BlockKind {
+    /// The emitted [`ZoneKind`], or `None` for a non-emitting opener
+    /// (`PlainIf`). Drives whether a matched pair produces a [`ZoneFact`].
+    fn zone_kind(self) -> Option<ZoneKind> {
+        match self {
+            Self::IncrementalGuard => Some(ZoneKind::IncrementalGuard),
+            Self::ForLoop => Some(ZoneKind::ForLoop),
+            Self::PlainIf => None,
+        }
+    }
+
+    /// Whether `closer` (an `endif`/`endfor` family) closes THIS opener:
+    /// `if`/`plain-if`/`incremental-guard` all close with `endif`; `for` closes
+    /// with `endfor`.
+    fn closes_with(self, closer: EndKind) -> bool {
+        matches!(
+            (self, closer),
+            (Self::IncrementalGuard | Self::PlainIf, EndKind::If) | (Self::ForLoop, EndKind::For)
+        )
+    }
+}
+
+/// The family an `end…` tag closes (`endif` vs `endfor`).
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+enum EndKind {
+    /// `{% endif %}`.
+    If,
+    /// `{% endfor %}`.
+    For,
+}
+
+/// The control-flow role of a `{%…%}` tag's leading keyword. `Other` is any tag
+/// we do not pair (`set`, `macro`, `call`, …) — scanned-and-skipped, never a
+/// zone boundary in v0.1 (L9).
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+enum TagRole {
+    /// `{% if … %}` / `{% for … %}` — opens a block.
+    Start { kind: BlockKind },
+    /// `{% else %}` / `{% elif … %}` — a mid-block divider (consumed by the
+    /// depth-stack, never its own zone).
+    Mid,
+    /// `{% endif %}` / `{% endfor %}` — closes a block.
+    End { kind: EndKind },
+    /// Any other `{%…%}` tag — skipped.
+    Other,
+}
+
+/// Hand-rolled tag-boundary scanner over `raw_code` (cute-dbt#448, Z1). Emits a
+/// [`ZoneFact`] per matched `{% if … %}…{% endif %}` / `{% for … %}…{% endfor %}`
+/// construct, pairing start↔end with a DEPTH-STACK (NOT first-`endif`-wins) so
+/// nested zones bind correctly. Mirrors fusion's `JinjaLayoutEvent` model with
+/// no dependency (L1–L4): lives in the ADAPTER beside [`macro_call_sites`], so
+/// even a future parser dep cannot leak into `src/domain/`.
+///
+/// Honesty backstop (FAIL-CLOSED): a malformed/unterminated/unbalanced tag
+/// stream degrades to an EMPTY vec for the offending construct — NEVER a panic,
+/// hang, or fabricated zone (§2.4). The edge surface owned by hand-roll:
+/// whitespace control (`{%-`/`-%}`), string-literal-aware `%}` skipping
+/// (`{% … "%}" … %}` does not close early), and `{#…#}` comments swallowing
+/// inner tags (`{# {% if %} #}` is NOT a zone).
+fn locate_raw_zones(raw_code: &str) -> Vec<ZoneFact> {
+    if raw_code.is_empty() {
+        return Vec::new();
+    }
+    // A malformed/unterminated tag aborts the whole scan (fail-closed).
+    let Some(tags) = scan_block_tags(raw_code) else {
+        return Vec::new();
+    };
+    let mut stack: Vec<(BlockKind, usize, u32)> = Vec::new();
+    let mut zones: Vec<ZoneFact> = Vec::new();
+    let mut next_block_id: u32 = 0;
+    for tag in &tags {
+        match tag.role {
+            TagRole::Start { kind } => {
+                let block_id = next_block_id;
+                next_block_id = next_block_id.wrapping_add(1);
+                stack.push((kind, tag.open, block_id));
+            }
+            TagRole::End { kind: end_kind } => {
+                match stack.pop() {
+                    // The closer must close the opener's family (if↔endif,
+                    // for↔endfor). A mismatch is unbalanced → fail-closed.
+                    Some((open_kind, open_at, block_id)) if open_kind.closes_with(end_kind) => {
+                        // A v0.1 zone is emitted ONLY for an incremental guard
+                        // or a for-loop; a plain `{% if %}` pairs but emits no
+                        // zone (L9). DELIBERATELY nested `if let` (NOT a
+                        // `let`-chain): the prefix-scanning `byte_span` is
+                        // skipped entirely for a non-emitting opener
+                        // (`zone_kind()` is `None`) — a real perf win for plain
+                        // `{% if %}` blocks — and it sidesteps the unstable
+                        // `let_chains` feature question on MSRV 1.88. Collapsing
+                        // to a `let`-chain would undo both, so the lint is
+                        // suppressed at this site only.
+                        #[allow(clippy::collapsible_if)]
+                        if let Some(zone_kind) = open_kind.zone_kind() {
+                            if let Some(raw_span) = byte_span(raw_code, open_at, tag.close) {
+                                zones.push(ZoneFact {
+                                    kind: zone_kind,
+                                    raw_span,
+                                    block_id,
+                                });
+                            }
+                        }
+                    }
+                    _ => return Vec::new(),
+                }
+            }
+            // A mid-block divider (`else`/`elif`) is valid ONLY inside an open
+            // block — it must have a matching opener on the depth-stack. An
+            // ORPHAN mid-tag (empty stack) is malformed Jinja → fail-closed
+            // (empty vec, never letting a later zone emit from a broken stream).
+            TagRole::Mid => {
+                if stack.is_empty() {
+                    return Vec::new();
+                }
+            }
+            // Other tags are consumed without affecting pairing.
+            TagRole::Other => {}
+        }
+    }
+    // Unclosed openers ⇒ unbalanced ⇒ fail-closed.
+    if !stack.is_empty() {
+        return Vec::new();
+    }
+    zones
+}
+
+/// Scan `raw_code` for every `{%…%}` block tag, skipping `{{…}}` variable tags
+/// and `{#…#}` comments wholesale (so an inner `{% … %}` inside a comment never
+/// surfaces). Returns `None` on a malformed/unterminated tag (an opener with no
+/// matching closer before EOF) — the fail-closed signal. Whitespace control
+/// (`{%-`/`-%}`) and string-literal-aware `%}` skipping are handled here.
+fn scan_block_tags(raw_code: &str) -> Option<Vec<BlockTag>> {
+    let bytes = raw_code.as_bytes();
+    let n = bytes.len();
+    let mut tags: Vec<BlockTag> = Vec::new();
+    let mut i = 0;
+    while i < n {
+        if bytes[i] == b'{' && i + 1 < n {
+            match bytes[i + 1] {
+                b'#' => {
+                    // Comment: skip to the matching `#}` (no nesting in Jinja).
+                    let close = find_close(bytes, i + 2, b'#')?;
+                    i = close;
+                }
+                b'{' => {
+                    // Variable tag: skip to `}}`, string-literal-aware.
+                    let close = find_expr_close(bytes, i + 2, b'}')?;
+                    i = close;
+                }
+                b'%' => {
+                    // Block tag: find the closing `%}`, string-literal-aware.
+                    let close = find_expr_close(bytes, i + 2, b'%')?;
+                    let role = classify_block_tag(&raw_code[i..close]);
+                    tags.push(BlockTag {
+                        open: i,
+                        close,
+                        role,
+                    });
+                    i = close;
+                }
+                _ => i += 1,
+            }
+        } else {
+            i += 1;
+        }
+    }
+    Some(tags)
+}
+
+/// Find the byte index PAST a `<delim>}` closer (e.g. `#}`) starting at `from`,
+/// scanning literally (comments do not respect string literals). Returns the
+/// index after the closing `}`, or `None` if unterminated.
+fn find_close(bytes: &[u8], from: usize, delim: u8) -> Option<usize> {
+    let n = bytes.len();
+    let mut i = from;
+    while i + 1 < n {
+        if bytes[i] == delim && bytes[i + 1] == b'}' {
+            return Some(i + 2);
+        }
+        i += 1;
+    }
+    None
+}
+
+/// Find the byte index PAST a `<delim>}` closer for an EXPRESSION tag (`%}` or
+/// `}}`), honoring single/double-quoted string literals so a `%}`/`}}` INSIDE a
+/// string does not close the tag early (§2.4). Returns the index after the
+/// closing `}`, or `None` if unterminated. (The whitespace-control `-%}` is
+/// covered: the `%` then `}` still close; the leading `-` is just preceding
+/// content.)
+fn find_expr_close(bytes: &[u8], from: usize, delim: u8) -> Option<usize> {
+    let n = bytes.len();
+    let mut i = from;
+    let mut quote: Option<u8> = None;
+    while i < n {
+        let b = bytes[i];
+        match quote {
+            Some(q) => {
+                if b == q {
+                    quote = None;
+                }
+                // Jinja string escapes (`\'`): skip the escaped byte.
+                else if b == b'\\' && i + 1 < n {
+                    i += 1;
+                }
+            }
+            None => {
+                if b == b'\'' || b == b'"' {
+                    quote = Some(b);
+                } else if b == delim && i + 1 < n && bytes[i + 1] == b'}' {
+                    return Some(i + 2);
+                }
+            }
+        }
+        i += 1;
+    }
+    None
+}
+
+/// Whether an `{% if … %}` condition contains an ACTUAL `is_incremental()` CALL
+/// (cute-dbt#448) — an identifier-boundary, quote-aware token, NOT a bare
+/// substring. The match requires:
+/// - the literal token `is_incremental` with IDENTIFIER BOUNDARIES on BOTH sides
+///   (the preceding/following char is not `[A-Za-z0-9_]`), so a larger
+///   identifier like `some_is_incremental_flag` does NOT match;
+/// - NOT inside a single/double-quoted string literal (a quoted
+///   `'is_incremental'` is data, not a call);
+/// - followed by optional whitespace then `(` — an actual call invocation.
+///
+/// A plain `{% if %}` whose condition merely embeds the substring must classify
+/// as `PlainIf` (no v0.1 zone) — never a false `IncrementalGuard`.
+fn mentions_is_incremental_call(cond: &str) -> bool {
+    const TOKEN: &str = "is_incremental";
+    let bytes = cond.as_bytes();
+    // Iterate every literal occurrence of the token; return on the first that
+    // is a real, unquoted, parenthesized call. Each predicate is a small
+    // single-purpose helper so the boundary/quote/call logic stays low-CC.
+    cond.match_indices(TOKEN).any(|(at, _)| {
+        token_left_boundary_ok(bytes, at)
+            && token_right_boundary_ok(bytes, at + TOKEN.len())
+            && call_paren_follows(bytes, at + TOKEN.len())
+            && !offset_in_string_literal(bytes, at)
+    })
+}
+
+/// Whether `b` is an identifier (word) char `[A-Za-z0-9_]`.
+fn is_word_byte(b: u8) -> bool {
+    b.is_ascii_alphanumeric() || b == b'_'
+}
+
+/// LEFT identifier boundary at token start `at`: the preceding byte (if any)
+/// must NOT be a word char, so `some_is_incremental_flag` does not match.
+fn token_left_boundary_ok(bytes: &[u8], at: usize) -> bool {
+    at == 0 || !is_word_byte(bytes[at - 1])
+}
+
+/// RIGHT identifier boundary just past the token (`after`): the next byte (if
+/// any) must NOT be a word char, so `is_incremental_flag` does not match.
+fn token_right_boundary_ok(bytes: &[u8], after: usize) -> bool {
+    after >= bytes.len() || !is_word_byte(bytes[after])
+}
+
+/// Whether an actual call `(` follows the token (skipping optional whitespace
+/// past `after`) — distinguishing a call `is_incremental()` from a bare ident.
+fn call_paren_follows(bytes: &[u8], after: usize) -> bool {
+    let mut j = after;
+    while j < bytes.len() && bytes[j].is_ascii_whitespace() {
+        j += 1;
+    }
+    bytes.get(j) == Some(&b'(')
+}
+
+/// Whether byte offset `at` lies INSIDE a single/double-quoted string literal,
+/// scanning from the start and honoring `\`-escapes — so a quoted
+/// `'is_incremental'` is treated as data, not a call.
+fn offset_in_string_literal(bytes: &[u8], at: usize) -> bool {
+    let mut quote: Option<u8> = None;
+    let mut i = 0usize;
+    while i < at {
+        i = step_string_scan(bytes, i, &mut quote);
+    }
+    quote.is_some()
+}
+
+/// Advance the quote-aware string scan one logical step from `i`, updating the
+/// open-quote state, and return the next index. A `\`-escape inside a quote
+/// consumes the escaped byte too.
+fn step_string_scan(bytes: &[u8], i: usize, quote: &mut Option<u8>) -> usize {
+    let b = bytes[i];
+    match *quote {
+        Some(q) => {
+            if b == b'\\' && i + 1 < bytes.len() {
+                return i + 2;
+            }
+            if b == q {
+                *quote = None;
+            }
+        }
+        None => {
+            if b == b'\'' || b == b'"' {
+                *quote = Some(b);
+            }
+        }
+    }
+    i + 1
+}
+
+/// Classify a full `{%…%}` tag slice by its leading keyword (after the `{%`
+/// delimiter + optional `-` whitespace-control dash + whitespace). An `if`
+/// whose condition makes an actual `is_incremental()` call is an
+/// [`ZoneKind::IncrementalGuard`]; any other `if`/`for` is a generic
+/// [`TagRole::Start`].
+fn classify_block_tag(tag: &str) -> TagRole {
+    // Strip `{%`, an optional `-`, and leading whitespace; strip the trailing
+    // `%}`/`-%}` and trailing whitespace, to read the inner statement.
+    let inner = tag
+        .strip_prefix("{%")
+        .unwrap_or(tag)
+        .trim_start_matches('-')
+        .trim_start();
+    let inner = inner
+        .strip_suffix("%}")
+        .unwrap_or(inner)
+        .trim_end()
+        .trim_end_matches('-')
+        .trim_end();
+    // The leading keyword is the first whitespace-delimited token.
+    let keyword = inner.split_whitespace().next().unwrap_or("");
+    match keyword {
+        "if" => {
+            // An incremental guard emits a zone; any other `{% if %}` still
+            // PAIRS (so nesting is honored) but emits no v0.1 zone (L9). Match an
+            // ACTUAL `is_incremental()` CALL — an identifier-boundary, quote-aware
+            // token — NOT a bare substring (`some_is_incremental_flag` and a
+            // quoted `'is_incremental'` must NOT classify as a guard).
+            let kind = if mentions_is_incremental_call(inner) {
+                BlockKind::IncrementalGuard
+            } else {
+                BlockKind::PlainIf
+            };
+            TagRole::Start { kind }
+        }
+        "for" => TagRole::Start {
+            kind: BlockKind::ForLoop,
+        },
+        "endif" => TagRole::End { kind: EndKind::If },
+        "endfor" => TagRole::End { kind: EndKind::For },
+        "else" | "elif" => TagRole::Mid,
+        _ => TagRole::Other,
+    }
+}
+
+/// Build a [`SourceSpan`] over the half-open byte range `[start, end)` of
+/// `text`, computing honest 1-based line/col endpoints by counting newlines.
+/// Returns `None` if the range is out of bounds or not on char boundaries
+/// (fail-closed — the scanner never fabricates a span).
+fn byte_span(text: &str, start: usize, end: usize) -> Option<SourceSpan> {
+    if start > end || end > text.len() {
+        return None;
+    }
+    if !text.is_char_boundary(start) || !text.is_char_boundary(end) {
+        return None;
+    }
+    Some(SourceSpan {
+        start: line_col_pos(text, start),
+        end: line_col_pos(text, end),
+    })
+}
+
+/// 1-based line / unicode-char col + 0-based byte for `offset` in `text`.
+fn line_col_pos(text: &str, offset: usize) -> SourcePos {
+    let prefix = &text[..offset];
+    let line =
+        u32::try_from(prefix.bytes().filter(|&b| b == b'\n').count() + 1).unwrap_or(u32::MAX);
+    let last_line = prefix.rsplit('\n').next().unwrap_or("");
+    let col = u32::try_from(last_line.chars().count() + 1).unwrap_or(u32::MAX);
+    let byte = u32::try_from(offset).unwrap_or(u32::MAX);
+    SourcePos { line, col, byte }
+}
+
 /// The macro's current body as plain context [`DiffLine`]s — the fallback
 /// the section renders when no inline diff applies. One terminator stripped
 /// (the engine-divergent normalization), then one Context line per `\n`-split
@@ -3961,7 +4567,24 @@ fn build_model_payload(
     // domain assembler emits one CteBody entry per node (and synthesizes the
     // lone terminal entry for a WITH-less model), so the derived keys agree
     // with the DAG by construction.
-    let source_map = SourceMap::from_cte_graph(&graph, compiled_code, TERMINAL_NODE_NAME);
+    let mut source_map = SourceMap::from_cte_graph(&graph, compiled_code, TERMINAL_NODE_NAME);
+    // cute-dbt#448 (Z1+Z2) — raw-Jinja zones: scan the model's raw_code for
+    // {% if is_incremental() %} / {% for %} control zones, resolve each into a
+    // SpanRole::Zone SourceMapEntry (compiled-span by honest token-location,
+    // None ⇒ pruned this build), and append to the spine. The presence verdict
+    // + node back-ref are derived downstream in gather_raw_zones. Only runs when
+    // there is both a source map (compiled present) AND raw_code to scan.
+    // DELIBERATELY nested `if let` (NOT a `let`-chain) — the model's `raw_code`
+    // is only read when a source map exists, and this sidesteps the unstable
+    // `let_chains` feature question on MSRV 1.88 while keeping behavior
+    // identical. Collapsing to a `let`-chain would reintroduce that question, so
+    // the lint is suppressed at this site only.
+    #[allow(clippy::collapsible_if)]
+    if let Some(sm) = source_map.as_mut() {
+        if let Some(raw) = model.raw_code().filter(|s| !s.is_empty()) {
+            append_zone_entries(sm, raw, compiled_code);
+        }
+    }
     let compiled_sql = build_compiled_sql(source_map.as_ref());
     let code_map = source_map.as_ref().map(CodeMapPayload::from_source_map);
     // cute-dbt#446 (CLL-1) — the Tier-2 column-lineage context: a pure
@@ -4555,8 +5178,6 @@ fn leaf_segment(id: &str) -> &str {
 #[cfg(test)]
 mod tests {
     use super::*;
-    use crate::domain::SourcePos;
-    use crate::domain::source_map::SourceMapEntry;
     use crate::domain::{
         BlastRadius, Checksum, ColumnMetaTags, ContractClass, ContractColumnDiff, CteEdge, CteNode,
         DEFAULT_MACRO_BODY_CAP, DEFAULT_REPORT_TITLE, DependsOn, DiffLine, DiffLineKind, EdgeType,
@@ -7145,11 +7766,10 @@ mod tests {
         }
     }
 
-    /// cute-dbt#445: `CodeMapPayload::from_source_map` projects `Zone` entries
-    /// into `raw_zones`. Core S2 never PRODUCES a `Zone` entry (the assembler
-    /// emits `CteBody` only), so this pins the deferred projection arm directly
-    /// from a hand-built `SourceMap` — keeping the wire surface stable + the
-    /// match arm killed before the S4/S5 raw-zone path lands.
+    /// cute-dbt#448: `gather_raw_zones` projects `Zone` entries into
+    /// `raw_zones` with the derived 3-state `presence` string + node back-ref.
+    /// A `compiled: None` zone (the incremental-guard compiled-OUT case) is
+    /// `compiled_out` with `node_id: None` — type-incapable of an edge.
     #[test]
     fn from_source_map_projects_zone_entries_into_raw_zones() {
         let sm = SourceMap {
@@ -7197,16 +7817,459 @@ mod tests {
         // The CteBody entry projects into node_spans (not raw_zones).
         assert_eq!(payload.node_spans.len(), 1);
         assert!(payload.node_spans.contains_key(TERMINAL_NODE_NAME));
-        // The Zone entry projects into raw_zones with its kind + spans.
+        // The Zone entry projects into raw_zones with kind + raw start/end +
+        // the derived presence string + node back-ref.
         assert_eq!(payload.raw_zones.len(), 1, "the Zone arm is projected");
         assert_eq!(payload.raw_zones[0].kind, ZoneKind::IncrementalGuard);
-        assert!(payload.raw_zones[0].raw.is_some());
+        assert_eq!(payload.raw_zones[0].start.byte, 0);
+        assert_eq!(payload.raw_zones[0].end.byte, 4);
+        assert_eq!(
+            payload.raw_zones[0].presence, "compiled_out",
+            "compiled: None ⇒ the honest pruned verdict"
+        );
+        assert!(
+            payload.raw_zones[0].node_id.is_none(),
+            "compiled_out ⇒ no node edge (never-a-false-claim)"
+        );
+    }
+
+    // ── cute-dbt#448 (Z1): the hand-rolled tag-boundary scanner ──
+
+    #[test]
+    fn scan_locates_an_incremental_guard() {
+        let raw = "select *\nfrom events\n{% if is_incremental() %}\nwhere ts > 0\n{% endif %}";
+        let zones = locate_raw_zones(raw);
+        assert_eq!(zones.len(), 1, "one incremental-guard zone");
+        assert_eq!(zones[0].kind, ZoneKind::IncrementalGuard);
+        // The raw span covers the whole {% if … %}…{% endif %} construct.
+        let covered = &raw[zones[0].raw_span.byte_range()];
+        assert!(covered.starts_with("{% if is_incremental()"));
+        assert!(covered.ends_with("{% endif %}"));
+    }
+
+    #[test]
+    fn scan_locates_a_for_loop() {
+        let raw = "case status\n{% for s in statuses %}when {{ s }} then 1\n{% endfor %}\nend";
+        let zones = locate_raw_zones(raw);
+        assert_eq!(zones.len(), 1, "one for-loop zone");
+        assert_eq!(zones[0].kind, ZoneKind::ForLoop);
+        let covered = &raw[zones[0].raw_span.byte_range()];
+        assert!(covered.starts_with("{% for s in statuses %}"));
+        assert!(covered.ends_with("{% endfor %}"));
+    }
+
+    #[test]
+    fn scan_pairs_nested_if_inside_for_by_depth_stack() {
+        // {% for %} containing {% if is_incremental() %}: the depth-stack pairs
+        // the inner endif to the inner if, NOT first-endif-wins.
+        let raw = "{% for x in xs %}{% if is_incremental() %}a{% endif %}b{% endfor %}";
+        let zones = locate_raw_zones(raw);
+        // Two zones: the inner incremental guard AND the outer for-loop.
+        assert_eq!(zones.len(), 2, "inner guard + outer for-loop");
+        let kinds: Vec<ZoneKind> = zones.iter().map(|z| z.kind).collect();
+        assert!(kinds.contains(&ZoneKind::IncrementalGuard));
+        assert!(kinds.contains(&ZoneKind::ForLoop));
+        // The inner guard closes FIRST (popped before the for-loop).
+        assert_eq!(
+            zones[0].kind,
+            ZoneKind::IncrementalGuard,
+            "inner endif pairs to the inner if (depth-stack, not first-endif-wins)"
+        );
+        // Inner guard's raw span is strictly inside the for-loop's raw span.
+        let outer = zones
+            .iter()
+            .find(|z| z.kind == ZoneKind::ForLoop)
+            .unwrap()
+            .raw_span;
+        assert!(outer.contains_range(&zones[0].raw_span));
+    }
+
+    #[test]
+    fn scan_handles_whitespace_control_trim_markers() {
+        // {%- … -%} whitespace control: the raw span stays anchored on the raw
+        // delimiters (the {%- is included).
+        let raw = "x\n{%- if is_incremental() -%}\ny\n{%- endif -%}\nz";
+        let zones = locate_raw_zones(raw);
+        assert_eq!(zones.len(), 1);
+        assert_eq!(zones[0].kind, ZoneKind::IncrementalGuard);
+        let covered = &raw[zones[0].raw_span.byte_range()];
+        assert!(covered.starts_with("{%- if is_incremental()"));
+        assert!(covered.ends_with("-%}"));
+    }
+
+    #[test]
+    fn scan_comment_swallows_an_inner_tag() {
+        // {# {% if %} #}: the comment swallows the inner tag — NO zone, and no
+        // dangling opener to unbalance the scan.
+        let raw = "select 1 {# {% if is_incremental() %} #} from t";
+        assert!(
+            locate_raw_zones(raw).is_empty(),
+            "a tag inside a comment is not a zone"
+        );
+    }
+
+    #[test]
+    fn scan_string_literal_does_not_close_the_tag_early() {
+        // A `%}` inside a string literal must NOT close the {% … %} tag.
+        let raw = "{% if is_incremental() and x == '%}' %}body{% endif %}";
+        let zones = locate_raw_zones(raw);
+        assert_eq!(
+            zones.len(),
+            1,
+            "a percent-brace inside a string literal does not close the tag early"
+        );
+        assert_eq!(zones[0].kind, ZoneKind::IncrementalGuard);
+        let covered = &raw[zones[0].raw_span.byte_range()];
+        assert!(covered.ends_with("{% endif %}"));
+    }
+
+    #[test]
+    fn scan_unbalanced_yields_empty_fail_closed() {
+        // An opener with no closer → empty vec (fail-closed), never a panic.
+        assert!(locate_raw_zones("{% if is_incremental() %}body").is_empty());
+        // A closer with no opener → empty.
+        assert!(locate_raw_zones("body{% endif %}").is_empty());
+        // A mismatched pair (if…endfor) → empty.
+        assert!(locate_raw_zones("{% if is_incremental() %}x{% endfor %}").is_empty());
+        // An unterminated tag → empty (never hangs).
+        assert!(locate_raw_zones("{% if is_incremental() ").is_empty());
+        // Empty input → empty.
+        assert!(locate_raw_zones("").is_empty());
+    }
+
+    #[test]
+    fn scan_plain_if_pairs_but_emits_no_zone() {
+        // A non-incremental {% if %} pairs correctly (so it does not unbalance a
+        // sibling zone) but emits NO v0.1 zone (L9).
+        let raw = "{% if foo %}a{% endif %}\n{% for x in xs %}b{% endfor %}";
+        let zones = locate_raw_zones(raw);
+        assert_eq!(zones.len(), 1, "only the for-loop is a zone");
+        assert_eq!(zones[0].kind, ZoneKind::ForLoop);
+    }
+
+    // ── cute-dbt#448 (Z2): zone facts → SpanRole::Zone entries + presence ──
+
+    #[test]
+    fn append_zone_entries_incremental_guard_pruned_is_compiled_out() {
+        // The {% if is_incremental() %} guard's body is ABSENT from a fresh
+        // compile (is_incremental()==false) → compiled: None, presence
+        // compiled_out, node_id: null.
+        let raw = "select *\nfrom events\n{% if is_incremental() %}\nwhere ts > (select max(ts) from this)\n{% endif %}";
+        let compiled = "select *\nfrom events"; // the guard body is pruned
+        let mut sm = SourceMap {
+            compiled: compiled.to_owned(),
+            entries: vec![SourceMapEntry {
+                role: SpanRole::CteBody {
+                    node_id: TERMINAL_NODE_NAME.to_owned(),
+                },
+                raw: None,
+                compiled: byte_span(compiled, 0, compiled.len()),
+            }],
+        };
+        append_zone_entries(&mut sm, raw, compiled);
+        let payload = CodeMapPayload::from_source_map(&sm);
+        assert_eq!(payload.raw_zones.len(), 1);
+        let z = &payload.raw_zones[0];
+        assert_eq!(z.kind, ZoneKind::IncrementalGuard);
+        assert_eq!(z.presence, "compiled_out", "guard pruned this build");
+        assert!(z.node_id.is_none(), "compiled_out ⇒ no node edge");
+    }
+
+    #[test]
+    fn append_zone_entries_shape_a_for_loop_is_structural() {
+        // A {% for %} expanding columns INSIDE one CTE body: the loop's literal
+        // tokens land inside the terminal CteBody span → Structural, bound to
+        // the containing node. The loop body carries a UNIQUE literal anchor
+        // ("when unique_status_marker then") that appears exactly once in the
+        // unrolled compiled output, so the ambiguity-safe resolver (FIX B) can
+        // soundly bind it (a loop whose body literal REPEATS verbatim per
+        // iteration is genuinely unbindable — see
+        // `resolve_zone_compiled_ambiguous_anchor_is_none_not_a_false_bind`).
+        let raw = "select\n  case status\n  {% for s in statuses %}when unique_status_marker then {{ s }}\n  {% endfor %}\n  end as label\nfrom events";
+        // The compiled text contains the loop's UNIQUE literal fragment
+        // ("when unique_status_marker then") inside the (whole-text) terminal
+        // body, exactly once.
+        let compiled = "select\n  case status\n  when unique_status_marker then 1\n  end as label\nfrom events";
+        let mut sm = SourceMap {
+            compiled: compiled.to_owned(),
+            entries: vec![SourceMapEntry {
+                role: SpanRole::CteBody {
+                    node_id: TERMINAL_NODE_NAME.to_owned(),
+                },
+                raw: None,
+                compiled: byte_span(compiled, 0, compiled.len()),
+            }],
+        };
+        append_zone_entries(&mut sm, raw, compiled);
+        let payload = CodeMapPayload::from_source_map(&sm);
+        assert_eq!(payload.raw_zones.len(), 1, "one for-loop zone");
+        let z = &payload.raw_zones[0];
+        assert_eq!(z.kind, ZoneKind::ForLoop);
+        assert_eq!(
+            z.presence, "structural",
+            "the loop expands inside one CTE body (strictly nested)"
+        );
+        assert_eq!(
+            z.node_id.as_deref(),
+            Some(TERMINAL_NODE_NAME),
+            "structural zone binds to its containing node"
+        );
+    }
+
+    #[test]
+    fn longest_literal_fragment_skips_jinja_constructs() {
+        // The anchor is the longest literal run with Jinja stripped.
+        let body = "{% if x %}select distinct id from {{ ref('t') }} where active{% endif %}";
+        let frag = longest_literal_fragment(body).expect("a literal fragment");
+        assert_eq!(frag, "select distinct id from");
+    }
+
+    #[test]
+    fn resolve_zone_compiled_returns_none_when_anchor_absent() {
+        // A zone whose literal anchor is not in the compiled text → None
+        // (degrade to absence, never a fabricated Some).
+        let raw = "{% if is_incremental() %}where unique_marker_xyz > 0{% endif %}";
+        let zone = &locate_raw_zones(raw)[0];
+        let compiled = "select * from events"; // anchor absent
+        assert!(resolve_zone_compiled(raw, &zone.raw_span, compiled).is_none());
+    }
+
+    // ── cute-dbt#448 FIX B (CodeRabbit Major, never-a-false-claim): the zone
+    // anchor must resolve UNAMBIGUOUSLY. A pruned guard whose literal also
+    // appears OUTSIDE the actual zone region must NOT fabricate a Some — it
+    // degrades to None (honest CompiledOut), never a false bind. ──
+
+    #[test]
+    fn resolve_zone_compiled_ambiguous_anchor_is_none_not_a_false_bind() {
+        // The guard's longest literal anchor ("where status = 'active'") ALSO
+        // appears verbatim elsewhere in `compiled` (the guard itself was pruned
+        // this build). Binding the FIRST occurrence would claim CompiledIn at a
+        // region OUTSIDE the zone — a false claim. The ambiguity-safe resolver
+        // refuses to bind and returns None (honest CompiledOut).
+        let raw = "{% if is_incremental() %}where status = 'active'{% endif %}";
+        let zone = &locate_raw_zones(raw)[0];
+        // The anchor "where status = 'active'" occurs TWICE in compiled, and
+        // neither is the (pruned) zone region — ambiguous, must not bind.
+        let compiled = "select * from a where status = 'active'\n\
+                        union all\n\
+                        select * from b where status = 'active'";
+        assert!(
+            resolve_zone_compiled(raw, &zone.raw_span, compiled).is_none(),
+            "a multiply-occurring anchor must NOT fabricate a Some (never-a-false-claim)"
+        );
+    }
+
+    #[test]
+    fn append_zone_entries_ambiguous_anchor_resolves_to_compiled_out() {
+        // End-to-end through the projection: the same ambiguous-anchor guard →
+        // presence compiled_out, node_id null (no fabricated edge), NOT a false
+        // structural/compiled_in.
+        let raw =
+            "select *\nfrom events\n{% if is_incremental() %}where status = 'active'{% endif %}";
+        let compiled = "select * from a where status = 'active'\n\
+                        union all\n\
+                        select * from b where status = 'active'";
+        let mut sm = SourceMap {
+            compiled: compiled.to_owned(),
+            entries: vec![SourceMapEntry {
+                role: SpanRole::CteBody {
+                    node_id: TERMINAL_NODE_NAME.to_owned(),
+                },
+                raw: None,
+                compiled: byte_span(compiled, 0, compiled.len()),
+            }],
+        };
+        append_zone_entries(&mut sm, raw, compiled);
+        let payload = CodeMapPayload::from_source_map(&sm);
+        assert_eq!(payload.raw_zones.len(), 1);
+        let z = &payload.raw_zones[0];
+        assert_eq!(
+            z.presence, "compiled_out",
+            "ambiguous anchor ⇒ honest absence, never a fabricated presence"
+        );
         assert!(
-            payload.raw_zones[0].compiled.is_none(),
-            "compiled: None (pruned) survives the projection verbatim"
+            z.node_id.is_none(),
+            "no fabricated node edge from an ambiguous anchor"
+        );
+    }
+
+    #[test]
+    fn resolve_zone_compiled_prefers_a_unique_shorter_anchor_over_an_ambiguous_longer_one() {
+        // The longest literal ("where status = 'active'") is ambiguous (appears
+        // twice), but a shorter UNIQUE literal fragment ("and rare_marker_q > 0")
+        // is present exactly once → bind that one (degrade gracefully to the
+        // next-best unambiguous candidate, never lie, never give up early).
+        let raw = "{% if is_incremental() %}where status = 'active'\n\
+                   {{ adapter }}and rare_marker_q > 0{% endif %}";
+        let zone = &locate_raw_zones(raw)[0];
+        let compiled = "select * from a where status = 'active'\n\
+                        union all\n\
+                        select * from b where status = 'active' and rare_marker_q > 0";
+        let span = resolve_zone_compiled(raw, &zone.raw_span, compiled)
+            .expect("the unique shorter fragment resolves");
+        let matched = &compiled[span.byte_range()];
+        assert_eq!(
+            matched, "and rare_marker_q > 0",
+            "binds the UNIQUE fragment, not the first occurrence of the ambiguous one"
         );
     }
 
+    // ── cute-dbt#448 FIX C (CodeRabbit Major, fail-closed): an orphan mid-block
+    // tag (`{% else %}` / `{% elif %}` with no opener on the depth-stack) is
+    // malformed Jinja → empty vec, never letting a later zone emit. ──
+
+    #[test]
+    fn scan_orphan_else_yields_empty_fail_closed() {
+        // An `{% else %}` with NO `{% if %}` opener is malformed — fail-closed.
+        assert!(
+            locate_raw_zones("select 1\n{% else %}\nselect 2").is_empty(),
+            "an orphan else (no opener) ⇒ empty (fail-closed)"
+        );
+        // An orphan `{% elif %}` is equally malformed.
+        assert!(
+            locate_raw_zones("select 1\n{% elif x %}\nselect 2").is_empty(),
+            "an orphan elif (no opener) ⇒ empty (fail-closed)"
+        );
+        // And it must NOT let a LATER well-formed zone emit from a broken stream.
+        assert!(
+            locate_raw_zones("{% else %}\n{% for x in xs %}a{% endfor %}").is_empty(),
+            "an orphan else must abort the whole scan, not just its own region"
+        );
+    }
+
+    #[test]
+    fn scan_well_placed_else_inside_a_block_still_pairs() {
+        // A WELL-PLACED `{% else %}` (inside an open block) is fine — the block
+        // still pairs and emits its zone. The orphan guard must not regress this.
+        let raw = "{% if is_incremental() %}a{% else %}b{% endif %}";
+        let zones = locate_raw_zones(raw);
+        assert_eq!(zones.len(), 1, "a well-placed else does not break pairing");
+        assert_eq!(zones[0].kind, ZoneKind::IncrementalGuard);
+    }
+
+    // ── cute-dbt#448 FIX D (CodeRabbit Major, classifier correctness): the
+    // IncrementalGuard classification matches an ACTUAL `is_incremental()` CALL,
+    // not a bare substring. ──
+
+    #[test]
+    fn classify_is_incremental_matches_an_actual_call_not_a_substring() {
+        // A real `is_incremental()` call IS a guard (zone emitted).
+        let raw = "{% if is_incremental() %}a{% endif %}";
+        let zones = locate_raw_zones(raw);
+        assert_eq!(zones.len(), 1);
+        assert_eq!(zones[0].kind, ZoneKind::IncrementalGuard);
+
+        // A larger identifier merely CONTAINING `is_incremental` is NOT a guard
+        // (it is a plain `{% if %}` — pairs, emits no zone).
+        assert!(
+            locate_raw_zones("{% if some_is_incremental_flag %}a{% endif %}").is_empty(),
+            "`some_is_incremental_flag` is a plain if, not an incremental guard"
+        );
+
+        // A QUOTED string containing `is_incremental` is data, not a call.
+        assert!(
+            locate_raw_zones("{% if x == 'is_incremental' %}a{% endif %}").is_empty(),
+            "a quoted 'is_incremental' literal is not a call ⇒ plain if, no zone"
+        );
+
+        // The bare token with no call parens is also not a call.
+        assert!(
+            locate_raw_zones("{% if is_incremental %}a{% endif %}").is_empty(),
+            "`is_incremental` with no `(` is not a call ⇒ plain if, no zone"
+        );
+
+        // Whitespace between the token and `(` is still a call.
+        let zones = locate_raw_zones("{% if is_incremental () %}a{% endif %}");
+        assert_eq!(zones.len(), 1, "whitespace before `(` is still a call");
+        assert_eq!(zones[0].kind, ZoneKind::IncrementalGuard);
+    }
+
+    // ── cute-dbt#448: per-helper unit coverage for the decomposed
+    // `mentions_is_incremental_call` predicates (the char-scanning broken into
+    // small single-purpose fns so each stays well under the strict CC gate). ──
+
+    #[test]
+    fn is_word_byte_classifies_identifier_chars() {
+        for b in [b'a', b'Z', b'0', b'9', b'_'] {
+            assert!(is_word_byte(b), "{} is a word char", b as char);
+        }
+        for b in [b' ', b'(', b'\'', b'-', b'.'] {
+            assert!(!is_word_byte(b), "{} is not a word char", b as char);
+        }
+    }
+
+    #[test]
+    fn token_boundary_helpers_honor_identifier_edges() {
+        // `xis_incremental` — left byte is a word char ⇒ left boundary fails.
+        let b = b"xis_incremental";
+        assert!(!token_left_boundary_ok(b, 1));
+        // Token at the very start ⇒ left boundary ok.
+        assert!(token_left_boundary_ok(b"is_incremental", 0));
+        // Right boundary: next byte `_` is a word char ⇒ fails.
+        assert!(!token_right_boundary_ok(b"is_incremental_x", 14));
+        // Right boundary at end-of-string ⇒ ok.
+        assert!(token_right_boundary_ok(b"is_incremental", 14));
+        // Right boundary: next byte `(` is not a word char ⇒ ok.
+        assert!(token_right_boundary_ok(b"is_incremental(", 14));
+    }
+
+    #[test]
+    fn call_paren_follows_skips_whitespace_then_requires_open_paren() {
+        assert!(call_paren_follows(b"(", 0));
+        assert!(call_paren_follows(b"   (", 0), "leading ws then `(`");
+        assert!(!call_paren_follows(b"   ", 0), "ws only, no `(`");
+        assert!(!call_paren_follows(b"x", 0), "non-`(` byte");
+        assert!(!call_paren_follows(b"", 0), "empty ⇒ no `(`");
+    }
+
+    #[test]
+    fn offset_in_string_literal_detects_quote_state() {
+        // Inside a single-quoted literal.
+        let s = b"x == 'is_incremental'";
+        assert!(
+            offset_in_string_literal(s, 6),
+            "offset 6 is inside the quote"
+        );
+        // The same buffer, AFTER the closing quote ⇒ not inside.
+        assert!(!offset_in_string_literal(s, 21));
+        // Outside any quote.
+        assert!(!offset_in_string_literal(b"is_incremental()", 0));
+        // Inside a DOUBLE-quoted literal (the other quote arm).
+        assert!(offset_in_string_literal(b"x == \"abc\"", 6));
+        // A `\`-escape inside a quote does not prematurely close it: the
+        // escaped `'` is consumed, so the offset past it is still inside.
+        assert!(
+            offset_in_string_literal(b"'a\\'b'c", 4),
+            "escaped quote keeps the literal open"
+        );
+        // A trailing lone backslash at end-of-buffer is handled (no overrun).
+        assert!(offset_in_string_literal(b"'a\\", 3), "unterminated escape");
+    }
+
+    #[test]
+    fn step_string_scan_advances_and_tracks_quotes() {
+        // Opening a quote from the neutral state.
+        let mut q: Option<u8> = None;
+        let next = step_string_scan(b"'a'", 0, &mut q);
+        assert_eq!((next, q), (1, Some(b'\'')));
+        // Closing the matching quote.
+        let mut q = Some(b'\'');
+        let next = step_string_scan(b"'a'", 2, &mut q);
+        assert_eq!((next, q), (3, None));
+        // An escape inside a quote skips two bytes and keeps the quote open.
+        let mut q = Some(b'\'');
+        let next = step_string_scan(b"\\'", 0, &mut q);
+        assert_eq!((next, q), (2, Some(b'\'')));
+        // A double-quote opener (the other neutral-state arm).
+        let mut q: Option<u8> = None;
+        let next = step_string_scan(b"\"x", 0, &mut q);
+        assert_eq!((next, q), (1, Some(b'"')));
+        // A neutral, non-quote byte just advances by one.
+        let mut q: Option<u8> = None;
+        let next = step_string_scan(b"ab", 0, &mut q);
+        assert_eq!((next, q), (1, None));
+    }
+
     /// cute-dbt#445 TDD #4 (the inlined-data assertion, without a browser): the
     /// serialized model payload — the data script the headless report reads —
     /// carries `code_map.compiled` + `node_spans`. The headless leg
diff --git a/src/domain/source_map.rs b/src/domain/source_map.rs
index b46b0fe..5e03fda 100644
--- a/src/domain/source_map.rs
+++ b/src/domain/source_map.rs
@@ -100,20 +100,48 @@ impl SourceMapEntry {
     /// compiled span for a parsed CTE / terminal select), so presence is
     /// TRIVIALLY `compiled.is_some()` — no 3-state, no containment scan. The
     /// only producer of a `compiled: None` entry, or of a span nested INSIDE
-    /// another's range, is the deferred `RawZoneSync` raw-zone path (S5), where
-    /// the 3-state `presence()` lands WITH it.
+    /// another's range, is the raw-zone path (cute-dbt#448), where the 3-state
+    /// [`SourceMapEntry::presence`] lands WITH it.
     #[must_use]
     pub fn is_compiled_in(&self) -> bool {
         self.compiled.is_some()
     }
+
+    /// The 3-state presence verdict — CORE (cute-dbt#448), un-gated. Derived,
+    /// NEVER stored (the entry holds `Option<SourceSpan>` only; this is the
+    /// single honesty authority, computed once at the projection boundary and
+    /// emitted as a string the prototype renders verbatim — never recomputed in
+    /// JS).
+    ///
+    /// `cte_spans` are the `CteBody` compiled spans of the SAME model (the
+    /// node-span table). The contract (never-a-false-claim):
+    /// - `compiled: None` ⇒ [`Presence::CompiledOut`] (tokens pruned this
+    ///   build; the entry is type-incapable of binding a node).
+    /// - `compiled: Some(c)` strictly contained inside a LARGER `CteBody` span
+    ///   (not equal to it) ⇒ [`Presence::Structural`] (a zone expanding inside
+    ///   one node body, e.g. a Shape-A `{% for %}`); click flashes the
+    ///   containing node.
+    /// - `compiled: Some(_)` otherwise ⇒ [`Presence::CompiledIn`].
+    ///
+    /// The containment uses the off-by-one-free half-open
+    /// [`SourceSpan::contains_range`] (no `end.byte - 1` arithmetic).
+    #[must_use]
+    pub fn presence(&self, cte_spans: &[SourceSpan]) -> Presence {
+        match self.compiled {
+            None => Presence::CompiledOut,
+            // Strictly contained inside a LARGER CTE body (not equal to it).
+            Some(c) if cte_spans.iter().any(|n| *n != c && n.contains_range(&c)) => {
+                Presence::Structural
+            }
+            Some(_) => Presence::CompiledIn,
+        }
+    }
 }
 
-/// RESERVED 3-state presence (cheap, forward-compatible) — the deriving METHOD
-/// lands behind `Experiment::RawZoneSync` (S5), NOT in core. The enum is kept
-/// now so the wire/derive surface is stable; the deriving method, `Structural`
-/// detection, and the containment scan are S5 work, because only the S5
-/// raw-zone path can ever produce a `compiled: None` or a span strictly nested
-/// inside a CTE body.
+/// The 3-state presence verdict (cute-dbt#448) — CORE, un-gated. Derived once
+/// by [`SourceMapEntry::presence`] at the `RawZonePayload` projection boundary
+/// and emitted as a string; NEVER a stored domain field (no drift), NEVER
+/// recomputed in JS (the prototype renders the pre-derived badge verbatim).
 #[derive(Debug, Clone, Copy, PartialEq, Eq)]
 pub enum Presence {
     /// Tokens present in this build's compiled output.
@@ -124,6 +152,19 @@ pub enum Presence {
     Structural,
 }
 
+impl Presence {
+    /// The `snake_case` wire string the Claude-Design prototype renders
+    /// verbatim (L6) — the single derive-once authority, never duplicated in JS.
+    #[must_use]
+    pub fn to_wire(self) -> &'static str {
+        match self {
+            Self::CompiledIn => "compiled_in",
+            Self::CompiledOut => "compiled_out",
+            Self::Structural => "structural",
+        }
+    }
+}
+
 /// One model's full raw↔compiled source map: the faithful text + the
 /// correspondence table. The SINGLE source of truth; per-CTE `compiled_sql`
 /// slices are a DERIVED PROJECTION of `(compiled, entries)` — not stored twice.
@@ -642,12 +683,127 @@ mod tests {
 
     #[test]
     fn presence_variants_are_distinct() {
-        // The reserved enum is stable now (the deriving method lands in S5).
         assert_ne!(Presence::CompiledIn, Presence::CompiledOut);
         assert_ne!(Presence::CompiledIn, Presence::Structural);
         assert_ne!(Presence::CompiledOut, Presence::Structural);
     }
 
+    #[test]
+    fn presence_to_wire_is_the_snake_case_string() {
+        assert_eq!(Presence::CompiledIn.to_wire(), "compiled_in");
+        assert_eq!(Presence::CompiledOut.to_wire(), "compiled_out");
+        assert_eq!(Presence::Structural.to_wire(), "structural");
+    }
+
+    // ── cute-dbt#448 (Z2): the 3-state presence() derivation ──
+
+    // `compiled: None` ⇒ CompiledOut (the incremental-guard compiled-OUT case:
+    // is_incremental()==false strips the guard body before the DAG; the zone is
+    // the honest "pruned this build" verdict).
+    #[test]
+    fn presence_none_compiled_is_compiled_out() {
+        let zone = SourceMapEntry {
+            role: SpanRole::Zone {
+                kind: ZoneKind::IncrementalGuard,
+            },
+            raw: Some(span(0, 30)),
+            compiled: None,
+        };
+        // Even with CTE spans available, a None-compiled zone is CompiledOut —
+        // and crucially type-incapable of any node edge.
+        let cte_spans = [span(0, 50)];
+        assert_eq!(zone.presence(&cte_spans), Presence::CompiledOut);
+    }
+
+    // `compiled: Some(c)` strictly nested INSIDE a larger CteBody span ⇒
+    // Structural (the Shape-A {% for %} expanding columns inside one CTE body —
+    // bound to the containing node, never a guessed sibling CTE edge).
+    #[test]
+    fn presence_strictly_nested_in_a_cte_body_is_structural() {
+        // CteBody [0,50); the zone's compiled tokens land at [10,20), strictly
+        // inside the body.
+        let cte_spans = [span(0, 50)];
+        let zone = SourceMapEntry {
+            role: SpanRole::Zone {
+                kind: ZoneKind::ForLoop,
+            },
+            raw: Some(span(5, 25)),
+            compiled: Some(span(10, 20)),
+        };
+        assert_eq!(zone.presence(&cte_spans), Presence::Structural);
+    }
+
+    // `compiled: Some(c)` not nested inside any LARGER CteBody span ⇒
+    // CompiledIn. A span EQUAL to a CteBody span is NOT structural (the
+    // `*n != c` guard): a zone whose compiled region coincides with a node is
+    // compiled-in, not nested.
+    #[test]
+    fn presence_equal_to_a_cte_body_span_is_compiled_in_not_structural() {
+        let cte_spans = [span(10, 20)];
+        let zone = SourceMapEntry {
+            role: SpanRole::Zone {
+                kind: ZoneKind::IncrementalGuard,
+            },
+            raw: Some(span(0, 30)),
+            compiled: Some(span(10, 20)),
+        };
+        assert_eq!(
+            zone.presence(&cte_spans),
+            Presence::CompiledIn,
+            "a span equal to a CteBody span is compiled-in, not structural"
+        );
+    }
+
+    // `compiled: Some(c)` with no containing CteBody (terminal WHERE predicate
+    // of an incremental guard compiling IN: the verified
+    // fct_encounters_incremental shape) ⇒ CompiledIn.
+    #[test]
+    fn presence_some_with_no_containing_body_is_compiled_in() {
+        // The guard's tokens compile in as a terminal WHERE [40,60), contained
+        // by NOTHING larger (cte_spans are smaller, earlier bodies).
+        let cte_spans = [span(0, 20), span(20, 38)];
+        let zone = SourceMapEntry {
+            role: SpanRole::Zone {
+                kind: ZoneKind::IncrementalGuard,
+            },
+            raw: Some(span(0, 70)),
+            compiled: Some(span(40, 60)),
+        };
+        assert_eq!(zone.presence(&cte_spans), Presence::CompiledIn);
+    }
+
+    // THE FITNESS TEST (§7, never-a-false-claim): compiled == None ⇒ no node
+    // edge. The presence verdict is CompiledOut for EVERY possible cte_spans
+    // configuration — a None-compiled entry can never bind a node.
+    #[test]
+    fn fitness_compiled_none_never_binds_a_node_for_any_cte_spans() {
+        // Exhaustive small enumeration of CteBody-span configurations.
+        const N: u32 = 5;
+        let zone = SourceMapEntry {
+            role: SpanRole::Zone {
+                kind: ZoneKind::ForLoop,
+            },
+            raw: Some(span(0, 100)),
+            compiled: None,
+        };
+        for a in 0..=N {
+            for b in a..=N {
+                for c in 0..=N {
+                    for d in c..=N {
+                        let cte_spans = [span(a, b), span(c, d)];
+                        assert_eq!(
+                            zone.presence(&cte_spans),
+                            Presence::CompiledOut,
+                            "compiled:None is CompiledOut for cte_spans [{a},{b}),[{c},{d}) — never an edge"
+                        );
+                    }
+                }
+            }
+        }
+        // And with no CteBody spans at all.
+        assert_eq!(zone.presence(&[]), Presence::CompiledOut);
+    }
+
     // unused-import guard: CteEdge/EdgeType used to keep the test module's
     // domain-cte surface honest (a CteGraph with an edge still assembles).
     #[test]