diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index c09cdc36..30bab507 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -341,6 +341,7 @@ jobs:
     #   - 26 after review_scope_variants (cute-dbt#302)
     #   - 27 after review_pr_anchor (cute-dbt#303)
     #   - 28 after agent_skill (cute-dbt#304)
+    #   - 29 after macro_perspective (cute-dbt#265)
     # The README is not a feature. The job name deliberately carries no
     # number (cute-dbt#68 — the old `==6` display label went stale across
     # several bumps): the enforced count lives ONLY in the `expected=`
@@ -358,7 +359,7 @@ jobs:
       - name: features/*.feature count matches expected
         run: |
           set -euo pipefail
-          expected=28
+          expected=29
           count=$(find features -maxdepth 1 -name '*.feature' | wc -l | tr -d ' ')
           echo "feature-count: expected ${expected}, found ${count}"
           if [ "$count" != "$expected" ]; then
diff --git a/examples/diff-showcase-report.html b/examples/diff-showcase-report.html
index c511ef8d..df34e613 100644
--- a/examples/diff-showcase-report.html
+++ b/examples/diff-showcase-report.html
@@ -2357,6 +2357,34 @@ <h2>Project definition changed</h2>
     </li>
   </ul>
 </section>
+<section class="macro-lens-panel" aria-label="Macro changes" data-testid="macro-lens-panel">
+  <h2>Macro changed <span class="macro-lens-experimental" data-testid="macro-lens-experimental">experimental</span></h2>
+  <p class="macro-lens-subtitle">A changed macro is invisible to model and unit-test scope selection &mdash; this section shows the macros this PR changed and the root-project models they reach. <span class="macro-lens-fidelity" data-testid="macro-lens-fidelity" data-fidelity="heuristic">fidelity: heuristic</span></p>
+  <div class="macro-lens-macro" data-testid="macro-lens-macro" data-macro="quarantine_filter">
+    <p class="macro-lens-macro-head"><code class="macro-lens-name">quarantine_filter</code> <span class="macro-lens-package" data-testid="macro-lens-package">healthcare_analytics</span> <code class="macro-lens-path">macros/data_quality/quarantine_filter.sql</code></p>
+    <pre class="macro-lens-diff" data-testid="macro-lens-diff"><span class="macro-lens-line is-context">{% macro quarantine_filter(enabled=true, field_name=&#39;is_dq_valid&#39;) %}</span>
+<span class="macro-lens-line is-context">    {% if enabled %}</span>
+<span class="macro-lens-line is-removed">    where {{ field_name }} is true</span>
+<span class="macro-lens-line is-added">    where {{ field_name }} = true</span>
+<span class="macro-lens-line is-context">    {% endif %}</span>
+<span class="macro-lens-line is-context">{% endmacro %}</span>
+</pre>
+    <p class="macro-lens-count" data-testid="macro-lens-count" data-count="3">Impacts 3 root-project models.</p>
+    <details class="macro-lens-tree-details" data-testid="macro-lens-tree-details" open>
+      <summary>impacted models</summary>
+      <ul class="macro-lens-tree" data-testid="macro-lens-tree">
+        <li class="macro-lens-tree-row macro-lens-tree-dir" data-testid="macro-lens-tree-row" data-kind="dir" data-depth="0" style="--macro-tree-depth: 0"><span class="macro-lens-tree-dir">models/</span></li>
+        <li class="macro-lens-tree-row macro-lens-tree-dir" data-testid="macro-lens-tree-row" data-kind="dir" data-depth="1" style="--macro-tree-depth: 1"><span class="macro-lens-tree-dir">intermediate/</span></li>
+        <li class="macro-lens-tree-row macro-lens-tree-dir" data-testid="macro-lens-tree-row" data-kind="dir" data-depth="2" style="--macro-tree-depth: 2"><span class="macro-lens-tree-dir">healthcare/</span></li>
+        <li class="macro-lens-tree-row macro-lens-tree-model" data-testid="macro-lens-tree-row" data-kind="model" data-depth="3" data-model="model.healthcare_analytics.int_encounters__enriched" style="--macro-tree-depth: 3"><code class="macro-lens-tree-model">int_encounters__enriched</code></li>
+        <li class="macro-lens-tree-row macro-lens-tree-dir" data-testid="macro-lens-tree-row" data-kind="dir" data-depth="1" style="--macro-tree-depth: 1"><span class="macro-lens-tree-dir">marts/</span></li>
+        <li class="macro-lens-tree-row macro-lens-tree-dir" data-testid="macro-lens-tree-row" data-kind="dir" data-depth="2" style="--macro-tree-depth: 2"><span class="macro-lens-tree-dir">core/</span></li>
+        <li class="macro-lens-tree-row macro-lens-tree-model" data-testid="macro-lens-tree-row" data-kind="model" data-depth="3" data-model="model.healthcare_analytics.fct_clinical_events" style="--macro-tree-depth: 3"><code class="macro-lens-tree-model">fct_clinical_events</code></li>
+        <li class="macro-lens-tree-row macro-lens-tree-model" data-testid="macro-lens-tree-row" data-kind="model" data-depth="3" data-model="model.healthcare_analytics.fct_encounters" style="--macro-tree-depth: 3"><code class="macro-lens-tree-model">fct_encounters</code></li>
+      </ul>
+    </details>
+  </div>
+</section>
 
 <section class="governance-panel" aria-label="Governance &amp; ownership" data-testid="governance-panel">
   <span class="gov-group-chip" data-testid="gov-group-chip" data-group="clinical_quality">group clinical_quality &middot; owner Clinical Quality Team &lt;clinical-quality@example.com&gt;</span>
@@ -2554,7 +2582,7 @@ <h2>Expected</h2>
 
 </div>
 
-<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)"},"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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='date_key'\n    )\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"}]},{"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)"},"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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='patient_key'\n    )\n}}\n\n-- depends_on: {{ ref('snp_patients') }}\n-- depends_on: {{ ref('stg_synthea__patients') }}\n\n{#\n    Patient Dimension with SCD Type 2 History Tracking\n\n    This dimension sources from the snp_patients snapshot, which tracks\n    changes to patient attributes over time. 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 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: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}]},{"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)"},"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"}]},{"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)"},"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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        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"}]},{"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        , 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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        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"}]},{"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)"},"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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='encounter_key'\n    )\n}}\n\nwith encounters as (\n    select * from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from {{ ref('dim_providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from {{ ref('dim_organizations') }}\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    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 loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"]},"evidence":[{"label":"unique_key","value":"encounter_key"}],"pin_node":"(final select)"},{"check":"enforcement.constraint-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters","construct":"constraint.primary_key[encounter_key]","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"]},"evidence":[{"label":"constraint","value":"primary_key on encounter_key"},{"label":"adapter","value":"duckdb (metadata-only)"},{"label":"backing-test","value":"present"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}]},{"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)"},"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. 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"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key=['year_month', 'encounter_class']\n    )\n}}\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 {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , 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"}]},{"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        , 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    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_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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key=['year_actual', 'encounter_class']\n    )\n}}\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 {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\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_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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='patient_summary_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Annual Patient Summary Fact\n\n    Purpose: Patient-level annual snapshots for outcomes tracking\n    Grain: One row per patient per calendar year\n    Use cases: Patient outcomes, annual cost summaries, cohort analysis\n\n    Aggregates encounters, procedures, conditions, and costs by patient and year.\n    Joins to 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"}]},{"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)"},"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"}]},{"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)"},"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"}]},{"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)"},"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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='ephemeral',\n        tags=['analytics', 'marts', 'view']\n    )\n}}\n\n{#\n    Encounter Summary View (Ephemeral)\n\n    Purpose: Enriched encounter view with dimension attributes\n    Materialization: Ephemeral (not persisted) - used as a building block\n    Use cases: Ad-hoc queries, building blocks for other models, BI tool queries\n\n    Joins fct_encounters with all dimension tables to provide a denormalized\n    view with human-readable names instead of just keys.\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 {{ ref('fct_encounters') }}\n),\n\npatients as (\n    select\n        patient_key\n        , full_name as patient_name\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , 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"}]},{"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 views\n","dag":{"nodes":[{"id":"condition_cohorts","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_patient_current_conditions.sql","role":"final"}],"edges":[{"from":"condition_cohorts","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_cohorts":"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 \"memory\".\"main_marts\".\"fct_condition_cohorts\"\n    where is_active = true\n)","final":"final 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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'marts', 'view']\n    )\n}}\n\n{#\n    Patient Current Conditions View\n\n    Purpose: Current (active) conditions for each patient\n    Grain: One row per 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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'marts', 'view']\n    )\n}}\n\n{#\n    Provider Active Patients View\n\n    Purpose: Active patient count per provider for panel management\n    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"}]}],"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"}]},"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":34,"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":34,"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":"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":[]}]}}</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)"},"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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='date_key'\n    )\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"}]},{"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)"},"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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='patient_key'\n    )\n}}\n\n-- depends_on: {{ ref('snp_patients') }}\n-- depends_on: {{ ref('stg_synthea__patients') }}\n\n{#\n    Patient Dimension with SCD Type 2 History Tracking\n\n    This dimension sources from the snp_patients snapshot, which tracks\n    changes to patient attributes over time. 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 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: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}]},{"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)"},"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"}]},{"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)"},"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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        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"}]},{"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        , 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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        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"}]},{"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)"},"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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='encounter_key'\n    )\n}}\n\nwith encounters as (\n    select * from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from {{ ref('dim_providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from {{ ref('dim_organizations') }}\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    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 loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"]},"evidence":[{"label":"unique_key","value":"encounter_key"}],"pin_node":"(final select)"},{"check":"enforcement.constraint-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters","construct":"constraint.primary_key[encounter_key]","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"]},"evidence":[{"label":"constraint","value":"primary_key on encounter_key"},{"label":"adapter","value":"duckdb (metadata-only)"},{"label":"backing-test","value":"present"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}]},{"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)"},"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. 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"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key=['year_month', 'encounter_class']\n    )\n}}\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 {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , 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"}]},{"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        , 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    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_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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key=['year_actual', 'encounter_class']\n    )\n}}\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 {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\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_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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='patient_summary_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Annual Patient Summary Fact\n\n    Purpose: Patient-level annual snapshots for outcomes tracking\n    Grain: One row per patient per calendar year\n    Use cases: Patient outcomes, annual cost summaries, cohort analysis\n\n    Aggregates encounters, procedures, conditions, and costs by patient and year.\n    Joins to 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"}]},{"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)"},"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"}]},{"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)"},"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"}]},{"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)"},"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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='ephemeral',\n        tags=['analytics', 'marts', 'view']\n    )\n}}\n\n{#\n    Encounter Summary View (Ephemeral)\n\n    Purpose: Enriched encounter view with dimension attributes\n    Materialization: Ephemeral (not persisted) - used as a building block\n    Use cases: Ad-hoc queries, building blocks for other models, BI tool queries\n\n    Joins fct_encounters with all dimension tables to provide a denormalized\n    view with human-readable names instead of just keys.\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 {{ ref('fct_encounters') }}\n),\n\npatients as (\n    select\n        patient_key\n        , full_name as patient_name\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , 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"}]},{"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 views\n","dag":{"nodes":[{"id":"condition_cohorts","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_patient_current_conditions.sql","role":"final"}],"edges":[{"from":"condition_cohorts","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_cohorts":"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 \"memory\".\"main_marts\".\"fct_condition_cohorts\"\n    where is_active = true\n)","final":"final 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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'marts', 'view']\n    )\n}}\n\n{#\n    Patient Current Conditions View\n\n    Purpose: Current (active) conditions for each patient\n    Grain: One row per 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"}]},{"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)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'marts', 'view']\n    )\n}}\n\n{#\n    Provider Active Patients View\n\n    Purpose: Active patient count per provider for panel management\n    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"}]}],"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"}]},"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":34,"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":34,"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":"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"}]}],"fidelity":"heuristic"}}</script>
 
 <script>
 /*! jQuery v3.7.1 | (c) OpenJS Foundation and other contributors | jquery.org/license */
diff --git a/features/macro_perspective.feature b/features/macro_perspective.feature
new file mode 100644
index 00000000..73f8ce79
--- /dev/null
+++ b/features/macro_perspective.feature
@@ -0,0 +1,62 @@
+# Maps: cute-dbt#265 (epic #265 — the macro perspective lens). A macro
+# edit is invisible to cute-dbt's model-and-unit-test scope selection: a
+# macros/*.sql file matches no model `original_file_path` and macros are
+# not unit-test targets, so a macro change slips through entirely. Slice B
+# gives the report a "macro changed" section — the changed macro's body
+# diff, the count of root-project models it reaches (the reverse
+# `macro_blast_radius`), and those models as a collapsible directory tree.
+#
+# cute-dbt#265 Slice B (epic #288 default-OFF posture): the whole macro
+# lens is gated behind the `macro-lens` experiment. Every scenario
+# asserting the section opts in via the experimental-switch Given
+# (CUTE_DBT_EXPERIMENTAL on the subprocess); the switch-off scenario at
+# the bottom pins the default posture (no section even with a macro edit,
+# byte-stable non-macro goldens). The section copy says "macro changed",
+# NEVER a `state:modified.macros` selector name (critique S2): the
+# macro-body-changed scope is a synthesized class, not a dbt state
+# selector.
+Feature: A changed macro is called out, never silently invisible
+  As a PR reviewer
+  I want a macros/*.sql edit surfaced with the models it reaches
+  So that a macro change is part of the review, not an invisible side door
+
+  Scenario: A macro edit in pr-diff mode renders the section with a body diff and the impacted-model tree
+    Given a current manifest with a root-project macro called by two models
+    And the working tree carries that macro's source file
+    And the PR diff edits the macro's body
+    And the experimental switch enables macro-lens
+    When I run cute-dbt report in pr-diff mode against the macro patch
+    Then the exit code is 0
+    And the report carries the macro-lens section
+    And the macro-lens section names the changed macro
+    And the macro-lens section carries the macro body diff
+    And the macro-lens section reports the impacted-model count as 2
+    And the macro-lens section lists both impacted models in the directory tree
+    And the macro-lens fidelity chip reads "heuristic"
+    And the macro-lens section never names a "state:modified.macros" selector
+
+  Scenario: No macro edit leaves the section absent and the report byte-stable
+    Given a current manifest with a root-project macro called by two models
+    And the working tree carries that macro's source file
+    And the PR diff edits only a model's SQL, not the macro
+    And the experimental switch enables macro-lens
+    When I run cute-dbt report in pr-diff mode against the macro patch
+    Then the exit code is 0
+    And the report carries no macro-lens section
+
+  Scenario: With the macro-lens switch off a macro edit renders no section
+    Given a current manifest with a root-project macro called by two models
+    And the working tree carries that macro's source file
+    And the PR diff edits the macro's body
+    When I run cute-dbt report in pr-diff mode against the macro patch
+    Then the exit code is 0
+    And the report carries no macro-lens section
+
+  Scenario: A vendor-package macro edit is filtered out of the lens
+    Given a current manifest with a vendor-package macro called by a root-project model
+    And the working tree carries that macro's source file
+    And the PR diff edits the macro's body
+    And the experimental switch enables macro-lens
+    When I run cute-dbt report in pr-diff mode against the macro patch
+    Then the exit code is 0
+    And the report carries no macro-lens section
diff --git a/lefthook.yml b/lefthook.yml
index 5edcef6b..c4fb453f 100644
--- a/lefthook.yml
+++ b/lefthook.yml
@@ -89,7 +89,9 @@ pre-push:
     feature-count:
       # Asserts the .feature-count invariant; the enforced count lives ONLY
       # in the `expected=` line below (cute-dbt#68 — numbers in labels and
-      # comments go stale). History: bumped to 28 in cute-dbt#304 when
+      # comments go stale). History: bumped to 29 in cute-dbt#265 when
+      # macro_perspective.feature joined the contract; previously
+      # bumped to 28 in cute-dbt#304 when
       # agent_skill.feature joined the contract; previously
       # bumped in cute-dbt#303 when review_pr_anchor.feature joined; previously
       # bumped in cute-dbt#302 when review_scope_variants.feature joined; previously
@@ -111,7 +113,7 @@ pre-push:
       # Mirrors the `feature-count` CI job; the README is not a feature.
       run: |
         set -euo pipefail
-        expected=28
+        expected=29
         count=$(find features -maxdepth 1 -name '*.feature' | wc -l | tr -d ' ')
         if [ "$count" != "$expected" ]; then
           echo "feature-count: expected ${expected} .feature files, found ${count}"
diff --git a/src/adapters/render.rs b/src/adapters/render.rs
index 8527e2ec..741b45e5 100644
--- a/src/adapters/render.rs
+++ b/src/adapters/render.rs
@@ -69,11 +69,12 @@ use crate::adapters::cte_engine::{TERMINAL_NODE_NAME, parse_cte_graph};
 use crate::domain::{
     BANNER_EMPTY_SCOPE, BlockDiff, CheckId, CheckPolicy, ConfigAttribution, CteGraph, DiffLine,
     DiffLineKind, EdgeType, Finding, FixtureTable, GovernanceFacts, HeuristicId, HookChangeFacts,
-    HookManifestPresence, InScopeSet, Instrument, Manifest, ModelInScopeSet, ModelYamlOutcome,
-    Node, NodeId, ProjectChange, ProjectChangeCategory, ProjectChangePanel, ProjectDefinition,
-    ProjectFacts, ProjectFallbackReason, SourceNode, TestMetadata, Tier, UnitTest,
-    UnitTestDataDiff, UnitTestGiven, UnitTestOverrides, UnitTestYamlBlock, VarAttribution,
-    VarChangeFacts, VarReference, VarScanFootprint, apply_check_policy, model_findings,
+    HookManifestPresence, InScopeSet, Instrument, MacroIdentity, Manifest, ModelInScopeSet,
+    ModelYamlOutcome, Node, NodeId, NormalizedDiffIndex, ProjectChange, ProjectChangeCategory,
+    ProjectChangePanel, ProjectDefinition, ProjectFacts, ProjectFallbackReason, SourceNode,
+    TestMetadata, Tier, UnitTest, UnitTestDataDiff, UnitTestGiven, UnitTestOverrides,
+    UnitTestYamlBlock, VarAttribution, VarChangeFacts, VarReference, VarScanFootprint,
+    apply_check_policy, macro_blast_radius, model_findings, reconstruct_macro_sql_diff,
     resolve_target_model, resolve_tested_model, table_from_manifest_rows,
 };
 
@@ -1384,6 +1385,106 @@ pub struct ReportPayload {
     /// (`experimental: ""`) goldens stay byte-identical.
     #[serde(skip_serializing_if = "GovernanceFacts::is_empty")]
     pub governance: GovernanceFacts,
+    /// The macro perspective lens facts (cute-dbt#265, Slice B) — the
+    /// changed root-project macros, each with its body diff, its
+    /// blast-radius impacted-model directory tree, and the count. Gated
+    /// behind [`Experiment::MacroLens`](crate::domain::Experiment::MacroLens):
+    /// the cli layer passes `None` when the experiment is off, so the key
+    /// is omitted from the JSON and the `{%- if macro_lens %}` template
+    /// section emits zero bytes — keeping the non-macro goldens
+    /// byte-identical. The same struct rides the JSON payload (downstream
+    /// consumers + headless assertions) and is server-rendered into the
+    /// section.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub macro_lens: Option<MacroLensPayload>,
+}
+
+/// The macro perspective lens (cute-dbt#265, Slice B) — the "macro changed"
+/// section facts.
+///
+/// Present (`Some`) exactly when the [`Experiment::MacroLens`](crate::domain::Experiment::MacroLens)
+/// experiment is on AND at least one root-project macro changed in the PR.
+/// Carries one [`ChangedMacroView`] per changed macro plus the per-arm
+/// [`fidelity`](Self::fidelity) chip. Server-rendered into the template +
+/// serialized into the JSON payload (the [`GovernanceFacts`] both-surfaces
+/// precedent).
+#[derive(Debug, Clone, Serialize)]
+pub struct MacroLensPayload {
+    /// Each changed root-project macro, in deterministic id order.
+    pub macros: Vec<ChangedMacroView>,
+    /// The fidelity of the change signal for THIS report's scope arm:
+    /// `"exact"` on the `--baseline-manifest` arm (a direct `macro_sql`
+    /// body comparison — fusion's `check_modified_macros` semantics) or
+    /// `"heuristic"` on the `--pr-diff` arm (path-primary + name-fallback
+    /// resolution against the diff). The chip states this plainly so the
+    /// reviewer reads the section's confidence honestly (critique S2 — no
+    /// `state:` borrowing, no false certainty).
+    pub fidelity: &'static str,
+}
+
+/// One changed root-project macro in the [`MacroLensPayload`].
+#[derive(Debug, Clone, Serialize)]
+pub struct ChangedMacroView {
+    /// The macro's bare name (the `X` of `{% macro X(...) %}`), or the
+    /// macro `unique_id` leaf when no identity name is recorded.
+    pub name: String,
+    /// The macro's owning package — always the root project here (the
+    /// blast radius and changed-macro detection both filter to it), shown
+    /// for parity with the governance chips.
+    pub package: String,
+    /// The macro's declaring file, project-relative (e.g.
+    /// `macros/data_quality/quarantine_filter.sql`). Empty when the
+    /// manifest carries no `original_file_path` for this macro (the rare
+    /// fusion null-fill).
+    pub path: String,
+    /// The reconstructed inline body diff (cute-dbt#111
+    /// [`reconstruct_macro_sql_diff`]),
+    /// present only on the `--pr-diff` arm when this macro's file was
+    /// touched + aligned + substantively changed. `None` ⇒ the section
+    /// shows the plain current body (`body_lines`).
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub diff: Option<BlockDiff>,
+    /// The macro's CURRENT body as plain context lines — the fallback the
+    /// section renders when [`diff`](Self::diff) is `None` (baseline arm,
+    /// or a pr-diff macro whose body the diff did not substantively touch).
+    /// Never empty when the macro has a body.
+    pub body_lines: Vec<DiffLine>,
+    /// The number of root-project models impacted by this macro change —
+    /// the [`macro_blast_radius`] cardinality. Stated as a count first
+    /// (critique S3 — the lightweight
+    /// surface is the count + tree, never inline bodies in Slice B).
+    pub impacted_count: usize,
+    /// The impacted models as a flattened collapsible directory tree
+    /// (founder D3), grouped by `original_file_path` directory structure.
+    /// Pre-order traversal with explicit [`depth`](MacroTreeRow::depth) so
+    /// the askama template renders the nesting without recursion. Empty
+    /// when the blast radius is empty (a materialization macro, or an
+    /// edit reaching no root-project model — the section states the
+    /// honest zero).
+    pub tree: Vec<MacroTreeRow>,
+}
+
+/// One row of a [`ChangedMacroView`]'s flattened impacted-model directory
+/// tree (cute-dbt#265 founder D3).
+///
+/// The tree is grouped by the impacted models' `original_file_path`
+/// directory segments; each directory is a `dir` row and each model is a
+/// `model` leaf. Flattened pre-order with a 0-based [`depth`](Self::depth)
+/// (the askama template indents by depth — recursion-free rendering).
+#[derive(Debug, Clone, Serialize)]
+pub struct MacroTreeRow {
+    /// `"dir"` for a directory grouping row, `"model"` for an impacted
+    /// model leaf — the template's per-kind CSS hook + `data-kind`.
+    pub kind: &'static str,
+    /// The display label: the bare directory segment for a `dir` row, the
+    /// bare model name for a `model` leaf.
+    pub label: String,
+    /// 0-based nesting depth — the template's indent driver (`dir` rows at
+    /// each path segment, the model leaf one level past its directory).
+    pub depth: usize,
+    /// The model's full node id for a `model` leaf (the stable selector
+    /// hook); empty for a `dir` row.
+    pub model_id: String,
 }
 
 /// Which scope source produced this report — selects the diff-scope
@@ -2014,6 +2115,207 @@ fn project_panel_view(
     }
 }
 
+/// Build the macro perspective lens (cute-dbt#265, Slice B) from the
+/// changed-macro set + the manifest.
+///
+/// `changed_macros` is the resolved set of changed root-project macro
+/// `unique_id`s (the cli picks the `changed_macros_pr_diff` /
+/// `changed_macros_baseline` arm by scope source). For each changed macro
+/// this resolves its name/package/path/body from the manifest, reconstructs
+/// the body diff on the `--pr-diff` arm (`index = Some(...)`), and walks the
+/// reverse [`macro_blast_radius`] into a collapsible directory tree of the
+/// impacted root-project models. `scope_source` selects the fidelity chip
+/// (`Baseline` = exact body comparison, `PrDiff` = path/name heuristic).
+///
+/// Returns `None` when `changed_macros` is empty — the cli only calls this
+/// when [`Experiment::MacroLens`](crate::domain::Experiment::MacroLens) is
+/// on, so `None` (no macro changed) and the off-gate (the cli passes the
+/// `None` directly) both omit the section, keeping the non-macro goldens
+/// byte-identical.
+#[must_use]
+pub fn build_macro_lens(
+    current: &Manifest,
+    changed_macros: &BTreeSet<String>,
+    scope_source: ScopeSource,
+    index: Option<&NormalizedDiffIndex>,
+) -> Option<MacroLensPayload> {
+    // Root-project filter: the lens is the REVIEWER's macros, never a
+    // vendor package's. The pr-diff path-primary channel resolves any macro
+    // whose file the PR touched regardless of package (a PR can vendor a
+    // dependency), so a vendor macro edit can reach the changed set — drop
+    // it here. A macro with no recorded `package_name` (the rare null-fill)
+    // is kept (fail-open: the diff resolved it to a real id), unless a
+    // project name is set and the macro's package explicitly differs.
+    let project = current.metadata().project_name();
+    let macros = changed_macros
+        .iter()
+        .filter(|macro_id| is_root_project_macro(current, macro_id, project))
+        .map(|macro_id| changed_macro_view(current, macro_id, index))
+        .collect::<Vec<_>>();
+    if macros.is_empty() {
+        return None;
+    }
+    let fidelity = match scope_source {
+        ScopeSource::Baseline => "exact",
+        ScopeSource::PrDiff => "heuristic",
+    };
+    Some(MacroLensPayload { macros, fidelity })
+}
+
+/// Whether a changed macro belongs to the root project — the lens filter
+/// (vendor-package macros are not the reviewer's concern). A macro whose
+/// recorded `package_name` matches `project` passes; a macro with no
+/// recorded package passes (fail-open — the diff resolved it to a real id);
+/// a macro whose package explicitly differs from a known project name is
+/// dropped.
+fn is_root_project_macro(current: &Manifest, macro_id: &str, project: Option<&str>) -> bool {
+    match (
+        current
+            .macro_identity()
+            .get(macro_id)
+            .and_then(MacroIdentity::package_name),
+        project,
+    ) {
+        (Some(pkg), Some(proj)) => pkg == proj,
+        // No recorded package, or no project name to compare against:
+        // fail-open (keep) — the changed-macro detection already resolved
+        // this id from the diff/baseline.
+        _ => true,
+    }
+}
+
+/// Resolve one changed macro into its [`ChangedMacroView`] — identity, body
+/// (diff on the pr-diff arm, plain context lines otherwise), and the
+/// flattened impacted-model directory tree.
+fn changed_macro_view(
+    current: &Manifest,
+    macro_id: &str,
+    index: Option<&NormalizedDiffIndex>,
+) -> ChangedMacroView {
+    let identity = current.macro_identity().get(macro_id);
+    // Name: the recorded identity name, else the unique_id leaf.
+    let name = identity
+        .and_then(|i| i.name())
+        .map_or_else(|| leaf_segment(macro_id).to_owned(), str::to_owned);
+    let package = identity
+        .and_then(|i| i.package_name())
+        .or_else(|| current.metadata().project_name())
+        .unwrap_or_default()
+        .to_owned();
+    let path = identity
+        .and_then(|i| i.original_file_path())
+        .unwrap_or_default()
+        .to_owned();
+    let body = current.macros().get(macro_id).map_or("", String::as_str);
+    // The pr-diff arm reconstructs the body diff when the macro's file was
+    // touched + aligned + substantively changed; baseline (no index) and an
+    // untouched/stale macro fall back to the plain body context lines.
+    let diff = index
+        .filter(|_| !path.is_empty())
+        .and_then(|idx| reconstruct_macro_sql_diff(body, &path, idx));
+    let body_lines = macro_body_context_lines(body);
+    let radius = macro_blast_radius(current, macro_id);
+    let impacted_count = radius.len();
+    let tree = impacted_model_tree(current, &radius);
+    ChangedMacroView {
+        name,
+        package,
+        path,
+        diff,
+        body_lines,
+        impacted_count,
+        tree,
+    }
+}
+
+/// 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
+/// line. Empty body ⇒ no lines.
+fn macro_body_context_lines(body: &str) -> Vec<DiffLine> {
+    if body.is_empty() {
+        return Vec::new();
+    }
+    let normalized = body.strip_suffix('\n').unwrap_or(body);
+    normalized
+        .split('\n')
+        .map(|line| DiffLine {
+            kind: DiffLineKind::Context,
+            text: line.to_owned(),
+            emphasis: None,
+        })
+        .collect()
+}
+
+/// Flatten the impacted-model blast radius into a collapsible directory
+/// tree (founder D3), grouped by each model's `original_file_path`
+/// directory segments.
+///
+/// Pre-order: every distinct directory prefix is a `dir` row (deepest level
+/// per its segment depth), then the model leaf one level past its directory.
+/// Deterministic — the radius is a [`BTreeSet`] (id order) and the directory
+/// grouping is built over a sorted path key, so the same radius always
+/// flattens to the same row sequence (golden-stable). A model with no
+/// `original_file_path` groups under a synthetic `(unknown path)` directory
+/// rather than being dropped (fail-open display).
+fn impacted_model_tree(current: &Manifest, radius: &BTreeSet<NodeId>) -> Vec<MacroTreeRow> {
+    // Sort by (directory-path, model-name) so the directory grouping is
+    // stable and adjacent models in the same directory cluster.
+    let mut entries: Vec<(Vec<String>, String, String)> = radius
+        .iter()
+        .map(|id| {
+            let node = current.node(id);
+            let ofp = node.and_then(Node::original_file_path);
+            let (dirs, _file) = split_dir_path(ofp);
+            let name = leaf_segment(id.as_str()).to_owned();
+            (dirs, name, id.as_str().to_owned())
+        })
+        .collect();
+    entries.sort();
+
+    let mut rows = Vec::new();
+    let mut open_dirs: Vec<String> = Vec::new();
+    for (dirs, name, model_id) in entries {
+        // Find the shared prefix with the currently-open directory stack;
+        // close (drop) the divergent tail, then open the new segments.
+        let shared = open_dirs
+            .iter()
+            .zip(dirs.iter())
+            .take_while(|(a, b)| a == b)
+            .count();
+        open_dirs.truncate(shared);
+        for segment in &dirs[shared..] {
+            rows.push(MacroTreeRow {
+                kind: "dir",
+                label: segment.clone(),
+                depth: open_dirs.len(),
+                model_id: String::new(),
+            });
+            open_dirs.push(segment.clone());
+        }
+        rows.push(MacroTreeRow {
+            kind: "model",
+            label: name,
+            depth: open_dirs.len(),
+            model_id,
+        });
+    }
+    rows
+}
+
+/// Split a model `original_file_path` into its directory segments and file
+/// leaf. A `None` / empty path groups under a synthetic `(unknown path)`
+/// directory (fail-open — an impacted model is never silently dropped).
+fn split_dir_path(ofp: Option<&str>) -> (Vec<String>, String) {
+    let path = ofp.unwrap_or("").trim_matches('/');
+    if path.is_empty() {
+        return (vec!["(unknown path)".to_owned()], String::new());
+    }
+    let mut segments: Vec<String> = path.split('/').map(str::to_owned).collect();
+    let file = segments.pop().unwrap_or_default();
+    (segments, file)
+}
+
 /// askama template binding for the v0.1 report.
 ///
 /// Asset values are pinned `&'static str` constants from
@@ -2113,6 +2415,15 @@ struct ReportTemplate<'a> {
     /// the same struct rides the JSON payload for downstream consumers +
     /// headless assertions.
     governance: GovernanceFacts,
+    /// The macro perspective lens (cute-dbt#265, Slice B) — `Some` exactly
+    /// when [`Experiment::MacroLens`](crate::domain::Experiment::MacroLens)
+    /// is on AND a root-project macro changed. The `{% if macro_lens %}`
+    /// section reads each `macros[i]` view (the body diff/lines + the
+    /// impacted-model directory tree + the count + the fidelity chip);
+    /// `None` emits zero bytes (the off-gate default), keeping the
+    /// non-macro goldens byte-identical. Rides the JSON payload too (the
+    /// `governance` both-surfaces precedent).
+    macro_lens: Option<MacroLensPayload>,
 }
 
 /// Serialize `payload` to JSON for safe embedding inside an HTML
@@ -2326,6 +2637,11 @@ pub fn build_payload_with_externals(
         // group chips are payload-level, not per-model, so they assemble
         // at the composition root, not inside the per-model walk).
         governance: GovernanceFacts::default(),
+        // cute-dbt#265 — macro lens defaults `None` here; the gated value
+        // is threaded in by `render_report_with_externals` (the macro
+        // section is payload-level, not per-model — it assembles at the
+        // composition root from the changed-macro set + blast radius).
+        macro_lens: None,
     }
 }
 
@@ -2385,6 +2701,7 @@ pub fn render_report(
         &CheckPolicy::default(),
         &ProjectFacts::default(),
         &GovernanceFacts::default(),
+        None,
     )
 }
 
@@ -2417,6 +2734,7 @@ pub fn render_report_with_externals(
     check_policy: &CheckPolicy<HeuristicId>,
     project_facts: &ProjectFacts,
     governance: &GovernanceFacts,
+    macro_lens: Option<&MacroLensPayload>,
 ) -> io::Result<()> {
     let mut payload = build_payload_with_externals(
         current,
@@ -2438,6 +2756,11 @@ pub fn render_report_with_externals(
     // JSON + zero DOM via `{%- if has_governance %}`, keeping the
     // non-experimental golden byte-identical.
     payload.governance = governance.clone();
+    // cute-dbt#265 — the gated macro lens (changed macros + body diffs +
+    // impacted-model trees, all built in `build_macro_lens`). `None` (the
+    // off-gate default, or no macro changed) ⇒ omitted from JSON + zero DOM
+    // via `{% if macro_lens %}`, keeping the non-macro goldens byte-identical.
+    payload.macro_lens = macro_lens.cloned();
     // The empty-scope banner contract reads the TRUE in-scope set, not the
     // widened render set or the changed subset (cute-dbt#91).
     let banner_text = compose_banner_text(in_scope);
@@ -2471,6 +2794,7 @@ pub fn render_report_with_externals(
             || !project_facts.var_references.is_empty(),
         has_governance: governance.has_content(),
         governance: governance.clone(),
+        macro_lens: macro_lens.cloned(),
     };
     let html = template
         .render()
@@ -3067,12 +3391,12 @@ mod tests {
     use super::*;
     use crate::domain::{
         BlastRadius, Checksum, ColumnMetaTags, ContractClass, ContractColumnDiff, CteEdge, CteNode,
-        DEFAULT_REPORT_TITLE, DependsOn, DiffLine, DiffLineKind, EdgeType, GovChip, Group,
-        GroupChip, Manifest, ManifestMetadata, MetaPair, ModelMetaTags, NodeConfig, NodeId, Owner,
-        UnitTest, UnitTestExpect, UnitTestGiven,
+        DEFAULT_REPORT_TITLE, DependsOn, DiffLine, DiffLineKind, EdgeType, FileHunks, GovChip,
+        Group, GroupChip, Hunk, Manifest, ManifestMetadata, MetaPair, ModelMetaTags, NodeConfig,
+        NodeId, Owner, PrDiff, UnitTest, UnitTestExpect, UnitTestGiven,
     };
     use serde_json::json;
-    use std::collections::{BTreeMap, HashMap};
+    use std::collections::{BTreeMap, BTreeSet, HashMap};
 
     // ===== parse_ref_name =====
 
@@ -6106,6 +6430,7 @@ mod tests {
             project_definition: None,
             project_change_panel: None,
             governance: GovernanceFacts::default(),
+            macro_lens: None,
         };
         let serialized = payload_json_for_html_script(&payload).unwrap();
         assert!(
@@ -6128,6 +6453,7 @@ mod tests {
             project_definition: None,
             project_change_panel: None,
             governance: GovernanceFacts::default(),
+            macro_lens: None,
         };
         let serialized = payload_json_for_html_script(&payload).unwrap();
         assert!(
@@ -6152,6 +6478,7 @@ mod tests {
             project_definition: None,
             project_change_panel: None,
             governance: GovernanceFacts::default(),
+            macro_lens: None,
         };
         let serialized = payload_json_for_html_script(&payload).unwrap();
         assert!(serialized.contains("a < b"), "bare `<` is preserved");
@@ -6171,6 +6498,7 @@ mod tests {
             project_definition: None,
             project_change_panel: None,
             governance: GovernanceFacts::default(),
+            macro_lens: None,
         };
         let serialized = payload_json_for_html_script(&payload).unwrap();
         assert!(
@@ -6203,6 +6531,7 @@ mod tests {
             project_definition: None,
             project_change_panel: None,
             governance: GovernanceFacts::default(),
+            macro_lens: None,
         };
         let serialized = payload_json_for_html_script(&payload).unwrap();
         assert!(
@@ -6233,6 +6562,7 @@ mod tests {
             project_definition: None,
             project_change_panel: None,
             governance: GovernanceFacts::default(),
+            macro_lens: None,
         };
         let serialized = payload_json_for_html_script(&original).unwrap();
         let parsed: serde_json::Value =
@@ -6286,6 +6616,7 @@ mod tests {
             &CheckPolicy::default(),
             &ProjectFacts::default(),
             governance,
+            None,
         )
         .expect("report renders");
         std::fs::read_to_string(&tmp).expect("read rendered report")
@@ -6746,6 +7077,338 @@ mod tests {
         assert!(!html.contains(r#"data-testid="gov-meta-chip""#));
     }
 
+    // ===== macro lens (cute-dbt#265, Slice B) =====
+
+    /// A root-project `model` node calling `direct_macros`, declaring file
+    /// `ofp`. Package = `shop` (the blast-radius root-project filter).
+    fn macro_model(id: &str, ofp: &str, direct_macros: &[&str]) -> Node {
+        Node::new(
+            NodeId::new(id),
+            "model",
+            Checksum::new("sha256", "abc"),
+            Some("select 1".to_owned()),
+            None,
+            DependsOn::new(
+                direct_macros.iter().map(|m| (*m).to_owned()).collect(),
+                vec![],
+            ),
+            Some(ofp.to_owned()),
+            NodeConfig::default(),
+            None,
+            BTreeMap::new(),
+        )
+        .with_identity(None, Some("shop".to_owned()))
+    }
+
+    /// A manifest with `shop` root project, the given macro-calling models,
+    /// and ONE macro (`macro.shop.add_dq_flags`, file `macros/dq.sql`).
+    fn macro_lens_manifest(models: Vec<Node>) -> Manifest {
+        let mut macros = HashMap::new();
+        macros.insert(
+            "macro.shop.add_dq_flags".to_owned(),
+            "{% macro add_dq_flags(col) %}\n  case when {{ col }} then 1 end\n{% endmacro %}"
+                .to_owned(),
+        );
+        let mut identity = BTreeMap::new();
+        identity.insert(
+            "macro.shop.add_dq_flags".to_owned(),
+            crate::domain::MacroIdentity::new(
+                Some("macros/dq.sql".to_owned()),
+                Some("add_dq_flags".to_owned()),
+                Some("shop".to_owned()),
+            ),
+        );
+        Manifest::new(
+            ManifestMetadata::new("v12").with_project_name(Some("shop".to_owned())),
+            models.into_iter().map(|n| (n.id().clone(), n)).collect(),
+            HashMap::new(),
+            macros,
+        )
+        .with_macro_identity(identity)
+    }
+
+    #[test]
+    fn build_macro_lens_empty_changed_set_is_none() {
+        // The off-gate / no-macro-changed contract: an empty set ⇒ None ⇒
+        // the section omits entirely (byte-identical non-macro golden).
+        let manifest = macro_lens_manifest(vec![macro_model(
+            "model.shop.orders",
+            "models/staging/orders.sql",
+            &["macro.shop.add_dq_flags"],
+        )]);
+        assert!(build_macro_lens(&manifest, &BTreeSet::new(), ScopeSource::PrDiff, None).is_none());
+    }
+
+    #[test]
+    fn build_macro_lens_baseline_arm_is_exact_fidelity() {
+        let manifest = macro_lens_manifest(vec![macro_model(
+            "model.shop.orders",
+            "models/staging/orders.sql",
+            &["macro.shop.add_dq_flags"],
+        )]);
+        let changed = BTreeSet::from(["macro.shop.add_dq_flags".to_owned()]);
+        let lens = build_macro_lens(&manifest, &changed, ScopeSource::Baseline, None)
+            .expect("a changed macro builds the lens");
+        assert_eq!(lens.fidelity, "exact");
+        assert_eq!(lens.macros.len(), 1);
+        let mac = &lens.macros[0];
+        assert_eq!(mac.name, "add_dq_flags");
+        assert_eq!(mac.package, "shop");
+        assert_eq!(mac.impacted_count, 1);
+        // Baseline arm: no diff index ⇒ plain body context lines.
+        assert!(mac.diff.is_none());
+        assert!(!mac.body_lines.is_empty());
+        assert!(
+            mac.body_lines
+                .iter()
+                .all(|l| l.kind == DiffLineKind::Context)
+        );
+    }
+
+    #[test]
+    fn build_macro_lens_pr_diff_arm_is_heuristic_with_body_diff() {
+        let manifest = macro_lens_manifest(vec![macro_model(
+            "model.shop.orders",
+            "models/staging/orders.sql",
+            &["macro.shop.add_dq_flags"],
+        )]);
+        // The diff touches the macro file's line 2; the `+` matches the
+        // working-tree body so the reconstruction splices the old line.
+        let diff = PrDiff {
+            renames: Vec::new(),
+            files: vec![FileHunks {
+                path: "macros/dq.sql".to_owned(),
+                hunks: vec![Hunk {
+                    new_start: 2,
+                    new_len: 1,
+                    removed_lines: vec!["  case when {{ col }} then 0 end".to_owned()],
+                    added_lines: vec!["  case when {{ col }} then 1 end".to_owned()],
+                }],
+            }],
+        };
+        let index = NormalizedDiffIndex::new(&diff, None);
+        let changed = BTreeSet::from(["macro.shop.add_dq_flags".to_owned()]);
+        let lens = build_macro_lens(&manifest, &changed, ScopeSource::PrDiff, Some(&index))
+            .expect("a changed macro builds the lens");
+        assert_eq!(lens.fidelity, "heuristic");
+        let mac = &lens.macros[0];
+        let body_diff = mac.diff.as_ref().expect("the touched macro body diffs");
+        assert!(body_diff.has_real_change());
+        assert!(
+            body_diff
+                .lines
+                .iter()
+                .any(|l| l.kind == DiffLineKind::Removed),
+            "the old macro line is spliced in",
+        );
+    }
+
+    #[test]
+    fn build_macro_lens_groups_impacted_models_into_a_directory_tree() {
+        // Two models in DIFFERENT directory subtrees both reach the macro:
+        // the tree must group each under its directory segments (founder D3).
+        let manifest = macro_lens_manifest(vec![
+            macro_model(
+                "model.shop.stg_orders",
+                "models/staging/stg_orders.sql",
+                &["macro.shop.add_dq_flags"],
+            ),
+            macro_model(
+                "model.shop.fct_orders",
+                "models/marts/core/fct_orders.sql",
+                &["macro.shop.add_dq_flags"],
+            ),
+        ]);
+        let changed = BTreeSet::from(["macro.shop.add_dq_flags".to_owned()]);
+        let lens = build_macro_lens(&manifest, &changed, ScopeSource::Baseline, None)
+            .expect("lens builds");
+        let mac = &lens.macros[0];
+        assert_eq!(mac.impacted_count, 2);
+        // Directory rows: models/, marts/, core/, staging/ — and 2 model
+        // leaves. Every model leaf carries its full node id.
+        let dirs: Vec<&str> = mac
+            .tree
+            .iter()
+            .filter(|r| r.kind == "dir")
+            .map(|r| r.label.as_str())
+            .collect();
+        assert!(
+            dirs.contains(&"models"),
+            "models/ dir row present: {dirs:?}"
+        );
+        assert!(dirs.contains(&"marts"), "marts/ dir row present: {dirs:?}");
+        assert!(
+            dirs.contains(&"staging"),
+            "staging/ dir row present: {dirs:?}"
+        );
+        let model_leaves: Vec<&str> = mac
+            .tree
+            .iter()
+            .filter(|r| r.kind == "model")
+            .map(|r| r.label.as_str())
+            .collect();
+        assert_eq!(model_leaves.len(), 2);
+        assert!(model_leaves.contains(&"fct_orders"));
+        assert!(model_leaves.contains(&"stg_orders"));
+        // A model leaf nests one level deeper than its deepest directory.
+        let fct_leaf = mac
+            .tree
+            .iter()
+            .find(|r| r.kind == "model" && r.label == "fct_orders")
+            .expect("fct leaf");
+        assert_eq!(fct_leaf.depth, 3, "models/marts/core/<model> → depth 3");
+        assert_eq!(fct_leaf.model_id, "model.shop.fct_orders");
+    }
+
+    #[test]
+    fn build_macro_lens_filters_out_vendor_package_macros() {
+        // A changed macro whose recorded package differs from the root
+        // project is dropped (vendor macros are not the reviewer's concern,
+        // even when the pr-diff path channel resolved one). With only a
+        // vendor macro in the set, the lens is None.
+        let manifest = macro_lens_manifest(vec![macro_model(
+            "model.shop.orders",
+            "models/staging/orders.sql",
+            &["macro.dbt_utils.helper"],
+        )])
+        .with_macro_identity({
+            let mut id = BTreeMap::new();
+            id.insert(
+                "macro.dbt_utils.helper".to_owned(),
+                crate::domain::MacroIdentity::new(
+                    Some("macros/u.sql".to_owned()),
+                    Some("helper".to_owned()),
+                    Some("dbt_utils".to_owned()),
+                ),
+            );
+            id
+        });
+        let changed = BTreeSet::from(["macro.dbt_utils.helper".to_owned()]);
+        assert!(
+            build_macro_lens(&manifest, &changed, ScopeSource::PrDiff, None).is_none(),
+            "a vendor-only changed set yields no lens",
+        );
+    }
+
+    #[test]
+    fn build_macro_lens_empty_blast_radius_yields_an_empty_tree() {
+        // A macro no model calls (e.g. a materialization macro) ⇒ count 0 +
+        // empty tree (the template states the honest zero, never a false
+        // claim).
+        let manifest = macro_lens_manifest(vec![macro_model(
+            "model.shop.orders",
+            "models/staging/orders.sql",
+            &["macro.shop.unrelated"],
+        )]);
+        let changed = BTreeSet::from(["macro.shop.add_dq_flags".to_owned()]);
+        let lens = build_macro_lens(&manifest, &changed, ScopeSource::Baseline, None)
+            .expect("lens builds");
+        assert_eq!(lens.macros[0].impacted_count, 0);
+        assert!(lens.macros[0].tree.is_empty());
+    }
+
+    #[test]
+    fn macro_lens_none_contributes_zero_payload_bytes() {
+        // The byte-identity invariant: serializing a payload with
+        // macro_lens == None must add ZERO bytes vs the same payload — the
+        // `skip_serializing_if = Option::is_none` contract that keeps
+        // non-macro goldens byte-identical.
+        let manifest = macro_lens_manifest(vec![macro_model(
+            "model.shop.orders",
+            "models/staging/orders.sql",
+            &["macro.shop.add_dq_flags"],
+        )]);
+        let models = ModelInScopeSet::from_iter([NodeId::new("model.shop.orders")]);
+        let mut payload = build_payload(
+            &manifest,
+            &InScopeSet::new(),
+            &models,
+            &HashMap::new(),
+            &HashMap::new(),
+            &HashMap::new(),
+            &HashMap::new(),
+            &HashMap::new(),
+            "",
+        );
+        payload.macro_lens = None;
+        let without = serde_json::to_string(&payload).expect("serialize");
+        assert!(
+            !without.contains("macro_lens"),
+            "macro_lens == None must not appear in the JSON: {without}",
+        );
+    }
+
+    /// Render a one-model PR-diff report carrying `macro_lens`, returning the
+    /// HTML — the macro-section render-integration harness.
+    fn render_html_with_macro_lens(
+        filename: &str,
+        macro_lens: Option<&MacroLensPayload>,
+    ) -> String {
+        let node = macro_model(
+            "model.shop.orders",
+            "models/staging/orders.sql",
+            &["macro.shop.add_dq_flags"],
+        );
+        let manifest = macro_lens_manifest(vec![node]);
+        let models = ModelInScopeSet::from_iter([NodeId::new("model.shop.orders")]);
+        let tmp = std::env::temp_dir().join(filename);
+        let _ = std::fs::remove_file(&tmp);
+        render_report_with_externals(
+            &tmp,
+            &manifest,
+            &InScopeSet::new(),
+            &models,
+            &InScopeSet::new(),
+            &HashMap::new(),
+            &HashMap::new(),
+            &HashMap::new(),
+            &HashMap::new(),
+            &HashMap::new(),
+            &HashMap::new(),
+            "",
+            ScopeSource::PrDiff,
+            "t",
+            None,
+            &CheckPolicy::default(),
+            &ProjectFacts::default(),
+            &GovernanceFacts::default(),
+            macro_lens,
+        )
+        .expect("report renders");
+        std::fs::read_to_string(&tmp).expect("read rendered report")
+    }
+
+    #[test]
+    fn macro_lens_off_emits_zero_macro_dom() {
+        // The off-gate render: macro_lens == None must add NO macro section
+        // markup — the byte-identity-with-non-macro-goldens contract.
+        let html = render_html_with_macro_lens("macro_off.html", None);
+        assert!(!html.contains(r#"data-testid="macro-lens-panel""#));
+        assert!(!html.contains("Macro changed"));
+    }
+
+    #[test]
+    fn macro_lens_on_renders_the_section_with_tree_and_count() {
+        let manifest = macro_lens_manifest(vec![macro_model(
+            "model.shop.orders",
+            "models/staging/orders.sql",
+            &["macro.shop.add_dq_flags"],
+        )]);
+        let changed = BTreeSet::from(["macro.shop.add_dq_flags".to_owned()]);
+        let lens =
+            build_macro_lens(&manifest, &changed, ScopeSource::PrDiff, None).expect("lens builds");
+        let html = render_html_with_macro_lens("macro_on.html", Some(&lens));
+        assert!(html.contains(r#"data-testid="macro-lens-panel""#));
+        assert!(html.contains("Macro changed"));
+        assert!(html.contains(r#"data-testid="macro-lens-experimental""#));
+        assert!(html.contains(r#"data-testid="macro-lens-tree""#));
+        assert!(html.contains(r#"data-testid="macro-lens-count""#));
+        // Honest naming (critique S2): never a `state:` selector name.
+        assert!(!html.contains("state:modified.macros"));
+        // The macro section is ABOVE the (absent) governance region — it
+        // simply renders; the placement is pinned by the template order.
+    }
+
     // ===== project panel: hooks + dispatch rows (cute-dbt#269) =====
 
     /// Render a one-model PR-diff report carrying `facts`, returning the
@@ -6775,6 +7438,7 @@ mod tests {
             &CheckPolicy::default(),
             facts,
             &GovernanceFacts::default(),
+            None,
         )
         .expect("report renders");
         std::fs::read_to_string(&tmp).expect("read rendered report")
diff --git a/src/cli/mod.rs b/src/cli/mod.rs
index 40c96371..80d9d3b3 100644
--- a/src/cli/mod.rs
+++ b/src/cli/mod.rs
@@ -58,8 +58,8 @@ use crate::adapters::manifest::{FileManifestSource, load_baseline};
 use crate::adapters::project_def::parse as parse_project_definition;
 use crate::adapters::project_file::FsProjectFileReader;
 use crate::adapters::render::{
-    ExternalFixtures, LoadedFixture, ScopeSource, build_payload, index_tests_for_models,
-    render_report_with_externals,
+    ExternalFixtures, LoadedFixture, MacroLensPayload, ScopeSource, build_macro_lens,
+    build_payload, index_tests_for_models, render_report_with_externals,
 };
 use crate::domain::{
     BlockDiff, CheckPolicy, ConfigAttribution, DEFAULT_REPORT_TITLE, DepDate, EnabledExperiments,
@@ -68,13 +68,14 @@ use crate::domain::{
     ProjectChangePanel, ProjectFacts, ProjectFallbackReason, ScopeInput, ScopeSelection,
     SuppressRule, SuppressionSource, UnitTest, UnitTestDataDiff, UnitTestYamlBlock, VarReference,
     all_models, attach_hook_facts, attach_model_yaml_diffs, attach_var_facts,
-    attribute_config_tree_changes, attribute_var_changes, changed_models, check_by_id,
-    diff_project_definitions, effective_fixture_format, external_fixture_table,
-    extract_model_block, extract_unit_test_block, gather_governance, hook_operations,
-    preflight_compiled, raw_hunk_lines, reconstruct_block_diffs, reconstruct_external_fixture_diff,
-    reconstruct_model_sql_diffs, reconstruct_table_diffs, refine_changed_by_hunks,
-    resolve_check_policy, resolve_experimental_config, reverse_apply, scan_pragmas,
-    select_in_scope, widen_with_config_attributions,
+    attribute_config_tree_changes, attribute_var_changes, changed_macros_baseline,
+    changed_macros_pr_diff, changed_models, check_by_id, diff_project_definitions,
+    effective_fixture_format, external_fixture_table, extract_model_block, extract_unit_test_block,
+    gather_governance, hook_operations, preflight_compiled, raw_hunk_lines,
+    reconstruct_block_diffs, reconstruct_external_fixture_diff, reconstruct_model_sql_diffs,
+    reconstruct_table_diffs, refine_changed_by_hunks, resolve_check_policy,
+    resolve_experimental_config, reverse_apply, scan_pragmas, select_in_scope,
+    widen_with_config_attributions,
 };
 use crate::ports::{ManifestSource, ProjectFileReader};
 
@@ -189,6 +190,12 @@ impl RunError {
 /// renders and nothing widens. `?` short-circuits before
 /// `render`, so a fail-closed manifest never produces a partial
 /// `report.html`.
+// `too_many_lines`: the report run loop is a linear composition root —
+// each gather slice (project-state, governance, macro-lens, the inline-diff
+// reconstructions) is one named, already-factored step threaded in sequence.
+// Splitting the loop into sub-loops would add indirection that buys nothing
+// at this single composition site (the `render_report` rationale, cli edition).
+#[allow(clippy::too_many_lines)]
 fn execute_report(args: &ReportArgs) -> Result<(), RunError> {
     let current = load_current(args)?;
     let scope_input = resolve_scope_input(args)?;
@@ -279,6 +286,9 @@ fn execute_report(args: &ReportArgs) -> Result<(), RunError> {
     } else {
         GovernanceFacts::default()
     };
+    // Macro perspective lens (cute-dbt#265, Slice B) — see `gather_macro_lens`.
+    let macro_lens = gather_macro_lens(&current, &scope_input, &experiments);
+
     // Stage-2 fail-closed reads the TRUE in-scope set (cute-dbt#91): the
     // widened render set is only for what the report displays. Config-tree
     // widened tests (cute-dbt#267) ARE the true scope, so they preflight.
@@ -401,6 +411,7 @@ fn execute_report(args: &ReportArgs) -> Result<(), RunError> {
         &check_policy,
         &project_facts,
         &governance_facts,
+        macro_lens.as_ref(),
     )
     .map_err(|err| RunError::output(&args.out, err))?;
     Ok(())
@@ -1328,6 +1339,45 @@ fn resolve_scope_input(args: &ReportArgs) -> Result<ScopeInput, RunError> {
     }
 }
 
+/// Gather the macro perspective lens (cute-dbt#265, Slice B), gated behind
+/// the `macro-lens` experiment (the epic #288 default-OFF posture).
+///
+/// Enabled: resolve the changed root-project macros by scope arm — the
+/// `PrDiff` arm runs the path-primary + name-fallback heuristic over the
+/// diff index, the `Baseline` arm runs the exact `macro_sql` body
+/// comparison (fusion's `check_modified_macros` semantics) — then build the
+/// section
+/// (per-macro body diff + reverse blast-radius directory tree + count +
+/// fidelity chip). Default (off): `None` ⇒ omitted from the JSON payload
+/// and zero DOM via `{% if macro_lens %}`, so the non-macro goldens stay
+/// byte-identical. A pure pass over the already-parsed manifest + the diff
+/// index (no file read, zero-egress); the blast radius never widens scope
+/// (it is a render-only contextualizer, the governance posture). The
+/// diff-showcase golden row sets `experimental:"1"`, so a changed
+/// playground macro surfaces here.
+fn gather_macro_lens(
+    current: &Manifest,
+    scope_input: &ScopeInput,
+    experiments: &EnabledExperiments,
+) -> Option<MacroLensPayload> {
+    if !experiments.is_enabled(Experiment::MacroLens) {
+        return None;
+    }
+    let (changed_macros, index, source) = match scope_input {
+        ScopeInput::PrDiff { index } => (
+            changed_macros_pr_diff(current, index),
+            Some(index),
+            ScopeSource::PrDiff,
+        ),
+        ScopeInput::Baseline { manifest, .. } => (
+            changed_macros_baseline(current, manifest),
+            None,
+            ScopeSource::Baseline,
+        ),
+    };
+    build_macro_lens(current, &changed_macros, source, index)
+}
+
 /// The diff-scope banner inputs for the selected scope source.
 ///
 /// Returns `(baseline_label, scope_source)`. The baseline arm carries the
@@ -1407,6 +1457,7 @@ fn render(
     check_policy: &CheckPolicy<HeuristicId>,
     project_facts: &ProjectFacts,
     governance: &GovernanceFacts,
+    macro_lens: Option<&MacroLensPayload>,
 ) -> Result<(), io::Error> {
     render_report_with_externals(
         out,
@@ -1427,6 +1478,7 @@ fn render(
         check_policy,
         project_facts,
         governance,
+        macro_lens,
     )
 }
 
diff --git a/src/domain/experimental.rs b/src/domain/experimental.rs
index b8ae534d..7933c4e8 100644
--- a/src/domain/experimental.rs
+++ b/src/domain/experimental.rs
@@ -51,12 +51,25 @@ pub enum Experiment {
     /// lifecycle chips. Every surface is render over already-parsed wire
     /// data, gated empty so the released golden never moves.
     Governance,
+    /// The macro perspective lens (cute-dbt#265, epic #265): the
+    /// "macro changed" section — the changed macro's body diff, the
+    /// collapsible directory tree of the impacted root-project models (the
+    /// reverse [`macro_blast_radius`](crate::domain::macro_blast_radius)),
+    /// the impacted-model count, and a per-arm fidelity chip (baseline =
+    /// exact macro-body comparison; pr-diff = path/name heuristic). Render
+    /// over already-parsed wire data + the PR diff index; gated empty so
+    /// the non-macro goldens stay byte-identical.
+    MacroLens,
 }
 
 impl Experiment {
     /// Every registered experiment, in declaration order — the closed
     /// vocabulary both opt-in surfaces validate against.
-    pub const ALL: &'static [Experiment] = &[Experiment::ProjectState, Experiment::Governance];
+    pub const ALL: &'static [Experiment] = &[
+        Experiment::ProjectState,
+        Experiment::Governance,
+        Experiment::MacroLens,
+    ];
 
     /// The kebab-case wire id this experiment is named by in the
     /// `[experimental]` TOML list and the `CUTE_DBT_EXPERIMENTAL` env
@@ -66,6 +79,7 @@ impl Experiment {
         match self {
             Self::ProjectState => "project-state",
             Self::Governance => "governance",
+            Self::MacroLens => "macro-lens",
         }
     }
 
@@ -284,6 +298,19 @@ mod tests {
         assert!(Experiment::ALL.contains(&Experiment::Governance));
     }
 
+    #[test]
+    fn macro_lens_is_a_registered_experiment_with_its_wire_id() {
+        // cute-dbt#265 Slice B: the gating seam for the macro perspective
+        // section. The id round-trips and the variant is in the closed
+        // vocabulary (so `1`/`all` enables it on the diff-showcase row).
+        assert_eq!(Experiment::MacroLens.id(), "macro-lens");
+        assert_eq!(
+            Experiment::from_id("macro-lens"),
+            Some(Experiment::MacroLens)
+        );
+        assert!(Experiment::ALL.contains(&Experiment::MacroLens));
+    }
+
     // ----- [experimental] TOML resolution -----
 
     #[test]
diff --git a/src/domain/mod.rs b/src/domain/mod.rs
index 146b5624..8ec9d879 100644
--- a/src/domain/mod.rs
+++ b/src/domain/mod.rs
@@ -99,8 +99,8 @@ pub use path::{match_changed_path, normalize_path};
 pub use pr_diff::{
     BlockDiff, DiffLine, DiffLineKind, FileHunks, Hunk, NormalizedDiffIndex, PrDiff,
     ReverseApplyError, attach_model_yaml_diffs, diff_lines, raw_hunk_lines,
-    reconstruct_block_diffs, reconstruct_model_sql_diffs, refine_changed_by_hunks, reverse_apply,
-    ws_equal,
+    reconstruct_block_diffs, reconstruct_macro_sql_diff, reconstruct_model_sql_diffs,
+    refine_changed_by_hunks, reverse_apply, ws_equal,
 };
 pub use preflight::{PreflightError, preflight_compiled};
 // `project_def::Span` is deliberately NOT re-exported here — `cte::Span`
diff --git a/src/domain/pr_diff.rs b/src/domain/pr_diff.rs
index 72cbede5..2887316d 100644
--- a/src/domain/pr_diff.rs
+++ b/src/domain/pr_diff.rs
@@ -706,6 +706,70 @@ fn model_sql_diff(model: &Node, index: &NormalizedDiffIndex) -> Option<BlockDiff
     diff.has_real_change().then_some(diff)
 }
 
+/// Reconstruct an inline diff of a changed macro's `macro_sql` body
+/// (cute-dbt#265 macro perspective, Slice B) — the macro sibling of the
+/// internal model-SQL diff (`model_sql_diff`).
+///
+/// A macro is not a [`Node`], so this takes the body string and its
+/// declaring path directly (from [`crate::domain::Manifest::macros`] +
+/// [`crate::domain::MacroIdentity::original_file_path`]) rather than a
+/// node. Everything else is the [`reconstruct_model_sql_diffs`] per-model
+/// contract verbatim:
+///
+/// 1. Normalize the body to git's line frame — strip **exactly one**
+///    trailing `\n` ([`str::strip_suffix`]). dbt engines diverge on the
+///    trailing terminator the same way they do for model `raw_code`
+///    (dbt-core strips it, dbt-fusion retains it); stripping one
+///    normalizes both to the same content frame so `body.split('\n')`
+///    lines up with the diff's new-side numbering.
+/// 2. Take the whole-file span `(body, 1, body.split('\n').count())` and
+///    the hunks touching `original_file_path`
+///    ([`NormalizedDiffIndex::hunks_for`]).
+/// 3. Emit a [`BlockDiff`] **only** when the diff still aligns
+///    ([`block_aligns_with_hunks`]), at least one hunk touches the file
+///    ([`hunk_touches_block`]), and the reconstruction carries a
+///    substantive change ([`BlockDiff::has_real_change`] — a
+///    whitespace-only edit emits nothing, cute-dbt#111). Otherwise `None`,
+///    so the macro section shows the plain current body.
+///
+/// Runs only on the [`crate::domain::ScopeInput::PrDiff`] arm — baseline
+/// mode has no hunks to reconstruct from (the macro lens carries no diff
+/// there; the fidelity chip names the baseline arm "exact" by macro-body
+/// comparison, not a rendered hunk diff). An empty body, or a macro with
+/// no `original_file_path` (the rare fusion null-fill the spike named),
+/// yields `None`.
+#[must_use]
+pub fn reconstruct_macro_sql_diff(
+    macro_body: &str,
+    original_file_path: &str,
+    index: &NormalizedDiffIndex,
+) -> Option<BlockDiff> {
+    if macro_body.is_empty() {
+        return None;
+    }
+    // Strip git's single trailing terminator (engine-divergent — the same
+    // normalization `model_sql_diff` applies). A real blank line at EOF
+    // survives.
+    let body = macro_body.strip_suffix('\n').unwrap_or(macro_body);
+    // Whole-file span: 1..=line count.
+    let span = BlockSpan::new(body, 1, body.split('\n').count());
+
+    let hunks = index.hunks_for(original_file_path);
+    if !block_aligns_with_hunks(&span, hunks) {
+        return None; // stale diff (N7b) → plain macro body
+    }
+    let touching: Vec<&Hunk> = hunks
+        .iter()
+        .filter(|h| hunk_touches_block(span.start, span.end, h))
+        .collect();
+    if touching.is_empty() {
+        return None; // the macro file is not touched by this diff
+    }
+    let diff = reconstruct_one(&span, &touching);
+    // Whitespace-only macro edit → all-Context → no diff (plain body).
+    diff.has_real_change().then_some(diff)
+}
+
 /// Attach an inline diff to each gathered Model-YAML block the PR diff
 /// genuinely edited (cute-dbt#247).
 ///
@@ -3427,6 +3491,123 @@ mod tests {
         assert!(reconstruct_model_sql_diffs(&current, &scope, &index).is_empty());
     }
 
+    // ----- reconstruct_macro_sql_diff (cute-dbt#265 macro lens) ---------
+
+    #[test]
+    fn reconstruct_macro_sql_diff_renders_a_touched_macro_body_edit() {
+        // The macro file's line 2 changed; the `+` matches the working-tree
+        // body, so the reconstruction splices the removed line in place.
+        let ofp = "macros/dq.sql";
+        let body = "{% macro dq() %}\n  where flag is true\n{% endmacro %}";
+        let diff = PrDiff {
+            renames: Vec::new(),
+            files: vec![FileHunks {
+                path: ofp.to_owned(),
+                hunks: vec![replace(
+                    2,
+                    &["  where flag = true"],
+                    &["  where flag is true"],
+                )],
+            }],
+        };
+        let index = NormalizedDiffIndex::new(&diff, None);
+        let reconstructed = reconstruct_macro_sql_diff(body, ofp, &index)
+            .expect("a touched macro body edit reconstructs");
+        assert!(reconstructed.has_real_change());
+        assert!(
+            reconstructed
+                .lines
+                .iter()
+                .any(|l| l.kind == DiffLineKind::Removed && l.text == "  where flag = true"),
+            "the old line is spliced in",
+        );
+    }
+
+    #[test]
+    fn reconstruct_macro_sql_diff_engine_trailing_newline_frame_is_identical() {
+        // dbt-core ships `macro_sql` stripped; dbt-fusion retains the file's
+        // trailing `\n`. Stripping exactly one terminator normalizes both to
+        // the SAME DiffLine frame (the model_sql_diff cross-engine guard).
+        let ofp = "macros/m.sql";
+        let core_body = "{% macro m() %}\nfrom u\n{% endmacro %}";
+        let fusion_body = "{% macro m() %}\nfrom u\n{% endmacro %}\n";
+        let diff = PrDiff {
+            renames: Vec::new(),
+            files: vec![FileHunks {
+                path: ofp.to_owned(),
+                hunks: vec![replace(2, &["from t"], &["from u"])],
+            }],
+        };
+        let index = NormalizedDiffIndex::new(&diff, None);
+        let core = reconstruct_macro_sql_diff(core_body, ofp, &index).expect("core reconstructs");
+        let fusion =
+            reconstruct_macro_sql_diff(fusion_body, ofp, &index).expect("fusion reconstructs");
+        assert_eq!(
+            core.lines, fusion.lines,
+            "the trailing-newline divergence must not move the frame",
+        );
+    }
+
+    #[test]
+    fn reconstruct_macro_sql_diff_skips_an_untouched_file() {
+        let body = "{% macro m() %}\nx\n{% endmacro %}";
+        let diff = PrDiff {
+            renames: Vec::new(),
+            files: vec![FileHunks {
+                path: "macros/OTHER.sql".to_owned(),
+                hunks: vec![replace(2, &["old"], &["new"])],
+            }],
+        };
+        let index = NormalizedDiffIndex::new(&diff, None);
+        assert!(reconstruct_macro_sql_diff(body, "macros/m.sql", &index).is_none());
+    }
+
+    #[test]
+    fn reconstruct_macro_sql_diff_skips_a_stale_diff() {
+        // The `+` body does not match the working-tree body → N7b drift →
+        // None (the section falls back to the plain current body).
+        let ofp = "macros/m.sql";
+        let body = "{% macro m() %}\nfrom u\n{% endmacro %}";
+        let diff = PrDiff {
+            renames: Vec::new(),
+            files: vec![FileHunks {
+                path: ofp.to_owned(),
+                hunks: vec![replace(2, &["from was"], &["from DRIFTED"])],
+            }],
+        };
+        let index = NormalizedDiffIndex::new(&diff, None);
+        assert!(reconstruct_macro_sql_diff(body, ofp, &index).is_none());
+    }
+
+    #[test]
+    fn reconstruct_macro_sql_diff_skips_a_whitespace_only_edit() {
+        // A re-indent of an otherwise-equal line → all-Context → None.
+        let ofp = "macros/m.sql";
+        let body = "{% macro m() %}\n    from u\n{% endmacro %}";
+        let diff = PrDiff {
+            renames: Vec::new(),
+            files: vec![FileHunks {
+                path: ofp.to_owned(),
+                hunks: vec![replace(2, &["from u"], &["    from u"])],
+            }],
+        };
+        let index = NormalizedDiffIndex::new(&diff, None);
+        assert!(reconstruct_macro_sql_diff(body, ofp, &index).is_none());
+    }
+
+    #[test]
+    fn reconstruct_macro_sql_diff_empty_body_is_none() {
+        let diff = PrDiff {
+            renames: Vec::new(),
+            files: vec![FileHunks {
+                path: "macros/m.sql".to_owned(),
+                hunks: vec![replace(1, &["old"], &["new"])],
+            }],
+        };
+        let index = NormalizedDiffIndex::new(&diff, None);
+        assert!(reconstruct_macro_sql_diff("", "macros/m.sql", &index).is_none());
+    }
+
     // ----- Non-`--unified=0` (context-bearing) hunk safety -----
     //
     // A hunk's `new_len` (from the `@@` header range) is independent of
diff --git a/templates/report.html b/templates/report.html
index e09ae74b..62ea924c 100644
--- a/templates/report.html
+++ b/templates/report.html
@@ -321,6 +321,54 @@ <h2>Project definition changed</h2>
 {%- endif %}
 </section>
 {% when None %}{% endmatch -%}
+{#- cute-dbt#265 (Slice B) — the macro perspective lens. Server-rendered
+   (no JS dependency), positioned ABOVE the governance chips (the founder's
+   "near the top"). Gated behind the `macro-lens` experiment: when off the
+   cli passes `macro_lens = None`, so this `{% match %}` emits ZERO bytes
+   (every surrounding tag trims both sides), keeping the non-macro goldens
+   byte-identical — the project_panel / governance precedents. Marked
+   EXPERIMENTAL (founder D7). The copy says "macro changed", NEVER a
+   `state:modified.macros` selector name (critique S2). Impacted models are
+   a collapsible DIRECTORY TREE (founder D3) — count + names only, NO inline
+   model bodies (Slice D), NO focused DAG (explore-only, #345), NO
+   report→explore hyperlink (it would break the one-file zero-egress
+   contract). Intentionally unstyled beyond the chassis hooks — Claude
+   Design owns the visual pass; the data-testid / data-* hooks are the
+   stable selectors the headless guards key off. -#}
+{% match macro_lens %}{% when Some with (lens) %}<section class="macro-lens-panel" aria-label="Macro changes" data-testid="macro-lens-panel">
+  <h2>Macro changed <span class="macro-lens-experimental" data-testid="macro-lens-experimental">experimental</span></h2>
+  <p class="macro-lens-subtitle">A changed macro is invisible to model and unit-test scope selection &mdash; this section shows the macros this PR changed and the root-project models they reach. <span class="macro-lens-fidelity" data-testid="macro-lens-fidelity" data-fidelity="{{ lens.fidelity }}">fidelity: {{ lens.fidelity }}</span></p>
+{%- for mac in lens.macros %}
+  <div class="macro-lens-macro" data-testid="macro-lens-macro" data-macro="{{ mac.name }}">
+    <p class="macro-lens-macro-head"><code class="macro-lens-name">{{ mac.name }}</code> <span class="macro-lens-package" data-testid="macro-lens-package">{{ mac.package }}</span>{% if !mac.path.is_empty() %} <code class="macro-lens-path">{{ mac.path }}</code>{% endif %}</p>
+    {#- The body diff (pr-diff arm, touched + aligned + substantive) or the
+        plain current body as context lines (baseline arm / untouched). The
+        DiffLine kind drives the per-line CSS hook, mirroring the project-def
+        raw-diff fallback. -#}
+{%- match mac.diff %}{% when Some with (d) %}
+    <pre class="macro-lens-diff" data-testid="macro-lens-diff">{% for line in d.lines %}<span class="macro-lens-line is-{% match line.kind %}{% when DiffLineKind::Context %}context{% when DiffLineKind::Removed %}removed{% when DiffLineKind::Added %}added{% endmatch %}">{{ line.text }}</span>
+{% endfor %}</pre>
+{%- when None %}
+    <pre class="macro-lens-body" data-testid="macro-lens-body">{% for line in mac.body_lines %}<span class="macro-lens-line is-context">{{ line.text }}</span>
+{% endfor %}</pre>
+{%- endmatch %}
+    <p class="macro-lens-count" data-testid="macro-lens-count" data-count="{{ mac.impacted_count }}">Impacts {{ mac.impacted_count }} root-project {% if mac.impacted_count == 1 %}model{% else %}models{% endif %}.</p>
+{%- if !mac.tree.is_empty() %}
+    <details class="macro-lens-tree-details" data-testid="macro-lens-tree-details" open>
+      <summary>impacted models</summary>
+      <ul class="macro-lens-tree" data-testid="macro-lens-tree">
+{%- for row in mac.tree %}
+        <li class="macro-lens-tree-row macro-lens-tree-{{ row.kind }}" data-testid="macro-lens-tree-row" data-kind="{{ row.kind }}" data-depth="{{ row.depth }}"{% if !row.model_id.is_empty() %} data-model="{{ row.model_id }}"{% endif %} style="--macro-tree-depth: {{ row.depth }}">{% if row.kind == "dir" %}<span class="macro-lens-tree-dir">{{ row.label }}/</span>{% else %}<code class="macro-lens-tree-model">{{ row.label }}</code>{% endif %}</li>
+{%- endfor %}
+      </ul>
+    </details>
+{%- else %}
+    <p class="macro-lens-empty" data-testid="macro-lens-empty">No root-project models reach this macro &mdash; a materialization macro, or a macro this PR added/removed rather than edited in place.</p>
+{%- endif %}
+  </div>
+{%- endfor %}
+</section>
+{% when None %}{% endmatch -%}
 {#- cute-dbt#260 (Slice 0 — the walking skeleton) — the governance
    surfaces section. Gated behind the `governance` experiment: when the
    experiment is off the cli passes an empty GovernanceFacts, so
diff --git a/tests/fixtures/MANIFEST.toml b/tests/fixtures/MANIFEST.toml
index bed1f07f..a4d0c3c1 100644
--- a/tests/fixtures/MANIFEST.toml
+++ b/tests/fixtures/MANIFEST.toml
@@ -479,7 +479,7 @@ is unchanged.\
 path = "playground-pr-diff.patch"
 origin = "synthetic-handcrafted"
 origin_url = "https://github.com/breezy-bays-labs/cute-dbt/issues/134"
-sha256 = "0dd17d61b9ac656f262b37580fbb87b51c3684487d32291e1b2004c34ba5079b"
+sha256 = "65489edf84ab4ebdf66a0bfdcdade1a9b86c79c17565fa4c1d8c4620ddf887aa"
 synthetic_only = true
 synthetic_handcrafted = true
 license = "MIT"
@@ -512,7 +512,13 @@ playground-source/dbt_project.yml (same-revision contract). \
 cute-dbt#269 appended two more dbt_project.yml hunks (an on-run-start \
 hook rewrite + a dispatch search_order reorder) so the showcase \
 renders the purpose-built hooks row (operation-node SQL diff) and the \
-UNKNOWN-tier dispatch banner. No \
+UNKNOWN-tier dispatch banner. cute-dbt#265 prepended a macro edit hunk \
+(`macros/data_quality/quarantine_filter.sql` line 3, \
+`is true`→`= true`) so the showcase renders the macro perspective lens \
+(Slice B): the macro body diff + the reverse blast-radius directory \
+tree of the 3 root-project models that reach the macro; its `+` side is \
+byte-aligned to the committed playground-source macro file \
+(same-revision contract). No \
 real data — a hypothetical PR against the synthetic Synthea playground.\
 """
 
@@ -677,6 +683,28 @@ the ProjectFileReader under `--project-root tests/fixtures/playground-source` \
 Pure synthetic data — fictitious patient ids, no real records.\
 """
 
+[[fixture]]
+path = "playground-source/macros/data_quality/quarantine_filter.sql"
+origin = "synthetic-handcrafted"
+origin_url = "https://github.com/breezy-bays-labs/cute-dbt/issues/265"
+sha256 = "c2908efeda52706648392679973fb2525d9ac65150547ae4d809e1354aa7f8bb"
+synthetic_only = true
+synthetic_handcrafted = true
+license = "MIT"
+description = """
+Synthetic root-project macro for the cute-dbt#265 macro perspective lens \
+(Slice B). Its body is byte-aligned to the committed \
+`macro.healthcare_analytics.quarantine_filter` `macro_sql` in \
+playground-current.json (the same-revision contract), so reading it through \
+`--project-root tests/fixtures/playground-source` matches the manifest. The \
+diff-showcase pr-diff patch carries an EDIT hunk against this file (a \
+`where {{ field_name }} is true`→`= true` change), so the golden \
+diff-showcase renders the macro body diff plus the reverse blast-radius \
+directory tree of the 3 root-project models that reach this macro \
+(`fct_clinical_events`, `fct_encounters`, `int_encounters__enriched`). \
+Pure synthetic Jinja — no real data.\
+"""
+
 [[fixture]]
 path = "source-yaml/manifest-current.json"
 origin = "synthetic-handcrafted"
diff --git a/tests/fixtures/playground-pr-diff.patch b/tests/fixtures/playground-pr-diff.patch
index 6772f268..7558948d 100644
--- a/tests/fixtures/playground-pr-diff.patch
+++ b/tests/fixtures/playground-pr-diff.patch
@@ -1,3 +1,9 @@
+diff --git a/macros/data_quality/quarantine_filter.sql b/macros/data_quality/quarantine_filter.sql
+--- a/macros/data_quality/quarantine_filter.sql
++++ b/macros/data_quality/quarantine_filter.sql
+@@ -3 +3 @@
+-    where {{ field_name }} is true
++    where {{ field_name }} = true
 diff --git a/models/marts/analytics/fct_provider_metrics.sql b/models/marts/analytics/fct_provider_metrics.sql
 --- a/models/marts/analytics/fct_provider_metrics.sql
 +++ b/models/marts/analytics/fct_provider_metrics.sql
diff --git a/tests/fixtures/playground-source/macros/data_quality/quarantine_filter.sql b/tests/fixtures/playground-source/macros/data_quality/quarantine_filter.sql
new file mode 100644
index 00000000..e9ca9e21
--- /dev/null
+++ b/tests/fixtures/playground-source/macros/data_quality/quarantine_filter.sql
@@ -0,0 +1,5 @@
+{% macro quarantine_filter(enabled=true, field_name='is_dq_valid') %}
+    {% if enabled %}
+    where {{ field_name }} = true
+    {% endif %}
+{% endmacro %}
diff --git a/tests/headless_toggle.rs b/tests/headless_toggle.rs
index 4f393824..04f15f44 100644
--- a/tests/headless_toggle.rs
+++ b/tests/headless_toggle.rs
@@ -43,15 +43,16 @@ use headless_chrome::{Browser, LaunchOptionsBuilder, Tab};
 
 use cute_dbt::adapters::manifest::FileManifestSource;
 use cute_dbt::adapters::render::{
-    ExternalFixtures, LoadedFixture, ScopeSource, render_report, render_report_with_externals,
+    ExternalFixtures, LoadedFixture, MacroLensPayload, ScopeSource, build_macro_lens,
+    render_report, render_report_with_externals,
 };
 use cute_dbt::domain::{
     BlockDiff, Checksum, DEFAULT_REPORT_TITLE, DependsOn, DiffLine, DiffLineKind, FileHunks,
-    HookChangeFacts, HookManifestPresence, Hunk, InScopeSet, Manifest, ManifestMetadata,
-    ModelInScopeSet, Node, NodeConfig, NodeId, NormalizedDiffIndex, PrDiff, ProjectChange,
-    ProjectChangeCategory, ProjectChangePanel, ProjectFacts, ProjectFallbackReason, SourceNode,
-    TestMetadata, UnitTest, UnitTestDataDiff, UnitTestExpect, UnitTestGiven, UnitTestYamlBlock,
-    external_fixture_table, raw_hunk_lines, reconstruct_table_diffs,
+    HookChangeFacts, HookManifestPresence, Hunk, InScopeSet, MacroIdentity, Manifest,
+    ManifestMetadata, ModelInScopeSet, Node, NodeConfig, NodeId, NormalizedDiffIndex, PrDiff,
+    ProjectChange, ProjectChangeCategory, ProjectChangePanel, ProjectFacts, ProjectFallbackReason,
+    SourceNode, TestMetadata, UnitTest, UnitTestDataDiff, UnitTestExpect, UnitTestGiven,
+    UnitTestYamlBlock, external_fixture_table, raw_hunk_lines, reconstruct_table_diffs,
 };
 use cute_dbt::ports::ManifestSource;
 
@@ -4782,6 +4783,7 @@ fn render_with_external_fixtures(
         &cute_dbt::domain::CheckPolicy::default(),
         &cute_dbt::domain::ProjectFacts::default(),
         &cute_dbt::domain::GovernanceFacts::default(),
+        None,
     )
     .expect("render writes the report");
     let p = out.to_str().expect("report path is valid UTF-8");
@@ -7396,6 +7398,7 @@ fn suppressed_findings_render_as_a_collapsed_count_with_reasons() {
         &policy,
         &cute_dbt::domain::ProjectFacts::default(),
         &cute_dbt::domain::GovernanceFacts::default(),
+        None,
     )
     .expect("render writes the report");
     let url = format!("file://{}", out.to_str().expect("UTF-8 path"));
@@ -7966,6 +7969,7 @@ fn suppressed_row_text_meets_aa_contrast_on_every_theme() {
         &policy,
         &cute_dbt::domain::ProjectFacts::default(),
         &cute_dbt::domain::GovernanceFacts::default(),
+        None,
     )
     .expect("render writes the report");
     let url = format!("file://{}", out.to_str().expect("UTF-8 path"));
@@ -12104,6 +12108,7 @@ fn render_with_project_facts(filename: &str, facts: &ProjectFacts) -> String {
         &cute_dbt::domain::CheckPolicy::default(),
         facts,
         &cute_dbt::domain::GovernanceFacts::default(),
+        None,
     )
     .expect("render writes the report");
     format!("file://{}", out.to_str().expect("UTF-8 path"))
@@ -12143,6 +12148,7 @@ fn render_governance_to_file(
         &cute_dbt::domain::CheckPolicy::default(),
         &cute_dbt::domain::ProjectFacts::default(),
         governance,
+        None,
     )
     .expect("render writes the report");
     format!("file://{}", out.to_str().expect("UTF-8 path"))
@@ -12241,6 +12247,166 @@ fn governance_contract_drawer_absent_without_a_class_in_a_real_browser() {
     let _ = tab.close(true);
 }
 
+// ===== macro lens (cute-dbt#265, Slice B) =====
+
+/// cute-dbt#265 Slice B — render a minimal pr-diff report carrying a
+/// `MacroLensPayload` (the server-rendered macro section) to a temp file,
+/// returning its file:// URL. Built from a real `build_macro_lens` over a
+/// synthetic manifest so the body diff + the impacted-model directory tree
+/// are the genuine render output, proven to survive in a real browser with
+/// the full asset bundle loaded.
+fn render_macro_lens_to_file(filename: &str) -> String {
+    // Two root-project models in different directory subtrees calling one
+    // macro, so the tree groups under distinct directories.
+    let macro_id = "macro.shop.add_dq_flags";
+    let body = "{% macro add_dq_flags(col) %}\n  case when {{ col }} then 1 end\n{% endmacro %}";
+    let mut macros = HashMap::new();
+    macros.insert(macro_id.to_owned(), body.to_owned());
+    let mut identity = std::collections::BTreeMap::new();
+    identity.insert(
+        macro_id.to_owned(),
+        MacroIdentity::new(
+            Some("macros/dq.sql".to_owned()),
+            Some("add_dq_flags".to_owned()),
+            Some("shop".to_owned()),
+        ),
+    );
+    let model = |bare: &str, ofp: &str| {
+        Node::new(
+            NodeId::new(format!("model.shop.{bare}")),
+            "model",
+            Checksum::new("sha256", bare),
+            Some("select 1".to_owned()),
+            None,
+            DependsOn::new(vec![macro_id.to_owned()], Vec::new()),
+            Some(format!("models/{ofp}")),
+            NodeConfig::default(),
+            None,
+            BTreeMap::new(),
+        )
+        .with_identity(None, Some("shop".to_owned()))
+    };
+    let nodes = [
+        model("stg_orders", "staging/stg_orders.sql"),
+        model("fct_orders", "marts/core/fct_orders.sql"),
+    ];
+    let m = Manifest::new(
+        ManifestMetadata::new("v12").with_project_name(Some("shop".to_owned())),
+        nodes.into_iter().map(|n| (n.id().clone(), n)).collect(),
+        HashMap::new(),
+        macros,
+    )
+    .with_macro_identity(identity);
+    // A pr-diff index touching the macro file's line 2, so the body diff
+    // reconstructs (the `+` matches the working-tree body).
+    let diff = PrDiff {
+        renames: Vec::new(),
+        files: vec![FileHunks {
+            path: "macros/dq.sql".to_owned(),
+            hunks: vec![Hunk {
+                new_start: 2,
+                new_len: 1,
+                removed_lines: vec!["  case when {{ col }} then 0 end".to_owned()],
+                added_lines: vec!["  case when {{ col }} then 1 end".to_owned()],
+            }],
+        }],
+    };
+    let index = NormalizedDiffIndex::new(&diff, None);
+    let changed = [macro_id.to_owned()].into_iter().collect();
+    let lens: MacroLensPayload =
+        build_macro_lens(&m, &changed, ScopeSource::PrDiff, Some(&index)).expect("lens builds");
+
+    let models: ModelInScopeSet = [
+        NodeId::new("model.shop.stg_orders"),
+        NodeId::new("model.shop.fct_orders"),
+    ]
+    .into_iter()
+    .collect();
+    let out = tmp(filename);
+    let _ = std::fs::remove_file(&out);
+    render_report_with_externals(
+        &out,
+        &m,
+        &InScopeSet::new(),
+        &models,
+        &InScopeSet::new(),
+        &HashMap::new(),
+        &HashMap::new(),
+        &HashMap::new(),
+        &HashMap::new(),
+        &HashMap::new(),
+        &HashMap::new(),
+        "",
+        ScopeSource::PrDiff,
+        DEFAULT_REPORT_TITLE,
+        None,
+        &cute_dbt::domain::CheckPolicy::default(),
+        &cute_dbt::domain::ProjectFacts::default(),
+        &cute_dbt::domain::GovernanceFacts::default(),
+        Some(&lens),
+    )
+    .expect("render writes the report");
+    format!("file://{}", out.to_str().expect("UTF-8 path"))
+}
+
+#[test]
+#[ignore = "requires Chrome; runs explicitly in the headless-zero-egress CI job via `-- --ignored`"]
+fn macro_lens_section_renders_with_tree_in_a_real_browser() {
+    // cute-dbt#265 Slice B — the server-rendered macro section survives in a
+    // real browser with the full asset bundle: the panel is in the DOM, the
+    // body diff renders, the impacted-model directory tree is present and
+    // (since it ships `<details open>`) its model leaves are visible. The
+    // dedicated headless_zero_egress job proves the same committed golden
+    // makes zero network requests; this guard proves the section RENDERS.
+    let url = render_macro_lens_to_file("cute_dbt_macro_lens.html");
+
+    let browser = launch_browser();
+    let tab = browser.new_tab().expect("new tab");
+    tab.navigate_to(&url).expect("navigate");
+    tab.wait_until_navigated().expect("await navigation");
+    wait_for_document_ready(&tab);
+
+    assert_eq!(
+        visible_count(&tab, "[data-testid=\"macro-lens-panel\"]"),
+        1,
+        "the macro-lens section renders",
+    );
+    assert_eq!(
+        visible_count(&tab, "[data-testid=\"macro-lens-diff\"]"),
+        1,
+        "the macro body diff renders",
+    );
+    // The directory tree ships `<details open>`, so its model leaves are
+    // visible without interaction — assert both impacted models are shown.
+    let leaves = eval(
+        &tab,
+        "document.querySelectorAll('[data-testid=\"macro-lens-tree-row\"][data-kind=\"model\"]').length",
+    );
+    assert_eq!(
+        leaves.as_i64(),
+        Some(2),
+        "both impacted-model leaves render in the tree",
+    );
+    let count = eval_string(
+        &tab,
+        "document.querySelector('[data-testid=\"macro-lens-count\"]').getAttribute('data-count')",
+    );
+    assert_eq!(count, "2", "the impacted-model count reads 2");
+    // The tree <details> can be toggled closed and re-opened — prove the
+    // expand affordance is live in the real DOM.
+    let toggled_open = eval(
+        &tab,
+        "(() => { const d = document.querySelector('[data-testid=\"macro-lens-tree-details\"]'); \
+         d.open = false; d.open = true; return d.open; })()",
+    );
+    assert_eq!(
+        toggled_open.as_bool(),
+        Some(true),
+        "the impacted-model tree <details> expands",
+    );
+    let _ = tab.close(true);
+}
+
 #[test]
 #[ignore = "requires Chrome; runs explicitly in the headless-zero-egress CI job via `-- --ignored`"]
 fn governance_meta_chip_tooltip_reveals_the_bubble_on_focus_in_a_real_browser() {
diff --git a/tests/steps/macro_perspective.rs b/tests/steps/macro_perspective.rs
new file mode 100644
index 00000000..28bb003e
--- /dev/null
+++ b/tests/steps/macro_perspective.rs
@@ -0,0 +1,358 @@
+//! Step definitions for `features/macro_perspective.feature`
+//! (cute-dbt#265 — the macro perspective lens, Slice B).
+//!
+//! Subprocess wire round-trip (the BDD house style, the
+//! `project_definition.rs` precedent): each scenario builds a synthetic
+//! current manifest carrying a root-project (or vendor) macro called by
+//! models, injects the macro in its real wire shape
+//! (`{ macro_sql, depends_on, original_file_path, name, package_name }` —
+//! the domain stores macros as bare body strings the reader does not
+//! accept), writes a real working-tree macro source file byte-aligned to
+//! that body (the same-revision contract), synthesizes a `--unified=0`
+//! patch editing exactly one line of it, runs the actual `cute-dbt` binary
+//! with `--pr-diff @macro.patch --project-root .`, and asserts the rendered
+//! macro-lens markup. The experimental switch is opted in exactly like a
+//! consumer (the `CUTE_DBT_EXPERIMENTAL` env surface, default-OFF).
+
+use std::collections::{BTreeMap, HashMap};
+
+use cucumber::{given, then, when};
+use serde_json::{Value, json};
+
+use super::super::common;
+use super::World;
+use cute_dbt::domain::{Checksum, DependsOn, Manifest, ManifestMetadata, Node, NodeConfig, NodeId};
+
+/// The project the macro-lens scenarios pin as the root project (the
+/// blast-radius `package_name == project_name` filter keys on this).
+const PROJECT: &str = "shop";
+
+/// The changed macro's declaring file, project-relative — the path the
+/// synthesized patch addresses and the working-tree source file lives at.
+const MACRO_PATH: &str = "macros/dq.sql";
+
+/// The macro body (3 lines). Line 2 is the line the patch edits; the `+`
+/// side byte-matches it so the N7b drift guard aligns. The working-tree
+/// source file is this body + one trailing newline (the on-disk shape the
+/// reconstruction normalizes).
+const MACRO_BODY: &str =
+    "{% macro add_dq_flags(col) %}\n  case when {{ col }} then 1 end\n{% endmacro %}";
+
+/// The macro's `unique_id` (root-project arm).
+fn root_macro_id() -> String {
+    format!("macro.{PROJECT}.add_dq_flags")
+}
+
+/// A root-project `model` node calling `macro_id` directly, declaring
+/// `original_file_path` and a passing compiled body (so Stage-2 preflight
+/// is a no-op — these models carry no unit tests).
+fn macro_model(bare: &str, ofp: &str, macro_id: &str) -> Node {
+    Node::new(
+        NodeId::new(format!("model.{PROJECT}.{bare}")),
+        "model",
+        Checksum::new("sha256", bare),
+        Some("select 1".to_owned()),
+        None,
+        DependsOn::new(vec![macro_id.to_owned()], Vec::new()),
+        Some(format!("models/{ofp}")),
+        NodeConfig::default(),
+        None,
+        BTreeMap::new(),
+    )
+    .with_identity(None, Some(PROJECT.to_owned()))
+}
+
+/// Build the root-project current manifest: two models in different
+/// directory subtrees, both calling the root-project macro.
+fn manifest_two_callers() -> Manifest {
+    let nodes = [
+        macro_model("stg_orders", "staging/stg_orders.sql", &root_macro_id()),
+        macro_model("fct_orders", "marts/core/fct_orders.sql", &root_macro_id()),
+    ];
+    Manifest::new(
+        ManifestMetadata::new("https://schemas.getdbt.com/dbt/manifest/v12.json")
+            .with_project_name(Some(PROJECT.to_owned())),
+        nodes.into_iter().map(|n| (n.id().clone(), n)).collect(),
+        HashMap::new(),
+        HashMap::new(),
+    )
+}
+
+/// Build the vendor-arm manifest: a root-project model calls a
+/// VENDOR-package macro (`macro.dbt_utils.add_dq_flags`). Editing the
+/// vendor macro must NOT surface the lens (the root-project filter on both
+/// the changed-macro detection name-key and the blast radius).
+fn manifest_vendor_macro() -> Manifest {
+    let vendor_id = "macro.dbt_utils.add_dq_flags".to_owned();
+    let node = macro_model("stg_orders", "staging/stg_orders.sql", &vendor_id);
+    Manifest::new(
+        ManifestMetadata::new("https://schemas.getdbt.com/dbt/manifest/v12.json")
+            .with_project_name(Some(PROJECT.to_owned())),
+        std::iter::once((node.id().clone(), node)).collect(),
+        HashMap::new(),
+        HashMap::new(),
+    )
+}
+
+/// Serialize the manifest, then inject the macro in its real wire shape:
+/// `{ macro_sql, depends_on: { macros: [] }, original_file_path, name,
+/// package_name }`. The domain `Manifest` cannot express the macro
+/// identity triple through a bare-body serialization, so the wire shape is
+/// spliced in at the JSON layer (the `serialize_with_wire_macros_to_tmp`
+/// precedent, extended with the #265 identity fields).
+fn serialize_with_wire_macro(manifest: &Manifest, name: &str, macro_id: &str, package: &str) {
+    let mut value: Value = serde_json::to_value(manifest).expect("manifest serializes to Value");
+    let macro_name = macro_id.rsplit('.').next().unwrap_or(macro_id);
+    if !value["macros"].is_object() {
+        value["macros"] = json!({});
+    }
+    value["macros"][macro_id] = json!({
+        "macro_sql": MACRO_BODY,
+        "depends_on": { "macros": [] },
+        "original_file_path": MACRO_PATH,
+        "name": macro_name,
+        "package_name": package,
+    });
+    let path = common::tmp(name);
+    std::fs::write(
+        &path,
+        serde_json::to_string(&value).expect("injected manifest serializes"),
+    )
+    .expect("write synthetic manifest");
+}
+
+/// The workdir the subprocess runs in (`--project-root .`); the working-tree
+/// macro source file is written under it.
+fn macro_workdir() -> std::path::PathBuf {
+    let workdir = common::tmp("macro_lens_workdir");
+    std::fs::create_dir_all(workdir.join("macros")).expect("create macro workdir");
+    workdir
+}
+
+// ---------------------------------------------------------------------
+// Given
+// ---------------------------------------------------------------------
+
+#[given("a current manifest with a root-project macro called by two models")]
+fn current_manifest_two_callers(world: &mut World) {
+    world.current_manifest = Some(manifest_two_callers());
+    // Mark the root arm so the When serializes the root macro wire shape.
+    world.fixture_choice = None;
+}
+
+#[given("a current manifest with a vendor-package macro called by a root-project model")]
+fn current_manifest_vendor_macro(world: &mut World) {
+    world.current_manifest = Some(manifest_vendor_macro());
+    // Stash the vendor id so the When injects the vendor wire shape.
+    world.last_named_model = Some("vendor".to_owned());
+}
+
+#[given("the working tree carries that macro's source file")]
+fn working_tree_carries_macro_source(_world: &mut World) {
+    let workdir = macro_workdir();
+    // Source file = the macro body + one trailing newline (the on-disk
+    // shape; the reconstruction strips exactly one terminator).
+    std::fs::write(workdir.join(MACRO_PATH), format!("{MACRO_BODY}\n"))
+        .expect("write macro source file");
+}
+
+#[given("the PR diff edits the macro's body")]
+fn diff_edits_macro_body(world: &mut World) {
+    // `--unified=0` single-line edit on macro line 2. The `+` side
+    // byte-matches the working-tree body (N7b aligns); the `-` side is the
+    // invented pre-edit line.
+    let patch = format!(
+        concat!(
+            "diff --git a/{path} b/{path}\n",
+            "--- a/{path}\n",
+            "+++ b/{path}\n",
+            "@@ -2 +2 @@\n",
+            "-  case when {{{{ col }}}} then 0 end\n",
+            "+  case when {{{{ col }}}} then 1 end\n",
+        ),
+        path = MACRO_PATH,
+    );
+    write_macro_patch(world, &patch);
+}
+
+#[given("the PR diff edits only a model's SQL, not the macro")]
+fn diff_edits_model_only(world: &mut World) {
+    // A diff touching a NON-macro file: the macro section must stay absent.
+    let patch = concat!(
+        "diff --git a/models/staging/stg_orders.sql b/models/staging/stg_orders.sql\n",
+        "--- a/models/staging/stg_orders.sql\n",
+        "+++ b/models/staging/stg_orders.sql\n",
+        "@@ -1 +1 @@\n",
+        "-select 0\n",
+        "+select 1\n",
+    );
+    write_macro_patch(world, patch);
+}
+
+#[given("the experimental switch enables macro-lens")]
+fn experimental_switch_enables_macro_lens(world: &mut World) {
+    world.experimental_env = Some("macro-lens".to_owned());
+}
+
+fn write_macro_patch(world: &mut World, patch: &str) {
+    let path = common::tmp("macro_lens.patch");
+    std::fs::write(&path, patch).expect("write macro.patch");
+    world.explicit_patch = Some(path);
+}
+
+// ---------------------------------------------------------------------
+// When
+// ---------------------------------------------------------------------
+
+#[when("I run cute-dbt report in pr-diff mode against the macro patch")]
+fn run_macro_pr_diff(world: &mut World) {
+    let manifest = world
+        .current_manifest
+        .take()
+        .expect("a Given built a manifest");
+    let is_vendor = world.last_named_model.as_deref() == Some("vendor");
+    if is_vendor {
+        serialize_with_wire_macro(
+            &manifest,
+            "macro_lens_current.json",
+            "macro.dbt_utils.add_dq_flags",
+            "dbt_utils",
+        );
+    } else {
+        serialize_with_wire_macro(
+            &manifest,
+            "macro_lens_current.json",
+            &root_macro_id(),
+            PROJECT,
+        );
+    }
+    world.current_manifest = Some(manifest);
+    let manifest_path = common::tmp("macro_lens_current.json");
+
+    let workdir = macro_workdir();
+    let patch_path = world
+        .explicit_patch
+        .clone()
+        .expect("a Given wrote the patch");
+    let out = common::tmp("macro_lens_report.html");
+    common::clear(&out);
+
+    let scope_arg = format!("@{}", common::s(&patch_path));
+    let mut cmd = std::process::Command::new(env!("CARGO_BIN_EXE_cute-dbt"));
+    cmd.args([
+        "report",
+        "--manifest",
+        common::s(&manifest_path),
+        "--pr-diff",
+        &scope_arg,
+        "--project-root",
+        ".",
+        "--out",
+        common::s(&out),
+    ])
+    .current_dir(&workdir);
+    cmd.env_remove("CUTE_DBT_EXPERIMENTAL");
+    if let Some(value) = &world.experimental_env {
+        cmd.env("CUTE_DBT_EXPERIMENTAL", value);
+    }
+    let output = cmd.output().expect("the cute-dbt binary spawns");
+    world.last_exit_code = output.status.code();
+    world.last_stderr = String::from_utf8_lossy(&output.stderr).into_owned();
+    world.report_html = std::fs::read_to_string(&out).ok();
+    if let Some(html) = &world.report_html {
+        common::assert_no_external_refs(html);
+    }
+    world.out_path = Some(out);
+}
+
+// ---------------------------------------------------------------------
+// Then
+// ---------------------------------------------------------------------
+
+fn html(world: &World) -> &str {
+    world
+        .report_html
+        .as_deref()
+        .unwrap_or_else(|| panic!("report.html was not written; stderr={}", world.last_stderr))
+}
+
+#[then("the report carries the macro-lens section")]
+fn report_carries_macro_section(world: &mut World) {
+    assert!(
+        html(world).contains(r#"data-testid="macro-lens-panel""#),
+        "the macro-lens section must render; stderr={}",
+        world.last_stderr,
+    );
+}
+
+#[then("the report carries no macro-lens section")]
+fn report_carries_no_macro_section(world: &mut World) {
+    assert!(
+        !html(world).contains(r#"data-testid="macro-lens-panel""#),
+        "no macro-lens section may render in this case; stderr={}",
+        world.last_stderr,
+    );
+}
+
+#[then("the macro-lens section names the changed macro")]
+fn section_names_macro(world: &mut World) {
+    assert!(
+        html(world).contains("add_dq_flags"),
+        "the section must name the changed macro",
+    );
+}
+
+#[then("the macro-lens section carries the macro body diff")]
+fn section_carries_body_diff(world: &mut World) {
+    let html = html(world);
+    assert!(
+        html.contains(r#"data-testid="macro-lens-diff""#),
+        "the section must carry the body diff",
+    );
+    assert!(
+        html.contains("then 0 end"),
+        "the diff must splice in the pre-edit macro line",
+    );
+}
+
+#[then(regex = r#"^the macro-lens section reports the impacted-model count as (\d+)$"#)]
+fn section_reports_count(world: &mut World, count: String) {
+    let html = html(world);
+    assert!(
+        html.contains(&format!(r#"data-count="{count}""#)),
+        "the impacted-model count must be {count}",
+    );
+}
+
+#[then("the macro-lens section lists both impacted models in the directory tree")]
+fn section_lists_both_models(world: &mut World) {
+    let html = html(world);
+    assert!(
+        html.contains(r#"data-testid="macro-lens-tree""#),
+        "tree present"
+    );
+    assert!(
+        html.contains(r#"data-model="model.shop.stg_orders""#),
+        "stg_orders leaf present",
+    );
+    assert!(
+        html.contains(r#"data-model="model.shop.fct_orders""#),
+        "fct_orders leaf present",
+    );
+}
+
+#[then(regex = r#"^the macro-lens fidelity chip reads "([^"]+)"$"#)]
+fn fidelity_chip_reads(world: &mut World, fidelity: String) {
+    assert!(
+        html(world).contains(&format!(r#"data-fidelity="{fidelity}""#)),
+        "the fidelity chip must read {fidelity:?}",
+    );
+}
+
+#[then(regex = r#"^the macro-lens section never names a "([^"]+)" selector$"#)]
+fn section_never_names_selector(world: &mut World, selector: String) {
+    assert!(
+        !html(world).contains(&selector),
+        "the section must never name the {selector:?} selector (critique S2)",
+    );
+}
diff --git a/tests/steps/mod.rs b/tests/steps/mod.rs
index 160b8741..5adb5e19 100644
--- a/tests/steps/mod.rs
+++ b/tests/steps/mod.rs
@@ -32,6 +32,7 @@ pub mod explore_test_badges;
 pub mod explore_view_toggle;
 pub mod fail_closed;
 pub mod incremental_models;
+pub mod macro_perspective;
 pub mod one_command_review;
 pub mod pr_diff_scoping;
 pub mod project_definition;