diff --git a/examples/diff-showcase-report.html b/examples/diff-showcase-report.html
index 13733604..8add3930 100644
--- a/examples/diff-showcase-report.html
+++ b/examples/diff-showcase-report.html
@@ -1542,11 +1542,16 @@
 .model-tooltip .ct-desc { font-size: 1.3em; line-height: 1.5; }
 .model-tooltip .mt-mat { font-size: 0.72rem; text-transform: uppercase; letter-spacing: 0.05em; color: #f0c674; border: 1px solid currentColor; border-radius: 999px; padding: 0.02rem 0.5rem; }
 .model-tooltip .mt-tag { font-size: 0.72rem; color: rgba(255,255,255,0.82); background: rgba(255,255,255,0.12); border-radius: 999px; padding: 0.02rem 0.5rem; }
-/* model-level tests inside the model-ref tip (the dark-context dress for
-   the shelf card's .dt-* chips). */
-.model-tooltip .mt-mtests { display: flex; flex-direction: column; gap: 0.3rem; margin-top: 0.3rem; }
-.model-tooltip .dt-key { color: color-mix(in oklab, var(--accent) 60%, white); }
-.model-tooltip .dt-val { background: rgba(255,255,255,0.14); color: rgba(255,255,255,0.92); }
+/* cute-dbt#235 — model-level tests inside the model-ref tip ride the
+   COLUMN tooltip's own .ct-test/.ct-key/.ct-vals anatomy (the shared
+   ctTestHtml builder — never a parallel chip system; the .mt-mtests/
+   .dt-* dress is retired here, the shelf card keeps its light-surface
+   .dt-* chips). Test names keep the readable-on-dark accent treatment
+   the retired .model-tooltip .dt-key carried; the value chips inherit
+   .col-tooltip .ct-val verbatim. The base .col-tooltip .ct-key color
+   is cute-dbt#233's lane — only this model-scoped override lives here
+   (same specificity, later in file, so it wins inside the model tip). */
+.model-tooltip .ct-key { color: color-mix(in oklab, var(--accent) 60%, white); }
 .ov-tooltip .ov-grp + .ov-grp { margin-top: 0.55rem; }
 .ov-tooltip .ov-grp-name { font-family: var(--font-mono); font-size: 0.78em; text-transform: uppercase; letter-spacing: 0.06em; opacity: 0.6; margin-bottom: 0.25rem; }
 .ov-tooltip .ov-row { display: flex; gap: 0.5rem; align-items: baseline; padding-left: 0.45rem; white-space: nowrap; }
@@ -7063,13 +7068,13 @@ <h2>Expected</h2>
       h += "</div>";
     }
     if (info.description) h += '<div class="ct-desc">' + escapeHtml(info.description) + "</div>";
+    // cute-dbt#235 — model tests ride the column tooltip's own
+    // .ct-test/.ct-key/.ct-vals anatomy (the shared ctTestHtml builder),
+    // so the model tip and the column tip stay visually consistent.
+    // The shelf card keeps its light-surface .dt-* chips (testChipsHtml).
     if (info.model_tests && info.model_tests.length) {
       h += '<div class="ct-tests-label">Model tests</div>';
-      h += '<div class="mt-mtests">';
-      info.model_tests.forEach(function (tt) {
-        h += '<div class="dt-row">' + testChipsHtml(tt) + "</div>";
-      });
-      h += "</div>";
+      h += '<div class="ct-tests">' + info.model_tests.map(ctTestHtml).join("") + "</div>";
     }
     el.innerHTML = h;
     el.hidden = false;
@@ -8343,6 +8348,31 @@ <h2>Expected</h2>
               : t.detail ? " " + t.detail : "";
     return t.name + extra;
   }
+  // One test row in the shared tooltip anatomy: accent `.ct-key` name +
+  // one `.ct-val` chip per argument. cute-dbt#202 — a multi-arg detail
+  // splits into one chip per argument (the ct-vals/ct-val dress the
+  // values branch uses; the ct-detail run is retired); the shipped
+  // single-value detail forms — relationships `m.f`, accepted_range
+  // `0–100` — carry no comma, so they stay one chip. cute-dbt#235 —
+  // shared by the column tooltip AND the model-ref tip's model-test
+  // rows, so the two bubbles cannot drift apart visually. Every
+  // interpolation passes through escapeHtml (payload strings never
+  // parse as HTML).
+  function ctTestHtml(t) {
+    var inner = '<span class="ct-key">' + escapeHtml(t.name) + "</span>";
+    if (t.values && t.values.length) {
+      inner += '<span class="ct-vals">'
+        + t.values.map(function (v) { return '<span class="ct-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
+        + "</span>";
+    } else if (t.detail) {
+      inner += '<span class="ct-vals">'
+        + String(t.detail).split(/,\s*/).map(function (a) {
+            return '<span class="ct-val">' + escapeHtml(a) + "</span>";
+          }).join("")
+        + "</span>";
+    }
+    return '<div class="ct-test">' + inner + "</div>";
+  }
   function decorateColHeader($th, name, meta) {
     if (!meta || (!meta.description && !(meta.tests && meta.tests.length))) return;
     var tests = meta.tests || [];
@@ -8378,26 +8408,7 @@ <h2>Expected</h2>
     var h = '<div class="ct-desc">' + escapeHtml(desc || name) + "</div>";
     if (tests.length) {
       h += '<div class="ct-tests-label">Data tests</div><div class="ct-tests">'
-         + tests.map(function (t) {
-             var inner = '<span class="ct-key">' + escapeHtml(t.name) + "</span>";
-             if (t.values && t.values.length) {
-               inner += '<span class="ct-vals">'
-                 + t.values.map(function (v) { return '<span class="ct-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
-                 + "</span>";
-             } else if (t.detail) {
-               // cute-dbt#202 — a multi-arg detail splits into one chip per
-               // argument (the ct-vals/ct-val dress the values branch uses;
-               // the ct-detail run is retired). The shipped detail forms —
-               // relationships `m.f`, accepted_range `0–100` — carry no
-               // comma, so a single-value detail stays one chip.
-               inner += '<span class="ct-vals">'
-                 + String(t.detail).split(/,\s*/).map(function (a) {
-                     return '<span class="ct-val">' + escapeHtml(a) + "</span>";
-                   }).join("")
-                 + "</span>";
-             }
-             return '<div class="ct-test">' + inner + "</div>";
-           }).join("")
+         + tests.map(ctTestHtml).join("")
          + "</div>";
     }
     el.innerHTML = h;
diff --git a/examples/explore/tests.html b/examples/explore/tests.html
index d56cd278..b3ede72b 100644
--- a/examples/explore/tests.html
+++ b/examples/explore/tests.html
@@ -661,7 +661,7 @@ <h2>v_provider_active_patients</h2>
 
 
 
-<script type="application/json" id="cute-dbt-data">{"baseline":"","models":[{"name":"dim_conditions","path":"models/marts/analytics/dim_conditions.sql","description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"condition_aggregates","role":"transform"},{"id":"deduped","role":"transform"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_conditions.sql","role":"final"}],"edges":[{"from":"conditions_source","to":"condition_aggregates","edge_type":"from"},{"from":"condition_aggregates","to":"deduped","edge_type":"from"},{"from":"deduped","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_aggregates":"condition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n)","conditions_source":"conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n)","deduped":"deduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n)","final":"final as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n)","unknown_member":"unknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}]},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_date","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_date_date_key.0869fd48f9"]},"evidence":[{"label":"unique_key","value":"date_key"}],"pin_node":"(final select)"}]},{"name":"dim_organizations","path":"models/marts/core/dim_organizations.sql","description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_organizations.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='organization_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"]},"evidence":[{"label":"unique_key","value":"organization_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__organizations')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}"]}]},{"name":"dim_patients","path":"models/marts/core/dim_patients.sql","description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","dag":{"nodes":[{"id":"snapshot_source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_patients.sql","role":"final"}],"edges":[{"from":"snapshot_source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n)"},"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 <= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_patients","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__patients')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}]},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _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\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme 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"}],"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"}],"pin_node":"final"}]},{"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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_providers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"]},"evidence":[{"label":"unique_key","value":"provider_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_providers","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('dim_organizations'), ref('stg_synthea__providers')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. 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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_condition_cohorts","path":"models/marts/analytics/fct_condition_cohorts.sql","description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"patients","role":"import"},{"id":"dim_conditions","role":"import"},{"id":"condition_windows","role":"transform"},{"id":"with_dimensions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final 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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}]},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"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","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)"]}]},{"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","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)"}]},{"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 %}","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","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}},{"input":"ref('stg_synthea__encounters')","bound_to_node":"source","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]},{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0},{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"patient_id":{"description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}}}],"expected":{"table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"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","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)"}]},{"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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_yearly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_actual, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"}]},{"name":"fct_patient_summary","path":"models/marts/analytics/fct_patient_summary.sql","description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health 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","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)"}]},{"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","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"}],"pin_node":"(final select)"}]},{"name":"int_dq_quarantine__encounters","path":"models/intermediate/quarantine/int_dq_quarantine__encounters.sql","description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","dag":{"nodes":[],"edges":[]},"compiled_sql":{"int_dq_quarantine__encounters":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__encounters',\n    description='Encounters quarantined due to data quality violations'\n) }}\n\n-- cute-dbt fixture: synthetic body mod","tests":[],"is_recursive":false},{"name":"int_dq_quarantine__medications","path":"models/intermediate/quarantine/int_dq_quarantine__medications.sql","description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","dag":{"nodes":[],"edges":[]},"compiled_sql":{"int_dq_quarantine__medications":"\n\n\n\n-- Medications quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__medications\"\nwhere is_dq_valid = false\n"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__medications',\n    description='Medications quarantined due to data quality violations'\n) }}","tests":[],"is_recursive":false},{"name":"int_encounters__enriched","path":"models/intermediate/healthcare/int_encounters__enriched.sql","description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"conditions","role":"import"},{"id":"medications","role":"import"},{"id":"procedures","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_encounters__enriched.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"conditions","to":"final","edge_type":"left"},{"from":"medications","to":"final","edge_type":"left"},{"from":"procedures","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","conditions":"conditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    group by encounter_id\n)","encounters":"encounters as (\n    select\n        encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","medications":"medications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n    \n    \n    where is_dq_valid = true\n    \n\n    group by encounter_id\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"raw_sql":"{{\n    config(\n        materialized='view'\n    )\n}}\n\nwith encounters as (\n    select\n        encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nconditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from {{ ref('stg_synthea__conditions') }}\n    group by encounter_id\n),\n\nmedications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from {{ ref('stg_synthea__medications') }}\n    {{ quarantine_filter() }}\n    group by encounter_id\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nfinal as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"int_patients__never_admitted":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"int_patients__with_conditions","path":"models/intermediate/healthcare/int_patients__with_conditions.sql","description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","dag":{"nodes":[{"id":"patients","role":"import"},{"id":"conditions","role":"import"},{"id":"condition_stats","role":"transform"},{"id":"chronic_flags","role":"transform"},{"id":"chronic_count","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_patients__with_conditions.sql","role":"final"}],"edges":[{"from":"conditions","to":"condition_stats","edge_type":"from"},{"from":"conditions","to":"chronic_flags","edge_type":"from"},{"from":"chronic_flags","to":"chronic_count","edge_type":"from"},{"from":"patients","to":"final","edge_type":"from"},{"from":"condition_stats","to":"final","edge_type":"left"},{"from":"chronic_flags","to":"final","edge_type":"left"},{"from":"chronic_count","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","chronic_count":"chronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n)","chronic_flags":"chronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n)","condition_stats":"condition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n)","conditions":"conditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","final":"final as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)","patients":"patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)"},"raw_sql":"{{\n    config(\n        materialized='view'\n    )\n}}\n\n{#\n    Patient condition history with chronic disease flags.\n\n    IMPORTANT: Chronic Condition Detection Limitations\n    --------------------------------------------------\n    The chronic condition flags (has_diabetes, has_hypertension, has_heart_disease)\n    use simplified text matching on condition_description. This is NOT clinical-grade\n    detection and should be used for analytics/reporting only, not clinical decisions.\n\n    Detection patterns:\n    - Diabetes: description contains 'diabetes'\n    - Hypertension: description contains 'hypertension' or 'high blood pressure'\n    - Heart disease: description contains 'heart disease', 'cardiac', 'coronary',\n                     or 'atrial fibrillation'\n\n    For production clinical use, consider:\n    1. Using SNOMED codes instead of text matching\n    2. Implementing ICD-10 code mappings\n    3. Consulting clinical informaticists for proper condition groupings\n    4. Moving patterns to a seed file or reference table for maintainability\n#}\n\nwith patients as (\n    select\n        patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from {{ ref('stg_synthea__conditions') }}\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.int_patients__with_conditions","construct":"left_join[final:condition_stats]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"final — LEFT JOIN condition_stats ON patients.patient_id = condition_stats.patient_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"final","sketches":["- input: ref('dim_patients')\n  rows:\n    - {patient_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__conditions')\n  rows:\n    - {patient_id: 1}\n# expect: the no-match row with NULL stg_synthea__conditions columns (or the intended fallback)"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","rows":null,"format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv"}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , 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    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref('stg_synthea__medications')","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. 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","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"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}]},{"name":"stg_synthea__conditions","path":"models/staging/synthea/stg_synthea__conditions.sql","description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_surrogate_key","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__conditions.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_surrogate_key","edge_type":"from"},{"from":"with_surrogate_key","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)","renamed":"renamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- dates\n        , START as condition_start_date\n        , STOP as condition_end_date\n\n        -- condition details\n        , CODE as condition_code\n        , DESCRIPTION as condition_description\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"conditions\"\n)","with_surrogate_key":"with_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_start_date as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as condition_id\n        , *\n    from renamed\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'conditions') }}\n),\n\nrenamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- dates\n        , START as condition_start_date\n        , STOP as condition_end_date\n\n        -- condition details\n        , CODE as condition_code\n        , DESCRIPTION as condition_description\n    from source\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        {{ dbt_utils.generate_surrogate_key(['patient_id', 'encounter_id', 'condition_code', 'condition_start_date']) }} as condition_id\n        , *\n    from renamed\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__encounters","path":"models/staging/synthea/stg_synthea__encounters.sql","description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_dq_flags","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__encounters.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_dq_flags","edge_type":"from"},{"from":"with_dq_flags","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_dq_flags\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as encounter_id\n\n        -- foreign keys\n        , PATIENT as patient_id\n        , ORGANIZATION as organization_id\n        , PROVIDER as provider_id\n        , PAYER as payer_id\n\n        -- timestamps\n        , cast(START as timestamp) as encounter_start_at\n        , cast(STOP as timestamp) as encounter_end_at\n\n        -- attributes\n        , ENCOUNTERCLASS as encounter_class\n        , CODE as encounter_code\n        , DESCRIPTION as encounter_description\n\n        -- costs\n        , BASE_ENCOUNTER_COST as base_encounter_cost\n        , TOTAL_CLAIM_COST as total_claim_cost\n        , PAYER_COVERAGE as payer_coverage\n\n        -- reason for visit\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"encounters\"\n)","with_dq_flags":"with_dq_flags as (\n    \n    select\n        *\n\n        -- individual validation flags\n        \n        , (encounter_end_at >= encounter_start_at) as valid_encounter_timestamps\n        \n        , (encounter_start_at <= current_timestamp) as no_future_encounter_dates\n        \n        , (encounter_end_at >= timestamp '1900-01-01') as end_after_1900\n        \n        , (encounter_start_at >= timestamp '1900-01-01') as start_after_1900\n        \n\n        -- overall validity flag\n        , \n    (\n        \n        (encounter_end_at >= encounter_start_at)\n         and \n        \n        (encounter_start_at <= current_timestamp)\n         and \n        \n        (encounter_end_at >= timestamp '1900-01-01')\n         and \n        \n        (encounter_start_at >= timestamp '1900-01-01')\n        \n        \n    )\n as is_dq_valid\n\n        -- array of failed test names\n        , \n    list_value(\n        \n        case when not (encounter_end_at >= encounter_start_at) then 'valid_encounter_timestamps' else null end\n        , \n        \n        case when not (encounter_start_at <= current_timestamp) then 'no_future_encounter_dates' else null end\n        , \n        \n        case when not (encounter_end_at >= timestamp '1900-01-01') then 'end_after_1900' else null end\n        , \n        \n        case when not (encounter_start_at >= timestamp '1900-01-01') then 'start_after_1900' else null end\n        \n        \n    )\n as failed_dq_tests\n\n    from renamed\n\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'encounters') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as encounter_id\n\n        -- foreign keys\n        , PATIENT as patient_id\n        , ORGANIZATION as organization_id\n        , PROVIDER as provider_id\n        , PAYER as payer_id\n\n        -- timestamps\n        , cast(START as timestamp) as encounter_start_at\n        , cast(STOP as timestamp) as encounter_end_at\n\n        -- attributes\n        , ENCOUNTERCLASS as encounter_class\n        , CODE as encounter_code\n        , DESCRIPTION as encounter_description\n\n        -- costs\n        , BASE_ENCOUNTER_COST as base_encounter_cost\n        , TOTAL_CLAIM_COST as total_claim_cost\n        , PAYER_COVERAGE as payer_coverage\n\n        -- reason for visit\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n),\n\nwith_dq_flags as (\n    {{ add_dq_flags(\n        source_cte='renamed',\n        validations={\n            'valid_encounter_timestamps': 'encounter_end_at >= encounter_start_at',\n            'no_future_encounter_dates': 'encounter_start_at <= current_timestamp',\n            'end_after_1900': 'encounter_end_at >= timestamp \\'1900-01-01\\'',\n            'start_after_1900': 'encounter_start_at >= timestamp \\'1900-01-01\\''\n        }\n    ) }}\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_dq_flags\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__medications","path":"models/staging/synthea/stg_synthea__medications.sql","description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_row_number","role":"transform"},{"id":"with_surrogate_key","role":"transform"},{"id":"with_dq_flags","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__medications.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_row_number","edge_type":"from"},{"from":"with_row_number","to":"with_surrogate_key","edge_type":"from"},{"from":"with_surrogate_key","to":"with_dq_flags","edge_type":"from"},{"from":"with_dq_flags","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_dq_flags\n)","renamed":"renamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , PAYER as payer_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamps\n        , cast(START as timestamp) as medication_start_at\n        , cast(STOP as timestamp) as medication_end_at\n\n        -- medication details\n        , CODE as medication_code\n        , DESCRIPTION as medication_description\n\n        -- costs\n        , BASE_COST as base_cost\n        , PAYER_COVERAGE as payer_coverage\n        , DISPENSES as dispenses\n        , TOTALCOST as total_cost\n\n        -- reason for medication\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"medications\"\n)","with_dq_flags":"with_dq_flags as (\n    \n    select\n        *\n\n        -- individual validation flags\n        \n        , (medication_end_at is null or medication_end_at >= medication_start_at) as valid_medication_dates\n        \n        , (cast(medication_start_at as date) <= current_date) as no_future_medication_dates\n        \n        , (medication_start_at >= timestamp '1900-01-01') as start_after_1900\n        \n        , (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') as end_after_1900_if_present\n        \n\n        -- overall validity flag\n        , \n    (\n        \n        (medication_end_at is null or medication_end_at >= medication_start_at)\n         and \n        \n        (cast(medication_start_at as date) <= current_date)\n         and \n        \n        (medication_start_at >= timestamp '1900-01-01')\n         and \n        \n        (medication_end_at is null or medication_end_at >= timestamp '1900-01-01')\n        \n        \n    )\n as is_dq_valid\n\n        -- array of failed test names\n        , \n    list_value(\n        \n        case when not (medication_end_at is null or medication_end_at >= medication_start_at) then 'valid_medication_dates' else null end\n        , \n        \n        case when not (cast(medication_start_at as date) <= current_date) then 'no_future_medication_dates' else null end\n        , \n        \n        case when not (medication_start_at >= timestamp '1900-01-01') then 'start_after_1900' else null end\n        , \n        \n        case when not (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') then 'end_after_1900_if_present' else null end\n        \n        \n    )\n as failed_dq_tests\n\n    from with_surrogate_key\n\n)","with_row_number":"with_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, medication_code, medication_start_at\n            order by medication_end_at asc nulls last, total_cost desc\n        ) as _row_num\n    from renamed\n)","with_surrogate_key":"with_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(medication_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(medication_start_at as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(_row_num as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as medication_id\n        , patient_id\n        , payer_id\n        , encounter_id\n        , medication_start_at\n        , medication_end_at\n        , medication_code\n        , medication_description\n        , base_cost\n        , payer_coverage\n        , dispenses\n        , total_cost\n        , reason_code\n        , reason_description\n    from with_row_number\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'medications') }}\n),\n\nrenamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , PAYER as payer_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamps\n        , cast(START as timestamp) as medication_start_at\n        , cast(STOP as timestamp) as medication_end_at\n\n        -- medication details\n        , CODE as medication_code\n        , DESCRIPTION as medication_description\n\n        -- costs\n        , BASE_COST as base_cost\n        , PAYER_COVERAGE as payer_coverage\n        , DISPENSES as dispenses\n        , TOTALCOST as total_cost\n\n        -- reason for medication\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n),\n\nwith_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, medication_code, medication_start_at\n            order by medication_end_at asc nulls last, total_cost desc\n        ) as _row_num\n    from renamed\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        {{ dbt_utils.generate_surrogate_key(['patient_id', 'encounter_id', 'medication_code', 'medication_start_at', '_row_num']) }} as medication_id\n        , patient_id\n        , payer_id\n        , encounter_id\n        , medication_start_at\n        , medication_end_at\n        , medication_code\n        , medication_description\n        , base_cost\n        , payer_coverage\n        , dispenses\n        , total_cost\n        , reason_code\n        , reason_description\n    from with_row_number\n),\n\nwith_dq_flags as (\n    {{ add_dq_flags(\n        source_cte='with_surrogate_key',\n        validations={\n            'valid_medication_dates': 'medication_end_at is null or medication_end_at >= medication_start_at',\n            'no_future_medication_dates': 'cast(medication_start_at as date) <= current_date',\n            'start_after_1900': 'medication_start_at >= timestamp \\'1900-01-01\\'',\n            'end_after_1900_if_present': 'medication_end_at is null or medication_end_at >= timestamp \\'1900-01-01\\''\n        }\n    ) }}\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_dq_flags\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__observations","path":"models/staging/synthea/stg_synthea__observations.sql","description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_row_number","role":"transform"},{"id":"with_surrogate_key","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__observations.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_row_number","edge_type":"from"},{"from":"with_row_number","to":"with_surrogate_key","edge_type":"from"},{"from":"with_surrogate_key","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)","renamed":"renamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamp\n        , cast(DATE as timestamp) as observation_at\n\n        -- observation details\n        , CODE as observation_code\n        , DESCRIPTION as observation_description\n        , VALUE as observation_value\n        , UNITS as units\n        , TYPE as observation_type\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"observations\"\n)","with_row_number":"with_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, observation_code, observation_at\n            order by observation_value, observation_description\n        ) as _row_num\n    from renamed\n)","with_surrogate_key":"with_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(observation_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(observation_at as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(_row_num as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as observation_id\n        , patient_id\n        , encounter_id\n        , observation_at\n        , observation_code\n        , observation_description\n        , observation_value\n        , units\n        , observation_type\n    from with_row_number\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'observations') }}\n),\n\nrenamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamp\n        , cast(DATE as timestamp) as observation_at\n\n        -- observation details\n        , CODE as observation_code\n        , DESCRIPTION as observation_description\n        , VALUE as observation_value\n        , UNITS as units\n        , TYPE as observation_type\n    from source\n),\n\nwith_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, observation_code, observation_at\n            order by observation_value, observation_description\n        ) as _row_num\n    from renamed\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        {{ dbt_utils.generate_surrogate_key(['patient_id', 'encounter_id', 'observation_code', 'observation_at', '_row_num']) }} as observation_id\n        , patient_id\n        , encounter_id\n        , observation_at\n        , observation_code\n        , observation_description\n        , observation_value\n        , units\n        , observation_type\n    from with_row_number\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__organizations","path":"models/staging/synthea/stg_synthea__organizations.sql","description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__organizations.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as organization_id\n\n        -- attributes\n        , NAME as organization_name\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , PHONE as phone\n\n        -- location\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , REVENUE as revenue\n        , UTILIZATION as utilization\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"organizations\"\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'organizations') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as organization_id\n\n        -- attributes\n        , NAME as organization_name\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , PHONE as phone\n\n        -- location\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , REVENUE as revenue\n        , UTILIZATION as utilization\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__patients","path":"models/staging/synthea/stg_synthea__patients.sql","description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__patients.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"patients\"\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'patients') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.stg_synthea__patients.test_stg_synthea__patients_renames_and_hashes_source_columns","name":"test_stg_synthea__patients_renames_and_hashes_source_columns","target_model":"stg_synthea__patients","changed":false,"description":"stg_synthea__patients renames the raw Synthea UPPERCASE columns to\nsnake_case and hashes the SSN with md5. The given mocks the\nsynthea_raw.patients SOURCE directly (inline dict rows) — the only\nunit test in this project whose input is a source() rather than a\nref() (cute-dbt#57). Dict expect compares listed columns only, so\nthe non-deterministic _loaded_at (current_timestamp) is excluded\nfrom assertion automatically.\n","tags":[],"meta":{},"defined_in":"models/staging/synthea/_synthea__models.yml","given":[{"input":"source('synthea_raw', 'patients')","bound_to_node":"source","table":{"columns":["CITY","FIRST","GENDER","Id","LAST","SSN","STATE"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"999-99-9999","key":{"t":"str","v":"999-99-9999"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"CITY":"Boston","FIRST":"Ada","GENDER":"F","Id":"c0000000-0000-0000-0000-000000000001","LAST":"Lovelace","SSN":"999-99-9999","STATE":"Massachusetts"}],"format":"dict"}],"expected":{"table":{"columns":["city","first_name","gender","last_name","patient_id","ssn_hash","state"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"257cf696294931925796925d89aa0afb","key":{"t":"str","v":"257cf696294931925796925d89aa0afb"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"city":"Boston","first_name":"Ada","gender":"F","last_name":"Lovelace","patient_id":"c0000000-0000-0000-0000-000000000001","ssn_hash":"257cf696294931925796925d89aa0afb","state":"Massachusetts"}],"format":"dict","column_meta":{"city":{"description":"City of residence"},"first_name":{"description":"Patient's first name","tests":[{"name":"not null"}]},"gender":{"description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},"last_name":{"description":"Patient's last name","tests":[{"name":"not null"}]},"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"ssn_hash":{"description":"MD5 hash of Social Security Number (masked for privacy)"},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}}],"is_recursive":false},{"name":"stg_synthea__payers","path":"models/staging/synthea/stg_synthea__payers.sql","description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__payers.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as payer_id\n\n        -- attributes\n        , NAME as payer_name\n        , ADDRESS as address\n        , CITY as city\n        , STATE_HEADQUARTERED as state\n        , ZIP as zip_code\n        , PHONE as phone\n\n        -- financial metrics\n        , AMOUNT_COVERED as amount_covered\n        , AMOUNT_UNCOVERED as amount_uncovered\n        , REVENUE as revenue\n\n        -- utilization metrics\n        , COVERED_ENCOUNTERS as covered_encounters\n        , UNCOVERED_ENCOUNTERS as uncovered_encounters\n        , COVERED_MEDICATIONS as covered_medications\n        , UNCOVERED_MEDICATIONS as uncovered_medications\n        , COVERED_PROCEDURES as covered_procedures\n        , UNCOVERED_PROCEDURES as uncovered_procedures\n        , COVERED_IMMUNIZATIONS as covered_immunizations\n        , UNCOVERED_IMMUNIZATIONS as uncovered_immunizations\n\n        -- member metrics\n        , UNIQUE_CUSTOMERS as unique_customers\n        , QOLS_AVG as average_quality_of_life_score\n        , MEMBER_MONTHS as member_months\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"payers\"\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'payers') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as payer_id\n\n        -- attributes\n        , NAME as payer_name\n        , ADDRESS as address\n        , CITY as city\n        , STATE_HEADQUARTERED as state\n        , ZIP as zip_code\n        , PHONE as phone\n\n        -- financial metrics\n        , AMOUNT_COVERED as amount_covered\n        , AMOUNT_UNCOVERED as amount_uncovered\n        , REVENUE as revenue\n\n        -- utilization metrics\n        , COVERED_ENCOUNTERS as covered_encounters\n        , UNCOVERED_ENCOUNTERS as uncovered_encounters\n        , COVERED_MEDICATIONS as covered_medications\n        , UNCOVERED_MEDICATIONS as uncovered_medications\n        , COVERED_PROCEDURES as covered_procedures\n        , UNCOVERED_PROCEDURES as uncovered_procedures\n        , COVERED_IMMUNIZATIONS as covered_immunizations\n        , UNCOVERED_IMMUNIZATIONS as uncovered_immunizations\n\n        -- member metrics\n        , UNIQUE_CUSTOMERS as unique_customers\n        , QOLS_AVG as average_quality_of_life_score\n        , MEMBER_MONTHS as member_months\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__procedures","path":"models/staging/synthea/stg_synthea__procedures.sql","description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_surrogate_key","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__procedures.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_surrogate_key","edge_type":"from"},{"from":"with_surrogate_key","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)","renamed":"renamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamp\n        , cast(DATE as timestamp) as procedure_at\n\n        -- procedure details\n        , CODE as procedure_code\n        , DESCRIPTION as procedure_description\n        , BASE_COST as base_cost\n\n        -- reason for procedure\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"procedures\"\n)","with_surrogate_key":"with_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(procedure_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(procedure_at as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as procedure_id\n        , *\n    from renamed\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'procedures') }}\n),\n\nrenamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamp\n        , cast(DATE as timestamp) as procedure_at\n\n        -- procedure details\n        , CODE as procedure_code\n        , DESCRIPTION as procedure_description\n        , BASE_COST as base_cost\n\n        -- reason for procedure\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        {{ dbt_utils.generate_surrogate_key(['patient_id', 'encounter_id', 'procedure_code', 'procedure_at']) }} as procedure_id\n        , *\n    from renamed\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__providers","path":"models/staging/synthea/stg_synthea__providers.sql","description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__providers.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as provider_id\n\n        -- foreign key\n        , ORGANIZATION as organization_id\n\n        -- attributes\n        , NAME as provider_name\n        , GENDER as gender\n        , SPECIALITY as specialty\n\n        -- location\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , UTILIZATION as utilization\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"providers\"\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'providers') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as provider_id\n\n        -- foreign key\n        , ORGANIZATION as organization_id\n\n        -- attributes\n        , NAME as provider_name\n        , GENDER as gender\n        , SPECIALITY as specialty\n\n        -- location\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , UTILIZATION as utilization\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_encounter_summary","path":"models/marts/analytics/views/v_encounter_summary.sql","description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full 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","tests":[],"is_recursive":false},{"name":"v_patient_current_conditions","path":"models/marts/analytics/views/v_patient_current_conditions.sql","description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient 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","tests":[],"is_recursive":false},{"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","tests":[],"is_recursive":false}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). 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"}]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at <= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_dq_quarantine__medications":{"description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"end_after_1900_if_present","description":"Flag: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"no_future_medication_dates","description":"Flag: cast(medication_start_at as date) <= current_date"},{"name":"start_after_1900","description":"Flag: medication_start_at >= timestamp '1900-01-01'"},{"name":"valid_medication_dates","description":"Flag: medication_end_at is null or medication_end_at >= medication_start_at"}]},"int_encounters__enriched":{"description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","materialized":"view","columns":[{"name":"condition_count","description":"Number of conditions diagnosed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID), sourced from fct_encounters","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_count","description":"Number of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_count","description":"Number of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_condition_cost","description":"Total cost associated with conditions (currently always 0 per model logic)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_cost","description":"Total cost of all clinical events (conditions + medications + procedures). Note: Condition costs are currently set to 0 in the model.\n","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_count","description":"Total count of all clinical events (conditions + medications + procedures)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_medication_cost","description":"Total cost of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_procedure_cost","description":"Total cost of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"int_patients__with_conditions":{"description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","materialized":"view","columns":[{"name":"active_conditions","description":"Number of currently active conditions (no end date)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"chronic_condition_count","description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"first_condition_date","description":"Date of earliest condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"has_diabetes","description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_heart_disease","description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_hypertension","description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_condition_date","description":"Date of most recent condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_conditions","description":"Total number of conditions ever diagnosed for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__conditions":{"description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"condition_code","description":"SNOMED CT code for the condition","tests":[{"name":"not null"}]},{"name":"condition_description","description":"Description of the condition","tests":[{"name":"not null"}]},{"name":"condition_end_date","description":"Date when condition resolved (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"condition_id","description":"Surrogate key for the condition record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_start_date","description":"Date when condition was diagnosed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__observations":{"description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"encounter_id","description":"Foreign key to the encounter. Note: ~10% of observations have null encounter_id (from external sources or historical data imports).\n","tests":[{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"observation_at","description":"Timestamp when observation was recorded","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"observation_code","description":"LOINC code for the observation","tests":[{"name":"not null"}]},{"name":"observation_description","description":"Description of the observation","tests":[{"name":"not null"}]},{"name":"observation_id","description":"Surrogate key for the observation record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"observation_type","description":"Type of observation (numeric, text, etc.)"},{"name":"observation_value","description":"Value of the observation (stored as string)"},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"units","description":"Units of measurement"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__organizations":{"description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Unique organization identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__procedures":{"description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost of the procedure","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"procedure_at","description":"Timestamp when the procedure was performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_code","description":"SNOMED CT code for the procedure","tests":[{"name":"not null"}]},{"name":"procedure_description","description":"Description of the procedure","tests":[{"name":"not null"}]},{"name":"procedure_id","description":"Surrogate key for the procedure record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of the procedure"},{"name":"reason_description","description":"Description of the reason for the procedure"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__providers":{"description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Foreign key to the provider's organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"provider_id","description":"Unique provider identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key"],"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)"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
+<script type="application/json" id="cute-dbt-data">{"baseline":"","models":[{"name":"dim_conditions","path":"models/marts/analytics/dim_conditions.sql","description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"condition_aggregates","role":"transform"},{"id":"deduped","role":"transform"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_conditions.sql","role":"final"}],"edges":[{"from":"conditions_source","to":"condition_aggregates","edge_type":"from"},{"from":"condition_aggregates","to":"deduped","edge_type":"from"},{"from":"deduped","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_aggregates":"condition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n)","conditions_source":"conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n)","deduped":"deduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n)","final":"final as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n)","unknown_member":"unknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}]},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_date","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_date_date_key.0869fd48f9"]},"evidence":[{"label":"unique_key","value":"date_key"}],"pin_node":"(final select)"}]},{"name":"dim_organizations","path":"models/marts/core/dim_organizations.sql","description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_organizations.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='organization_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"]},"evidence":[{"label":"unique_key","value":"organization_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__organizations')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}"]}]},{"name":"dim_patients","path":"models/marts/core/dim_patients.sql","description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","dag":{"nodes":[{"id":"snapshot_source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_patients.sql","role":"final"}],"edges":[{"from":"snapshot_source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n)"},"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 <= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_patients","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__patients')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}]},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _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\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme 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"}],"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"}],"pin_node":"final"}]},{"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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_providers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"]},"evidence":[{"label":"unique_key","value":"provider_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_providers","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('dim_organizations'), ref('stg_synthea__providers')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. 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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_condition_cohorts","path":"models/marts/analytics/fct_condition_cohorts.sql","description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"patients","role":"import"},{"id":"dim_conditions","role":"import"},{"id":"condition_windows","role":"transform"},{"id":"with_dimensions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final 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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}]},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"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","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)"]}]},{"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","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)"}]},{"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 %}","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","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}},{"input":"ref('stg_synthea__encounters')","bound_to_node":"source","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]},{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0},{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"patient_id":{"description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}}}],"expected":{"table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"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","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)"}]},{"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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_yearly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_actual, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"}]},{"name":"fct_patient_summary","path":"models/marts/analytics/fct_patient_summary.sql","description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health 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","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)"}]},{"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","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"}],"pin_node":"(final select)"}]},{"name":"int_dq_quarantine__encounters","path":"models/intermediate/quarantine/int_dq_quarantine__encounters.sql","description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","dag":{"nodes":[],"edges":[]},"compiled_sql":{"int_dq_quarantine__encounters":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__encounters',\n    description='Encounters quarantined due to data quality violations'\n) }}\n\n-- cute-dbt fixture: synthetic body mod","tests":[],"is_recursive":false},{"name":"int_dq_quarantine__medications","path":"models/intermediate/quarantine/int_dq_quarantine__medications.sql","description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","dag":{"nodes":[],"edges":[]},"compiled_sql":{"int_dq_quarantine__medications":"\n\n\n\n-- Medications quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__medications\"\nwhere is_dq_valid = false\n"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__medications',\n    description='Medications quarantined due to data quality violations'\n) }}","tests":[],"is_recursive":false},{"name":"int_encounters__enriched","path":"models/intermediate/healthcare/int_encounters__enriched.sql","description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"conditions","role":"import"},{"id":"medications","role":"import"},{"id":"procedures","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_encounters__enriched.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"conditions","to":"final","edge_type":"left"},{"from":"medications","to":"final","edge_type":"left"},{"from":"procedures","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","conditions":"conditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    group by encounter_id\n)","encounters":"encounters as (\n    select\n        encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","medications":"medications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n    \n    \n    where is_dq_valid = true\n    \n\n    group by encounter_id\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"raw_sql":"{{\n    config(\n        materialized='view'\n    )\n}}\n\nwith encounters as (\n    select\n        encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nconditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from {{ ref('stg_synthea__conditions') }}\n    group by encounter_id\n),\n\nmedications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from {{ ref('stg_synthea__medications') }}\n    {{ quarantine_filter() }}\n    group by encounter_id\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nfinal as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"int_patients__never_admitted":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"int_patients__with_conditions","path":"models/intermediate/healthcare/int_patients__with_conditions.sql","description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","dag":{"nodes":[{"id":"patients","role":"import"},{"id":"conditions","role":"import"},{"id":"condition_stats","role":"transform"},{"id":"chronic_flags","role":"transform"},{"id":"chronic_count","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_patients__with_conditions.sql","role":"final"}],"edges":[{"from":"conditions","to":"condition_stats","edge_type":"from"},{"from":"conditions","to":"chronic_flags","edge_type":"from"},{"from":"chronic_flags","to":"chronic_count","edge_type":"from"},{"from":"patients","to":"final","edge_type":"from"},{"from":"condition_stats","to":"final","edge_type":"left"},{"from":"chronic_flags","to":"final","edge_type":"left"},{"from":"chronic_count","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","chronic_count":"chronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n)","chronic_flags":"chronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n)","condition_stats":"condition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n)","conditions":"conditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","final":"final as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)","patients":"patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)"},"raw_sql":"{{\n    config(\n        materialized='view'\n    )\n}}\n\n{#\n    Patient condition history with chronic disease flags.\n\n    IMPORTANT: Chronic Condition Detection Limitations\n    --------------------------------------------------\n    The chronic condition flags (has_diabetes, has_hypertension, has_heart_disease)\n    use simplified text matching on condition_description. This is NOT clinical-grade\n    detection and should be used for analytics/reporting only, not clinical decisions.\n\n    Detection patterns:\n    - Diabetes: description contains 'diabetes'\n    - Hypertension: description contains 'hypertension' or 'high blood pressure'\n    - Heart disease: description contains 'heart disease', 'cardiac', 'coronary',\n                     or 'atrial fibrillation'\n\n    For production clinical use, consider:\n    1. Using SNOMED codes instead of text matching\n    2. Implementing ICD-10 code mappings\n    3. Consulting clinical informaticists for proper condition groupings\n    4. Moving patterns to a seed file or reference table for maintainability\n#}\n\nwith patients as (\n    select\n        patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from {{ ref('stg_synthea__conditions') }}\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.int_patients__with_conditions","construct":"left_join[final:condition_stats]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"final — LEFT JOIN condition_stats ON patients.patient_id = condition_stats.patient_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"final","sketches":["- input: ref('dim_patients')\n  rows:\n    - {patient_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__conditions')\n  rows:\n    - {patient_id: 1}\n# expect: the no-match row with NULL stg_synthea__conditions columns (or the intended fallback)"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","rows":null,"format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv"}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , 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    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref('stg_synthea__medications')","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. 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","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"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}]},{"name":"stg_synthea__conditions","path":"models/staging/synthea/stg_synthea__conditions.sql","description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_surrogate_key","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__conditions.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_surrogate_key","edge_type":"from"},{"from":"with_surrogate_key","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)","renamed":"renamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- dates\n        , START as condition_start_date\n        , STOP as condition_end_date\n\n        -- condition details\n        , CODE as condition_code\n        , DESCRIPTION as condition_description\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"conditions\"\n)","with_surrogate_key":"with_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_start_date as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as condition_id\n        , *\n    from renamed\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'conditions') }}\n),\n\nrenamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- dates\n        , START as condition_start_date\n        , STOP as condition_end_date\n\n        -- condition details\n        , CODE as condition_code\n        , DESCRIPTION as condition_description\n    from source\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        {{ dbt_utils.generate_surrogate_key(['patient_id', 'encounter_id', 'condition_code', 'condition_start_date']) }} as condition_id\n        , *\n    from renamed\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__encounters","path":"models/staging/synthea/stg_synthea__encounters.sql","description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_dq_flags","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__encounters.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_dq_flags","edge_type":"from"},{"from":"with_dq_flags","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_dq_flags\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as encounter_id\n\n        -- foreign keys\n        , PATIENT as patient_id\n        , ORGANIZATION as organization_id\n        , PROVIDER as provider_id\n        , PAYER as payer_id\n\n        -- timestamps\n        , cast(START as timestamp) as encounter_start_at\n        , cast(STOP as timestamp) as encounter_end_at\n\n        -- attributes\n        , ENCOUNTERCLASS as encounter_class\n        , CODE as encounter_code\n        , DESCRIPTION as encounter_description\n\n        -- costs\n        , BASE_ENCOUNTER_COST as base_encounter_cost\n        , TOTAL_CLAIM_COST as total_claim_cost\n        , PAYER_COVERAGE as payer_coverage\n\n        -- reason for visit\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"encounters\"\n)","with_dq_flags":"with_dq_flags as (\n    \n    select\n        *\n\n        -- individual validation flags\n        \n        , (encounter_end_at >= encounter_start_at) as valid_encounter_timestamps\n        \n        , (encounter_start_at <= current_timestamp) as no_future_encounter_dates\n        \n        , (encounter_end_at >= timestamp '1900-01-01') as end_after_1900\n        \n        , (encounter_start_at >= timestamp '1900-01-01') as start_after_1900\n        \n\n        -- overall validity flag\n        , \n    (\n        \n        (encounter_end_at >= encounter_start_at)\n         and \n        \n        (encounter_start_at <= current_timestamp)\n         and \n        \n        (encounter_end_at >= timestamp '1900-01-01')\n         and \n        \n        (encounter_start_at >= timestamp '1900-01-01')\n        \n        \n    )\n as is_dq_valid\n\n        -- array of failed test names\n        , \n    list_value(\n        \n        case when not (encounter_end_at >= encounter_start_at) then 'valid_encounter_timestamps' else null end\n        , \n        \n        case when not (encounter_start_at <= current_timestamp) then 'no_future_encounter_dates' else null end\n        , \n        \n        case when not (encounter_end_at >= timestamp '1900-01-01') then 'end_after_1900' else null end\n        , \n        \n        case when not (encounter_start_at >= timestamp '1900-01-01') then 'start_after_1900' else null end\n        \n        \n    )\n as failed_dq_tests\n\n    from renamed\n\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'encounters') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as encounter_id\n\n        -- foreign keys\n        , PATIENT as patient_id\n        , ORGANIZATION as organization_id\n        , PROVIDER as provider_id\n        , PAYER as payer_id\n\n        -- timestamps\n        , cast(START as timestamp) as encounter_start_at\n        , cast(STOP as timestamp) as encounter_end_at\n\n        -- attributes\n        , ENCOUNTERCLASS as encounter_class\n        , CODE as encounter_code\n        , DESCRIPTION as encounter_description\n\n        -- costs\n        , BASE_ENCOUNTER_COST as base_encounter_cost\n        , TOTAL_CLAIM_COST as total_claim_cost\n        , PAYER_COVERAGE as payer_coverage\n\n        -- reason for visit\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n),\n\nwith_dq_flags as (\n    {{ add_dq_flags(\n        source_cte='renamed',\n        validations={\n            'valid_encounter_timestamps': 'encounter_end_at >= encounter_start_at',\n            'no_future_encounter_dates': 'encounter_start_at <= current_timestamp',\n            'end_after_1900': 'encounter_end_at >= timestamp \\'1900-01-01\\'',\n            'start_after_1900': 'encounter_start_at >= timestamp \\'1900-01-01\\''\n        }\n    ) }}\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_dq_flags\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__medications","path":"models/staging/synthea/stg_synthea__medications.sql","description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_row_number","role":"transform"},{"id":"with_surrogate_key","role":"transform"},{"id":"with_dq_flags","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__medications.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_row_number","edge_type":"from"},{"from":"with_row_number","to":"with_surrogate_key","edge_type":"from"},{"from":"with_surrogate_key","to":"with_dq_flags","edge_type":"from"},{"from":"with_dq_flags","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_dq_flags\n)","renamed":"renamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , PAYER as payer_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamps\n        , cast(START as timestamp) as medication_start_at\n        , cast(STOP as timestamp) as medication_end_at\n\n        -- medication details\n        , CODE as medication_code\n        , DESCRIPTION as medication_description\n\n        -- costs\n        , BASE_COST as base_cost\n        , PAYER_COVERAGE as payer_coverage\n        , DISPENSES as dispenses\n        , TOTALCOST as total_cost\n\n        -- reason for medication\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"medications\"\n)","with_dq_flags":"with_dq_flags as (\n    \n    select\n        *\n\n        -- individual validation flags\n        \n        , (medication_end_at is null or medication_end_at >= medication_start_at) as valid_medication_dates\n        \n        , (cast(medication_start_at as date) <= current_date) as no_future_medication_dates\n        \n        , (medication_start_at >= timestamp '1900-01-01') as start_after_1900\n        \n        , (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') as end_after_1900_if_present\n        \n\n        -- overall validity flag\n        , \n    (\n        \n        (medication_end_at is null or medication_end_at >= medication_start_at)\n         and \n        \n        (cast(medication_start_at as date) <= current_date)\n         and \n        \n        (medication_start_at >= timestamp '1900-01-01')\n         and \n        \n        (medication_end_at is null or medication_end_at >= timestamp '1900-01-01')\n        \n        \n    )\n as is_dq_valid\n\n        -- array of failed test names\n        , \n    list_value(\n        \n        case when not (medication_end_at is null or medication_end_at >= medication_start_at) then 'valid_medication_dates' else null end\n        , \n        \n        case when not (cast(medication_start_at as date) <= current_date) then 'no_future_medication_dates' else null end\n        , \n        \n        case when not (medication_start_at >= timestamp '1900-01-01') then 'start_after_1900' else null end\n        , \n        \n        case when not (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') then 'end_after_1900_if_present' else null end\n        \n        \n    )\n as failed_dq_tests\n\n    from with_surrogate_key\n\n)","with_row_number":"with_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, medication_code, medication_start_at\n            order by medication_end_at asc nulls last, total_cost desc\n        ) as _row_num\n    from renamed\n)","with_surrogate_key":"with_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(medication_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(medication_start_at as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(_row_num as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as medication_id\n        , patient_id\n        , payer_id\n        , encounter_id\n        , medication_start_at\n        , medication_end_at\n        , medication_code\n        , medication_description\n        , base_cost\n        , payer_coverage\n        , dispenses\n        , total_cost\n        , reason_code\n        , reason_description\n    from with_row_number\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'medications') }}\n),\n\nrenamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , PAYER as payer_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamps\n        , cast(START as timestamp) as medication_start_at\n        , cast(STOP as timestamp) as medication_end_at\n\n        -- medication details\n        , CODE as medication_code\n        , DESCRIPTION as medication_description\n\n        -- costs\n        , BASE_COST as base_cost\n        , PAYER_COVERAGE as payer_coverage\n        , DISPENSES as dispenses\n        , TOTALCOST as total_cost\n\n        -- reason for medication\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n),\n\nwith_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, medication_code, medication_start_at\n            order by medication_end_at asc nulls last, total_cost desc\n        ) as _row_num\n    from renamed\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        {{ dbt_utils.generate_surrogate_key(['patient_id', 'encounter_id', 'medication_code', 'medication_start_at', '_row_num']) }} as medication_id\n        , patient_id\n        , payer_id\n        , encounter_id\n        , medication_start_at\n        , medication_end_at\n        , medication_code\n        , medication_description\n        , base_cost\n        , payer_coverage\n        , dispenses\n        , total_cost\n        , reason_code\n        , reason_description\n    from with_row_number\n),\n\nwith_dq_flags as (\n    {{ add_dq_flags(\n        source_cte='with_surrogate_key',\n        validations={\n            'valid_medication_dates': 'medication_end_at is null or medication_end_at >= medication_start_at',\n            'no_future_medication_dates': 'cast(medication_start_at as date) <= current_date',\n            'start_after_1900': 'medication_start_at >= timestamp \\'1900-01-01\\'',\n            'end_after_1900_if_present': 'medication_end_at is null or medication_end_at >= timestamp \\'1900-01-01\\''\n        }\n    ) }}\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_dq_flags\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__observations","path":"models/staging/synthea/stg_synthea__observations.sql","description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_row_number","role":"transform"},{"id":"with_surrogate_key","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__observations.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_row_number","edge_type":"from"},{"from":"with_row_number","to":"with_surrogate_key","edge_type":"from"},{"from":"with_surrogate_key","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)","renamed":"renamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamp\n        , cast(DATE as timestamp) as observation_at\n\n        -- observation details\n        , CODE as observation_code\n        , DESCRIPTION as observation_description\n        , VALUE as observation_value\n        , UNITS as units\n        , TYPE as observation_type\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"observations\"\n)","with_row_number":"with_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, observation_code, observation_at\n            order by observation_value, observation_description\n        ) as _row_num\n    from renamed\n)","with_surrogate_key":"with_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(observation_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(observation_at as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(_row_num as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as observation_id\n        , patient_id\n        , encounter_id\n        , observation_at\n        , observation_code\n        , observation_description\n        , observation_value\n        , units\n        , observation_type\n    from with_row_number\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'observations') }}\n),\n\nrenamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamp\n        , cast(DATE as timestamp) as observation_at\n\n        -- observation details\n        , CODE as observation_code\n        , DESCRIPTION as observation_description\n        , VALUE as observation_value\n        , UNITS as units\n        , TYPE as observation_type\n    from source\n),\n\nwith_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, observation_code, observation_at\n            order by observation_value, observation_description\n        ) as _row_num\n    from renamed\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        {{ dbt_utils.generate_surrogate_key(['patient_id', 'encounter_id', 'observation_code', 'observation_at', '_row_num']) }} as observation_id\n        , patient_id\n        , encounter_id\n        , observation_at\n        , observation_code\n        , observation_description\n        , observation_value\n        , units\n        , observation_type\n    from with_row_number\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__organizations","path":"models/staging/synthea/stg_synthea__organizations.sql","description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__organizations.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as organization_id\n\n        -- attributes\n        , NAME as organization_name\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , PHONE as phone\n\n        -- location\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , REVENUE as revenue\n        , UTILIZATION as utilization\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"organizations\"\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'organizations') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as organization_id\n\n        -- attributes\n        , NAME as organization_name\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , PHONE as phone\n\n        -- location\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , REVENUE as revenue\n        , UTILIZATION as utilization\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__patients","path":"models/staging/synthea/stg_synthea__patients.sql","description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__patients.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"patients\"\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'patients') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.stg_synthea__patients.test_stg_synthea__patients_renames_and_hashes_source_columns","name":"test_stg_synthea__patients_renames_and_hashes_source_columns","target_model":"stg_synthea__patients","changed":false,"description":"stg_synthea__patients renames the raw Synthea UPPERCASE columns to\nsnake_case and hashes the SSN with md5. The given mocks the\nsynthea_raw.patients SOURCE directly (inline dict rows) — the only\nunit test in this project whose input is a source() rather than a\nref() (cute-dbt#57). Dict expect compares listed columns only, so\nthe non-deterministic _loaded_at (current_timestamp) is excluded\nfrom assertion automatically.\n","tags":[],"meta":{},"defined_in":"models/staging/synthea/_synthea__models.yml","given":[{"input":"source('synthea_raw', 'patients')","bound_to_node":"source","table":{"columns":["CITY","FIRST","GENDER","Id","LAST","SSN","STATE"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"999-99-9999","key":{"t":"str","v":"999-99-9999"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"CITY":"Boston","FIRST":"Ada","GENDER":"F","Id":"c0000000-0000-0000-0000-000000000001","LAST":"Lovelace","SSN":"999-99-9999","STATE":"Massachusetts"}],"format":"dict","column_meta":{"Id":{"description":"Unique patient identifier (UUID)"}}}],"expected":{"table":{"columns":["city","first_name","gender","last_name","patient_id","ssn_hash","state"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"257cf696294931925796925d89aa0afb","key":{"t":"str","v":"257cf696294931925796925d89aa0afb"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"city":"Boston","first_name":"Ada","gender":"F","last_name":"Lovelace","patient_id":"c0000000-0000-0000-0000-000000000001","ssn_hash":"257cf696294931925796925d89aa0afb","state":"Massachusetts"}],"format":"dict","column_meta":{"city":{"description":"City of residence"},"first_name":{"description":"Patient's first name","tests":[{"name":"not null"}]},"gender":{"description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},"last_name":{"description":"Patient's last name","tests":[{"name":"not null"}]},"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"ssn_hash":{"description":"MD5 hash of Social Security Number (masked for privacy)"},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}}],"is_recursive":false},{"name":"stg_synthea__payers","path":"models/staging/synthea/stg_synthea__payers.sql","description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__payers.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as payer_id\n\n        -- attributes\n        , NAME as payer_name\n        , ADDRESS as address\n        , CITY as city\n        , STATE_HEADQUARTERED as state\n        , ZIP as zip_code\n        , PHONE as phone\n\n        -- financial metrics\n        , AMOUNT_COVERED as amount_covered\n        , AMOUNT_UNCOVERED as amount_uncovered\n        , REVENUE as revenue\n\n        -- utilization metrics\n        , COVERED_ENCOUNTERS as covered_encounters\n        , UNCOVERED_ENCOUNTERS as uncovered_encounters\n        , COVERED_MEDICATIONS as covered_medications\n        , UNCOVERED_MEDICATIONS as uncovered_medications\n        , COVERED_PROCEDURES as covered_procedures\n        , UNCOVERED_PROCEDURES as uncovered_procedures\n        , COVERED_IMMUNIZATIONS as covered_immunizations\n        , UNCOVERED_IMMUNIZATIONS as uncovered_immunizations\n\n        -- member metrics\n        , UNIQUE_CUSTOMERS as unique_customers\n        , QOLS_AVG as average_quality_of_life_score\n        , MEMBER_MONTHS as member_months\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"payers\"\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'payers') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as payer_id\n\n        -- attributes\n        , NAME as payer_name\n        , ADDRESS as address\n        , CITY as city\n        , STATE_HEADQUARTERED as state\n        , ZIP as zip_code\n        , PHONE as phone\n\n        -- financial metrics\n        , AMOUNT_COVERED as amount_covered\n        , AMOUNT_UNCOVERED as amount_uncovered\n        , REVENUE as revenue\n\n        -- utilization metrics\n        , COVERED_ENCOUNTERS as covered_encounters\n        , UNCOVERED_ENCOUNTERS as uncovered_encounters\n        , COVERED_MEDICATIONS as covered_medications\n        , UNCOVERED_MEDICATIONS as uncovered_medications\n        , COVERED_PROCEDURES as covered_procedures\n        , UNCOVERED_PROCEDURES as uncovered_procedures\n        , COVERED_IMMUNIZATIONS as covered_immunizations\n        , UNCOVERED_IMMUNIZATIONS as uncovered_immunizations\n\n        -- member metrics\n        , UNIQUE_CUSTOMERS as unique_customers\n        , QOLS_AVG as average_quality_of_life_score\n        , MEMBER_MONTHS as member_months\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__procedures","path":"models/staging/synthea/stg_synthea__procedures.sql","description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_surrogate_key","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__procedures.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_surrogate_key","edge_type":"from"},{"from":"with_surrogate_key","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)","renamed":"renamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamp\n        , cast(DATE as timestamp) as procedure_at\n\n        -- procedure details\n        , CODE as procedure_code\n        , DESCRIPTION as procedure_description\n        , BASE_COST as base_cost\n\n        -- reason for procedure\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"procedures\"\n)","with_surrogate_key":"with_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(procedure_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(procedure_at as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as procedure_id\n        , *\n    from renamed\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'procedures') }}\n),\n\nrenamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- timestamp\n        , cast(DATE as timestamp) as procedure_at\n\n        -- procedure details\n        , CODE as procedure_code\n        , DESCRIPTION as procedure_description\n        , BASE_COST as base_cost\n\n        -- reason for procedure\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        {{ dbt_utils.generate_surrogate_key(['patient_id', 'encounter_id', 'procedure_code', 'procedure_at']) }} as procedure_id\n        , *\n    from renamed\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"stg_synthea__providers","path":"models/staging/synthea/stg_synthea__providers.sql","description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__providers.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as provider_id\n\n        -- foreign key\n        , ORGANIZATION as organization_id\n\n        -- attributes\n        , NAME as provider_name\n        , GENDER as gender\n        , SPECIALITY as specialty\n\n        -- location\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , UTILIZATION as utilization\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"providers\"\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'providers') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as provider_id\n\n        -- foreign key\n        , ORGANIZATION as organization_id\n\n        -- attributes\n        , NAME as provider_name\n        , GENDER as gender\n        , SPECIALITY as specialty\n\n        -- location\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- metrics\n        , UTILIZATION as utilization\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_encounter_summary","path":"models/marts/analytics/views/v_encounter_summary.sql","description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full 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","tests":[],"is_recursive":false},{"name":"v_patient_current_conditions","path":"models/marts/analytics/views/v_patient_current_conditions.sql","description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient 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","tests":[],"is_recursive":false},{"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","tests":[],"is_recursive":false}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). 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"}]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at <= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_dq_quarantine__medications":{"description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"end_after_1900_if_present","description":"Flag: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"no_future_medication_dates","description":"Flag: cast(medication_start_at as date) <= current_date"},{"name":"start_after_1900","description":"Flag: medication_start_at >= timestamp '1900-01-01'"},{"name":"valid_medication_dates","description":"Flag: medication_end_at is null or medication_end_at >= medication_start_at"}]},"int_encounters__enriched":{"description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","materialized":"view","columns":[{"name":"condition_count","description":"Number of conditions diagnosed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID), sourced from fct_encounters","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_count","description":"Number of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_count","description":"Number of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_condition_cost","description":"Total cost associated with conditions (currently always 0 per model logic)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_cost","description":"Total cost of all clinical events (conditions + medications + procedures). Note: Condition costs are currently set to 0 in the model.\n","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_count","description":"Total count of all clinical events (conditions + medications + procedures)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_medication_cost","description":"Total cost of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_procedure_cost","description":"Total cost of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"int_patients__with_conditions":{"description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","materialized":"view","columns":[{"name":"active_conditions","description":"Number of currently active conditions (no end date)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"chronic_condition_count","description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"first_condition_date","description":"Date of earliest condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"has_diabetes","description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_heart_disease","description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_hypertension","description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_condition_date","description":"Date of most recent condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_conditions","description":"Total number of conditions ever diagnosed for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__conditions":{"description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"condition_code","description":"SNOMED CT code for the condition","tests":[{"name":"not null"}]},{"name":"condition_description","description":"Description of the condition","tests":[{"name":"not null"}]},{"name":"condition_end_date","description":"Date when condition resolved (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"condition_id","description":"Surrogate key for the condition record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_start_date","description":"Date when condition was diagnosed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__observations":{"description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"encounter_id","description":"Foreign key to the encounter. Note: ~10% of observations have null encounter_id (from external sources or historical data imports).\n","tests":[{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"observation_at","description":"Timestamp when observation was recorded","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"observation_code","description":"LOINC code for the observation","tests":[{"name":"not null"}]},{"name":"observation_description","description":"Description of the observation","tests":[{"name":"not null"}]},{"name":"observation_id","description":"Surrogate key for the observation record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"observation_type","description":"Type of observation (numeric, text, etc.)"},{"name":"observation_value","description":"Value of the observation (stored as string)"},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"units","description":"Units of measurement"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__organizations":{"description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Unique organization identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__procedures":{"description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost of the procedure","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"procedure_at","description":"Timestamp when the procedure was performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_code","description":"SNOMED CT code for the procedure","tests":[{"name":"not null"}]},{"name":"procedure_description","description":"Description of the procedure","tests":[{"name":"not null"}]},{"name":"procedure_id","description":"Surrogate key for the procedure record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of the procedure"},{"name":"reason_description","description":"Description of the reason for the procedure"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__providers":{"description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Foreign key to the provider's organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"provider_id","description":"Unique provider identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key"],"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)"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
 <script>
 
 /* cute-dbt explore tests viewer v1 (cute-dbt#102)
diff --git a/examples/jaffle-shop-report.html b/examples/jaffle-shop-report.html
index 03b0408c..f56dd90f 100644
--- a/examples/jaffle-shop-report.html
+++ b/examples/jaffle-shop-report.html
@@ -1542,11 +1542,16 @@
 .model-tooltip .ct-desc { font-size: 1.3em; line-height: 1.5; }
 .model-tooltip .mt-mat { font-size: 0.72rem; text-transform: uppercase; letter-spacing: 0.05em; color: #f0c674; border: 1px solid currentColor; border-radius: 999px; padding: 0.02rem 0.5rem; }
 .model-tooltip .mt-tag { font-size: 0.72rem; color: rgba(255,255,255,0.82); background: rgba(255,255,255,0.12); border-radius: 999px; padding: 0.02rem 0.5rem; }
-/* model-level tests inside the model-ref tip (the dark-context dress for
-   the shelf card's .dt-* chips). */
-.model-tooltip .mt-mtests { display: flex; flex-direction: column; gap: 0.3rem; margin-top: 0.3rem; }
-.model-tooltip .dt-key { color: color-mix(in oklab, var(--accent) 60%, white); }
-.model-tooltip .dt-val { background: rgba(255,255,255,0.14); color: rgba(255,255,255,0.92); }
+/* cute-dbt#235 — model-level tests inside the model-ref tip ride the
+   COLUMN tooltip's own .ct-test/.ct-key/.ct-vals anatomy (the shared
+   ctTestHtml builder — never a parallel chip system; the .mt-mtests/
+   .dt-* dress is retired here, the shelf card keeps its light-surface
+   .dt-* chips). Test names keep the readable-on-dark accent treatment
+   the retired .model-tooltip .dt-key carried; the value chips inherit
+   .col-tooltip .ct-val verbatim. The base .col-tooltip .ct-key color
+   is cute-dbt#233's lane — only this model-scoped override lives here
+   (same specificity, later in file, so it wins inside the model tip). */
+.model-tooltip .ct-key { color: color-mix(in oklab, var(--accent) 60%, white); }
 .ov-tooltip .ov-grp + .ov-grp { margin-top: 0.55rem; }
 .ov-tooltip .ov-grp-name { font-family: var(--font-mono); font-size: 0.78em; text-transform: uppercase; letter-spacing: 0.06em; opacity: 0.6; margin-bottom: 0.25rem; }
 .ov-tooltip .ov-row { display: flex; gap: 0.5rem; align-items: baseline; padding-left: 0.45rem; white-space: nowrap; }
@@ -7032,13 +7037,13 @@ <h2>Expected</h2>
       h += "</div>";
     }
     if (info.description) h += '<div class="ct-desc">' + escapeHtml(info.description) + "</div>";
+    // cute-dbt#235 — model tests ride the column tooltip's own
+    // .ct-test/.ct-key/.ct-vals anatomy (the shared ctTestHtml builder),
+    // so the model tip and the column tip stay visually consistent.
+    // The shelf card keeps its light-surface .dt-* chips (testChipsHtml).
     if (info.model_tests && info.model_tests.length) {
       h += '<div class="ct-tests-label">Model tests</div>';
-      h += '<div class="mt-mtests">';
-      info.model_tests.forEach(function (tt) {
-        h += '<div class="dt-row">' + testChipsHtml(tt) + "</div>";
-      });
-      h += "</div>";
+      h += '<div class="ct-tests">' + info.model_tests.map(ctTestHtml).join("") + "</div>";
     }
     el.innerHTML = h;
     el.hidden = false;
@@ -8312,6 +8317,31 @@ <h2>Expected</h2>
               : t.detail ? " " + t.detail : "";
     return t.name + extra;
   }
+  // One test row in the shared tooltip anatomy: accent `.ct-key` name +
+  // one `.ct-val` chip per argument. cute-dbt#202 — a multi-arg detail
+  // splits into one chip per argument (the ct-vals/ct-val dress the
+  // values branch uses; the ct-detail run is retired); the shipped
+  // single-value detail forms — relationships `m.f`, accepted_range
+  // `0–100` — carry no comma, so they stay one chip. cute-dbt#235 —
+  // shared by the column tooltip AND the model-ref tip's model-test
+  // rows, so the two bubbles cannot drift apart visually. Every
+  // interpolation passes through escapeHtml (payload strings never
+  // parse as HTML).
+  function ctTestHtml(t) {
+    var inner = '<span class="ct-key">' + escapeHtml(t.name) + "</span>";
+    if (t.values && t.values.length) {
+      inner += '<span class="ct-vals">'
+        + t.values.map(function (v) { return '<span class="ct-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
+        + "</span>";
+    } else if (t.detail) {
+      inner += '<span class="ct-vals">'
+        + String(t.detail).split(/,\s*/).map(function (a) {
+            return '<span class="ct-val">' + escapeHtml(a) + "</span>";
+          }).join("")
+        + "</span>";
+    }
+    return '<div class="ct-test">' + inner + "</div>";
+  }
   function decorateColHeader($th, name, meta) {
     if (!meta || (!meta.description && !(meta.tests && meta.tests.length))) return;
     var tests = meta.tests || [];
@@ -8347,26 +8377,7 @@ <h2>Expected</h2>
     var h = '<div class="ct-desc">' + escapeHtml(desc || name) + "</div>";
     if (tests.length) {
       h += '<div class="ct-tests-label">Data tests</div><div class="ct-tests">'
-         + tests.map(function (t) {
-             var inner = '<span class="ct-key">' + escapeHtml(t.name) + "</span>";
-             if (t.values && t.values.length) {
-               inner += '<span class="ct-vals">'
-                 + t.values.map(function (v) { return '<span class="ct-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
-                 + "</span>";
-             } else if (t.detail) {
-               // cute-dbt#202 — a multi-arg detail splits into one chip per
-               // argument (the ct-vals/ct-val dress the values branch uses;
-               // the ct-detail run is retired). The shipped detail forms —
-               // relationships `m.f`, accepted_range `0–100` — carry no
-               // comma, so a single-value detail stays one chip.
-               inner += '<span class="ct-vals">'
-                 + String(t.detail).split(/,\s*/).map(function (a) {
-                     return '<span class="ct-val">' + escapeHtml(a) + "</span>";
-                   }).join("")
-                 + "</span>";
-             }
-             return '<div class="ct-test">' + inner + "</div>";
-           }).join("")
+         + tests.map(ctTestHtml).join("")
          + "</div>";
     }
     el.innerHTML = h;
diff --git a/examples/playground-report.html b/examples/playground-report.html
index cf75ef2e..b73b2b5d 100644
--- a/examples/playground-report.html
+++ b/examples/playground-report.html
@@ -1542,11 +1542,16 @@
 .model-tooltip .ct-desc { font-size: 1.3em; line-height: 1.5; }
 .model-tooltip .mt-mat { font-size: 0.72rem; text-transform: uppercase; letter-spacing: 0.05em; color: #f0c674; border: 1px solid currentColor; border-radius: 999px; padding: 0.02rem 0.5rem; }
 .model-tooltip .mt-tag { font-size: 0.72rem; color: rgba(255,255,255,0.82); background: rgba(255,255,255,0.12); border-radius: 999px; padding: 0.02rem 0.5rem; }
-/* model-level tests inside the model-ref tip (the dark-context dress for
-   the shelf card's .dt-* chips). */
-.model-tooltip .mt-mtests { display: flex; flex-direction: column; gap: 0.3rem; margin-top: 0.3rem; }
-.model-tooltip .dt-key { color: color-mix(in oklab, var(--accent) 60%, white); }
-.model-tooltip .dt-val { background: rgba(255,255,255,0.14); color: rgba(255,255,255,0.92); }
+/* cute-dbt#235 — model-level tests inside the model-ref tip ride the
+   COLUMN tooltip's own .ct-test/.ct-key/.ct-vals anatomy (the shared
+   ctTestHtml builder — never a parallel chip system; the .mt-mtests/
+   .dt-* dress is retired here, the shelf card keeps its light-surface
+   .dt-* chips). Test names keep the readable-on-dark accent treatment
+   the retired .model-tooltip .dt-key carried; the value chips inherit
+   .col-tooltip .ct-val verbatim. The base .col-tooltip .ct-key color
+   is cute-dbt#233's lane — only this model-scoped override lives here
+   (same specificity, later in file, so it wins inside the model tip). */
+.model-tooltip .ct-key { color: color-mix(in oklab, var(--accent) 60%, white); }
 .ov-tooltip .ov-grp + .ov-grp { margin-top: 0.55rem; }
 .ov-tooltip .ov-grp-name { font-family: var(--font-mono); font-size: 0.78em; text-transform: uppercase; letter-spacing: 0.06em; opacity: 0.6; margin-bottom: 0.25rem; }
 .ov-tooltip .ov-row { display: flex; gap: 0.5rem; align-items: baseline; padding-left: 0.45rem; white-space: nowrap; }
@@ -1988,7 +1993,7 @@ <h2>Expected</h2>
 
 </div>
 
-<script type="application/json" id="cute-dbt-data">{"baseline":"tests/fixtures/playground-baseline.json","models":[{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","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"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_payers","construct":"union[final]","verdict":{"status":"unknown"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"suppressed":{"source":"pragma","reason":"unknown_member is a constant sentinel arm, exercised by design"},"pin_node":"final"}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","dag":{"nodes":[{"id":"valid_encounters","role":"import"},{"id":"conditions","role":"import"},{"id":"medications","role":"import"},{"id":"procedures","role":"import"},{"id":"all_events","role":"transform"},{"id":"patients","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_clinical_events.sql","role":"final"}],"edges":[{"from":"conditions","to":"all_events","edge_type":"union_all"},{"from":"medications","to":"all_events","edge_type":"union_all"},{"from":"procedures","to":"all_events","edge_type":"union_all"},{"from":"all_events","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"encounters","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_encounters_incremental","path":"models/marts/core/fct_encounters_incremental.sql","description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","dag":{"nodes":[{"id":"source","role":"import"},{"id":"(final select)","label":"fct_encounters_incremental.sql","role":"final"}],"edges":[{"from":"source","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)"},"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 %}","tests":[{"id":"unit_test.healthcare_analytics.fct_encounters_incremental.test_fct_encounters_incremental_appends_new_encounters","name":"test_fct_encounters_incremental_appends_new_encounters","target_model":"fct_encounters_incremental","changed":true,"description":"fct_encounters_incremental appends encounters newer than the latest loaded start_time. With is_incremental() true only the new encounter is merged; Expected is that single inserted row (the materialization delta), not the resulting table.","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt#145 — an incremental-model unit test. `overrides.macros.\n  # is_incremental: true` drives the model's is_incremental() branch,\n  # `given: - input: this` supplies the model's prior state, and Expected is\n  # the rows MERGED/INSERTED by the run — not the final table after the merge.\n  - name: test_fct_encounters_incremental_appends_new_encounters\n    description: |\n      fct_encounters_incremental appends encounters newer than the latest\n      already-loaded start_time. With is_incremental() true, only the new\n      encounter (e2) is merged; the prior row (e1, supplied via `this`) is\n      untouched, so Expected is the single inserted row — the materialization\n      delta, not the resulting table.\n    model: fct_encounters_incremental\n    overrides:\n      macros:\n        is_incremental: true\n    given:\n      - input: this\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n      - input: ref('stg_synthea__encounters')\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n          - encounter_id: 'e2'\n            patient_id: 'p2'\n            encounter_type: 'emergency'\n            start_time: '2025-02-01 14:30:00'\n            total_cost: 500.0\n    expect:\n      format: dict\n      rows:\n        - encounter_id: 'e2'\n          patient_id: 'p2'\n          encounter_type: 'emergency'\n          start_time: '2025-02-01 14:30:00'\n          total_cost: 500.0","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}},{"input":"ref('stg_synthea__encounters')","bound_to_node":"source","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]},{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0},{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"patient_id":{"description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}}}],"expected":{"table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"int_dq_quarantine__encounters","path":"models/intermediate/quarantine/int_dq_quarantine__encounters.sql","description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","dag":{"nodes":[],"edges":[]},"compiled_sql":{"int_dq_quarantine__encounters":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__encounters',\n    description='Encounters quarantined due to data quality violations'\n) }}\n\n-- cute-dbt fixture: synthetic body mod","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"int_patients__never_admitted":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # Cross-join demo (cute-dbt#64): mart_date_state_grid's CROSS JOIN\n  # CTE gives cute-dbt's renderer a real cross-join edge to draw. This\n  # test also mixes given formats (dict + csv) in a single unit_test.\n  - name: test_mart_date_state_grid_cross_joins_months_and_states\n    description: |\n      mart_date_state_grid CROSS JOINs the distinct calendar months from\n      dim_date with the distinct non-null patient states from staging.\n      Three daily dim_date rows collapse to two months; four patient\n      rows collapse to two states (one NULL filtered out, one duplicate\n      deduplicated). The result is the full 2 x 2 cartesian product —\n      four grid rows. Mixes a dict given (dim_date) with a csv given\n      (patients) in one test.\n    model: mart_date_state_grid\n    given:\n      - input: ref('dim_date')\n        format: dict\n        rows:\n          - date_actual: 2024-01-15\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-01-16\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-02-01\n            year_actual: 2024\n            month_actual: 2\n            month_name: 'February'\n      - input: ref('stg_synthea__patients')\n        format: csv\n        fixture: mart_date_state_grid_patients\n    expect:\n      format: csv\n      rows: |\n        month_key,year_actual,month_actual,month_name,state\n        202401,2024,1,January,Massachusetts\n        202401,2024,1,January,Connecticut\n        202402,2024,2,February,Massachusetts\n        202402,2024,2,February,Connecticut","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","table":{"columns":["patient_id","state"],"rows":[{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},{"display":"","key":{"t":"null"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":"patient_id,state\na00000aa-0000-0000-0000-000000000001,Massachusetts\na00000aa-0000-0000-0000-000000000002,Connecticut\na00000aa-0000-0000-0000-000000000003,\na00000aa-0000-0000-0000-000000000004,Massachusetts\n","format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv","column_meta":{"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , 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    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","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":false,"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).","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"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}]},{"name":"stg_synthea__patients","path":"models/staging/synthea/stg_synthea__patients.sql","description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__patients.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"patients\"\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'patients') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.stg_synthea__patients.test_stg_synthea__patients_renames_and_hashes_source_columns","name":"test_stg_synthea__patients_renames_and_hashes_source_columns","target_model":"stg_synthea__patients","changed":false,"description":"stg_synthea__patients renames the raw Synthea UPPERCASE columns to\nsnake_case and hashes the SSN with md5. The given mocks the\nsynthea_raw.patients SOURCE directly (inline dict rows) — the only\nunit test in this project whose input is a source() rather than a\nref() (cute-dbt#57). Dict expect compares listed columns only, so\nthe non-deterministic _loaded_at (current_timestamp) is excluded\nfrom assertion automatically.\n","tags":[],"meta":{},"defined_in":"models/staging/synthea/_synthea__models.yml","authoring_yaml":"  - name: test_stg_synthea__patients_renames_and_hashes_source_columns\n    description: |\n      stg_synthea__patients renames the raw Synthea UPPERCASE columns to\n      snake_case and hashes the SSN with md5. The given mocks the\n      synthea_raw.patients SOURCE directly (inline dict rows) — the only\n      unit test in this project whose input is a source() rather than a\n      ref() (cute-dbt#57). Dict expect compares listed columns only, so\n      the non-deterministic _loaded_at (current_timestamp) is excluded\n      from assertion automatically.\n    model: stg_synthea__patients\n    given:\n      - input: source('synthea_raw', 'patients')\n        format: dict\n        rows:\n          - Id: 'c0000000-0000-0000-0000-000000000001'\n            FIRST: 'Ada'\n            LAST: 'Lovelace'\n            GENDER: 'F'\n            CITY: 'Boston'\n            STATE: 'Massachusetts'\n            SSN: '999-99-9999'\n    expect:\n      format: dict\n      rows:\n        - patient_id: 'c0000000-0000-0000-0000-000000000001'\n          first_name: 'Ada'\n          last_name: 'Lovelace'\n          gender: 'F'\n          city: 'Boston'\n          state: 'Massachusetts'\n          ssn_hash: '257cf696294931925796925d89aa0afb'","given":[{"input":"source('synthea_raw', 'patients')","bound_to_node":"source","table":{"columns":["CITY","FIRST","GENDER","Id","LAST","SSN","STATE"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"999-99-9999","key":{"t":"str","v":"999-99-9999"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"CITY":"Boston","FIRST":"Ada","GENDER":"F","Id":"c0000000-0000-0000-0000-000000000001","LAST":"Lovelace","SSN":"999-99-9999","STATE":"Massachusetts"}],"format":"dict"}],"expected":{"table":{"columns":["city","first_name","gender","last_name","patient_id","ssn_hash","state"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"257cf696294931925796925d89aa0afb","key":{"t":"str","v":"257cf696294931925796925d89aa0afb"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"city":"Boston","first_name":"Ada","gender":"F","last_name":"Lovelace","patient_id":"c0000000-0000-0000-0000-000000000001","ssn_hash":"257cf696294931925796925d89aa0afb","state":"Massachusetts"}],"format":"dict","column_meta":{"city":{"description":"City of residence"},"first_name":{"description":"Patient's first name","tests":[{"name":"not null"}]},"gender":{"description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},"last_name":{"description":"Patient's last name","tests":[{"name":"not null"}]},"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"ssn_hash":{"description":"MD5 hash of Social Security Number (masked for privacy)"},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}}],"is_recursive":false}],"manifest_nodes":{"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at \u003c= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key"],"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)"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
+<script type="application/json" id="cute-dbt-data">{"baseline":"tests/fixtures/playground-baseline.json","models":[{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","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"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_payers","construct":"union[final]","verdict":{"status":"unknown"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"suppressed":{"source":"pragma","reason":"unknown_member is a constant sentinel arm, exercised by design"},"pin_node":"final"}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","dag":{"nodes":[{"id":"valid_encounters","role":"import"},{"id":"conditions","role":"import"},{"id":"medications","role":"import"},{"id":"procedures","role":"import"},{"id":"all_events","role":"transform"},{"id":"patients","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_clinical_events.sql","role":"final"}],"edges":[{"from":"conditions","to":"all_events","edge_type":"union_all"},{"from":"medications","to":"all_events","edge_type":"union_all"},{"from":"procedures","to":"all_events","edge_type":"union_all"},{"from":"all_events","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"encounters","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"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","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_encounters_incremental","path":"models/marts/core/fct_encounters_incremental.sql","description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","dag":{"nodes":[{"id":"source","role":"import"},{"id":"(final select)","label":"fct_encounters_incremental.sql","role":"final"}],"edges":[{"from":"source","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)"},"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 %}","tests":[{"id":"unit_test.healthcare_analytics.fct_encounters_incremental.test_fct_encounters_incremental_appends_new_encounters","name":"test_fct_encounters_incremental_appends_new_encounters","target_model":"fct_encounters_incremental","changed":true,"description":"fct_encounters_incremental appends encounters newer than the latest loaded start_time. With is_incremental() true only the new encounter is merged; Expected is that single inserted row (the materialization delta), not the resulting table.","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt#145 — an incremental-model unit test. `overrides.macros.\n  # is_incremental: true` drives the model's is_incremental() branch,\n  # `given: - input: this` supplies the model's prior state, and Expected is\n  # the rows MERGED/INSERTED by the run — not the final table after the merge.\n  - name: test_fct_encounters_incremental_appends_new_encounters\n    description: |\n      fct_encounters_incremental appends encounters newer than the latest\n      already-loaded start_time. With is_incremental() true, only the new\n      encounter (e2) is merged; the prior row (e1, supplied via `this`) is\n      untouched, so Expected is the single inserted row — the materialization\n      delta, not the resulting table.\n    model: fct_encounters_incremental\n    overrides:\n      macros:\n        is_incremental: true\n    given:\n      - input: this\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n      - input: ref('stg_synthea__encounters')\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n          - encounter_id: 'e2'\n            patient_id: 'p2'\n            encounter_type: 'emergency'\n            start_time: '2025-02-01 14:30:00'\n            total_cost: 500.0\n    expect:\n      format: dict\n      rows:\n        - encounter_id: 'e2'\n          patient_id: 'p2'\n          encounter_type: 'emergency'\n          start_time: '2025-02-01 14:30:00'\n          total_cost: 500.0","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}},{"input":"ref('stg_synthea__encounters')","bound_to_node":"source","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]},{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0},{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"patient_id":{"description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}}}],"expected":{"table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"int_dq_quarantine__encounters","path":"models/intermediate/quarantine/int_dq_quarantine__encounters.sql","description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","dag":{"nodes":[],"edges":[]},"compiled_sql":{"int_dq_quarantine__encounters":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__encounters',\n    description='Encounters quarantined due to data quality violations'\n) }}\n\n-- cute-dbt fixture: synthetic body mod","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"int_patients__never_admitted":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # Cross-join demo (cute-dbt#64): mart_date_state_grid's CROSS JOIN\n  # CTE gives cute-dbt's renderer a real cross-join edge to draw. This\n  # test also mixes given formats (dict + csv) in a single unit_test.\n  - name: test_mart_date_state_grid_cross_joins_months_and_states\n    description: |\n      mart_date_state_grid CROSS JOINs the distinct calendar months from\n      dim_date with the distinct non-null patient states from staging.\n      Three daily dim_date rows collapse to two months; four patient\n      rows collapse to two states (one NULL filtered out, one duplicate\n      deduplicated). The result is the full 2 x 2 cartesian product —\n      four grid rows. Mixes a dict given (dim_date) with a csv given\n      (patients) in one test.\n    model: mart_date_state_grid\n    given:\n      - input: ref('dim_date')\n        format: dict\n        rows:\n          - date_actual: 2024-01-15\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-01-16\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-02-01\n            year_actual: 2024\n            month_actual: 2\n            month_name: 'February'\n      - input: ref('stg_synthea__patients')\n        format: csv\n        fixture: mart_date_state_grid_patients\n    expect:\n      format: csv\n      rows: |\n        month_key,year_actual,month_actual,month_name,state\n        202401,2024,1,January,Massachusetts\n        202401,2024,1,January,Connecticut\n        202402,2024,2,February,Massachusetts\n        202402,2024,2,February,Connecticut","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","table":{"columns":["patient_id","state"],"rows":[{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},{"display":"","key":{"t":"null"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":"patient_id,state\na00000aa-0000-0000-0000-000000000001,Massachusetts\na00000aa-0000-0000-0000-000000000002,Connecticut\na00000aa-0000-0000-0000-000000000003,\na00000aa-0000-0000-0000-000000000004,Massachusetts\n","format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv","column_meta":{"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , 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    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","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":false,"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).","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"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}]},{"name":"stg_synthea__patients","path":"models/staging/synthea/stg_synthea__patients.sql","description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__patients.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"patients\"\n)"},"raw_sql":"with source as (\n    select * from {{ source('synthea_raw', 'patients') }}\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.stg_synthea__patients.test_stg_synthea__patients_renames_and_hashes_source_columns","name":"test_stg_synthea__patients_renames_and_hashes_source_columns","target_model":"stg_synthea__patients","changed":false,"description":"stg_synthea__patients renames the raw Synthea UPPERCASE columns to\nsnake_case and hashes the SSN with md5. The given mocks the\nsynthea_raw.patients SOURCE directly (inline dict rows) — the only\nunit test in this project whose input is a source() rather than a\nref() (cute-dbt#57). Dict expect compares listed columns only, so\nthe non-deterministic _loaded_at (current_timestamp) is excluded\nfrom assertion automatically.\n","tags":[],"meta":{},"defined_in":"models/staging/synthea/_synthea__models.yml","authoring_yaml":"  - name: test_stg_synthea__patients_renames_and_hashes_source_columns\n    description: |\n      stg_synthea__patients renames the raw Synthea UPPERCASE columns to\n      snake_case and hashes the SSN with md5. The given mocks the\n      synthea_raw.patients SOURCE directly (inline dict rows) — the only\n      unit test in this project whose input is a source() rather than a\n      ref() (cute-dbt#57). Dict expect compares listed columns only, so\n      the non-deterministic _loaded_at (current_timestamp) is excluded\n      from assertion automatically.\n    model: stg_synthea__patients\n    given:\n      - input: source('synthea_raw', 'patients')\n        format: dict\n        rows:\n          - Id: 'c0000000-0000-0000-0000-000000000001'\n            FIRST: 'Ada'\n            LAST: 'Lovelace'\n            GENDER: 'F'\n            CITY: 'Boston'\n            STATE: 'Massachusetts'\n            SSN: '999-99-9999'\n    expect:\n      format: dict\n      rows:\n        - patient_id: 'c0000000-0000-0000-0000-000000000001'\n          first_name: 'Ada'\n          last_name: 'Lovelace'\n          gender: 'F'\n          city: 'Boston'\n          state: 'Massachusetts'\n          ssn_hash: '257cf696294931925796925d89aa0afb'","given":[{"input":"source('synthea_raw', 'patients')","bound_to_node":"source","table":{"columns":["CITY","FIRST","GENDER","Id","LAST","SSN","STATE"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"999-99-9999","key":{"t":"str","v":"999-99-9999"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"CITY":"Boston","FIRST":"Ada","GENDER":"F","Id":"c0000000-0000-0000-0000-000000000001","LAST":"Lovelace","SSN":"999-99-9999","STATE":"Massachusetts"}],"format":"dict","column_meta":{"Id":{"description":"Unique patient identifier (UUID)"}}}],"expected":{"table":{"columns":["city","first_name","gender","last_name","patient_id","ssn_hash","state"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"257cf696294931925796925d89aa0afb","key":{"t":"str","v":"257cf696294931925796925d89aa0afb"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"city":"Boston","first_name":"Ada","gender":"F","last_name":"Lovelace","patient_id":"c0000000-0000-0000-0000-000000000001","ssn_hash":"257cf696294931925796925d89aa0afb","state":"Massachusetts"}],"format":"dict","column_meta":{"city":{"description":"City of residence"},"first_name":{"description":"Patient's first name","tests":[{"name":"not null"}]},"gender":{"description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},"last_name":{"description":"Patient's last name","tests":[{"name":"not null"}]},"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"ssn_hash":{"description":"MD5 hash of Social Security Number (masked for privacy)"},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}}],"is_recursive":false}],"manifest_nodes":{"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at \u003c= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key"],"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)"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
 
 <script>
 /*! jQuery v3.7.1 | (c) OpenJS Foundation and other contributors | jquery.org/license */
@@ -7032,13 +7037,13 @@ <h2>Expected</h2>
       h += "</div>";
     }
     if (info.description) h += '<div class="ct-desc">' + escapeHtml(info.description) + "</div>";
+    // cute-dbt#235 — model tests ride the column tooltip's own
+    // .ct-test/.ct-key/.ct-vals anatomy (the shared ctTestHtml builder),
+    // so the model tip and the column tip stay visually consistent.
+    // The shelf card keeps its light-surface .dt-* chips (testChipsHtml).
     if (info.model_tests && info.model_tests.length) {
       h += '<div class="ct-tests-label">Model tests</div>';
-      h += '<div class="mt-mtests">';
-      info.model_tests.forEach(function (tt) {
-        h += '<div class="dt-row">' + testChipsHtml(tt) + "</div>";
-      });
-      h += "</div>";
+      h += '<div class="ct-tests">' + info.model_tests.map(ctTestHtml).join("") + "</div>";
     }
     el.innerHTML = h;
     el.hidden = false;
@@ -8312,6 +8317,31 @@ <h2>Expected</h2>
               : t.detail ? " " + t.detail : "";
     return t.name + extra;
   }
+  // One test row in the shared tooltip anatomy: accent `.ct-key` name +
+  // one `.ct-val` chip per argument. cute-dbt#202 — a multi-arg detail
+  // splits into one chip per argument (the ct-vals/ct-val dress the
+  // values branch uses; the ct-detail run is retired); the shipped
+  // single-value detail forms — relationships `m.f`, accepted_range
+  // `0–100` — carry no comma, so they stay one chip. cute-dbt#235 —
+  // shared by the column tooltip AND the model-ref tip's model-test
+  // rows, so the two bubbles cannot drift apart visually. Every
+  // interpolation passes through escapeHtml (payload strings never
+  // parse as HTML).
+  function ctTestHtml(t) {
+    var inner = '<span class="ct-key">' + escapeHtml(t.name) + "</span>";
+    if (t.values && t.values.length) {
+      inner += '<span class="ct-vals">'
+        + t.values.map(function (v) { return '<span class="ct-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
+        + "</span>";
+    } else if (t.detail) {
+      inner += '<span class="ct-vals">'
+        + String(t.detail).split(/,\s*/).map(function (a) {
+            return '<span class="ct-val">' + escapeHtml(a) + "</span>";
+          }).join("")
+        + "</span>";
+    }
+    return '<div class="ct-test">' + inner + "</div>";
+  }
   function decorateColHeader($th, name, meta) {
     if (!meta || (!meta.description && !(meta.tests && meta.tests.length))) return;
     var tests = meta.tests || [];
@@ -8347,26 +8377,7 @@ <h2>Expected</h2>
     var h = '<div class="ct-desc">' + escapeHtml(desc || name) + "</div>";
     if (tests.length) {
       h += '<div class="ct-tests-label">Data tests</div><div class="ct-tests">'
-         + tests.map(function (t) {
-             var inner = '<span class="ct-key">' + escapeHtml(t.name) + "</span>";
-             if (t.values && t.values.length) {
-               inner += '<span class="ct-vals">'
-                 + t.values.map(function (v) { return '<span class="ct-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
-                 + "</span>";
-             } else if (t.detail) {
-               // cute-dbt#202 — a multi-arg detail splits into one chip per
-               // argument (the ct-vals/ct-val dress the values branch uses;
-               // the ct-detail run is retired). The shipped detail forms —
-               // relationships `m.f`, accepted_range `0–100` — carry no
-               // comma, so a single-value detail stays one chip.
-               inner += '<span class="ct-vals">'
-                 + String(t.detail).split(/,\s*/).map(function (a) {
-                     return '<span class="ct-val">' + escapeHtml(a) + "</span>";
-                   }).join("")
-                 + "</span>";
-             }
-             return '<div class="ct-test">' + inner + "</div>";
-           }).join("")
+         + tests.map(ctTestHtml).join("")
          + "</div>";
     }
     el.innerHTML = h;
diff --git a/src/adapters/manifest.rs b/src/adapters/manifest.rs
index 9dfe5242..28cc81ec 100644
--- a/src/adapters/manifest.rs
+++ b/src/adapters/manifest.rs
@@ -260,12 +260,31 @@ struct WireSource {
     database: Option<String>,
     #[serde(default)]
     relation_name: Option<String>,
+    /// Source `columns` map (cute-dbt#235) — the same wire shape as a
+    /// node's (fusion `ManifestSource.columns` serializes through the
+    /// shared `serialize_dbt_columns`, `dbt-schemas`
+    /// `manifest/manifest_nodes.rs` @ `9977b6cb…`), so [`WireColumn`] is
+    /// reused verbatim. Feeds the `source(...)`-given column-header
+    /// tooltips; only `description` is consumed.
+    #[serde(default)]
+    columns: BTreeMap<String, WireColumn>,
 }
 
 impl WireSource {
     /// Translate into the domain [`SourceNode`], folding the
-    /// authoritative map `key` into the id (the [`WireNode`] precedent).
+    /// authoritative map `key` into the id (the [`WireNode`] precedent)
+    /// and keeping only non-empty column descriptions (the cute-dbt#165
+    /// empty-string-unset drop, applied to sources for cute-dbt#235).
     fn into_domain(self, key: String) -> SourceNode {
+        let column_descriptions = self
+            .columns
+            .into_iter()
+            .filter_map(|(name, col)| {
+                col.description
+                    .filter(|d| !d.is_empty())
+                    .map(|desc| (name, desc))
+            })
+            .collect();
         SourceNode::new(
             NodeId::new(key),
             self.source_name.unwrap_or_default(),
@@ -275,6 +294,7 @@ impl WireSource {
             self.database,
             self.relation_name,
         )
+        .with_column_descriptions(column_descriptions)
     }
 }
 
@@ -996,6 +1016,47 @@ mod tests {
         assert_eq!(source.relation_name(), None);
     }
 
+    #[test]
+    fn parse_manifest_extracts_source_column_descriptions_and_drops_empty_ones() {
+        // cute-dbt#235 — source `columns` ride the same wire shape as
+        // node columns (fusion serializes both through
+        // `serialize_dbt_columns`; an unset description is `""`). Only
+        // non-empty prose reaches the domain map — the #165 drop rule.
+        let json = format!(
+            r#"{{
+              "metadata": {{ "dbt_schema_version": "{V12_URL}" }},
+              "sources": {{
+                "source.shop.synthea_raw.patients": {{
+                  "schema": "main",
+                  "name": "patients",
+                  "source_name": "synthea_raw",
+                  "columns": {{
+                    "Id": {{ "name": "Id", "description": "Unique patient identifier (UUID)" }},
+                    "FIRST": {{ "name": "FIRST", "description": "" }},
+                    "LAST": {{ "name": "LAST" }}
+                  }}
+                }}
+              }}
+            }}"#
+        );
+        let manifest = parse_manifest(&json).expect("source entry with columns parses");
+        let source = manifest
+            .source_by_name("synthea_raw", "patients")
+            .expect("the (source_name, name) pair resolves");
+        assert_eq!(
+            source.column_descriptions().get("Id"),
+            Some(&"Unique patient identifier (UUID)".to_owned()),
+        );
+        assert!(
+            !source.column_descriptions().contains_key("FIRST"),
+            "fusion's empty-string unset description is dropped",
+        );
+        assert!(
+            !source.column_descriptions().contains_key("LAST"),
+            "a column with no description key contributes nothing",
+        );
+    }
+
     #[test]
     fn parse_manifest_tolerates_a_degenerate_source_entry() {
         // A sources entry with every consumed key absent must not fail
diff --git a/src/adapters/render.rs b/src/adapters/render.rs
index a69ba047..f25fe83e 100644
--- a/src/adapters/render.rs
+++ b/src/adapters/render.rs
@@ -743,11 +743,27 @@ pub fn column_meta_for_model(
     for (column, description) in model.column_descriptions() {
         meta.entry(column.clone()).or_default().description = Some(description.clone());
     }
+    for (column, test) in attached_column_tests(current, model.id()) {
+        meta.entry(column.to_owned()).or_default().tests.push(test);
+    }
+    meta
+}
+
+/// The column-scoped data tests attached to the node `attached` —
+/// shared by the model/seed arm ([`column_meta_for_model`]) and the
+/// source arm ([`column_meta_for_source`]) of the cute-dbt#165/#235
+/// column-header tooltips. Deterministic: sorted by (column, name,
+/// values, detail, test node id) — `Manifest::nodes` is a `HashMap`
+/// with no inherent order.
+fn attached_column_tests<'m>(
+    current: &'m Manifest,
+    attached: &NodeId,
+) -> Vec<(&'m str, ColumnTestPayload)> {
     let mut tests: Vec<(&str, ColumnTestPayload, &str)> = current
         .nodes()
         .iter()
         .filter_map(|(id, node)| {
-            if node.resource_type() != "test" || node.attached_node() != Some(model.id()) {
+            if node.resource_type() != "test" || node.attached_node() != Some(attached) {
                 return None;
             }
             let column = node.column_name()?;
@@ -764,12 +780,53 @@ pub fn column_meta_for_model(
             b.2,
         ))
     });
-    for (column, test, _) in tests {
+    tests
+        .into_iter()
+        .map(|(column, test, _)| (column, test))
+        .collect()
+}
+
+/// A SOURCE's column-metadata map (cute-dbt#235) — the
+/// [`column_meta_for_model`] twin for `source('a','b')` given inputs:
+/// authored column descriptions from the ingested source `columns`
+/// block, merged with column-scoped data tests whose `attached_node` is
+/// the source. Only columns with a description and/or ≥1 test appear,
+/// so a metadata-less source contributes nothing (honest degrade — the
+/// JS renders no trigger, never an empty bubble).
+fn column_meta_for_source(
+    current: &Manifest,
+    source: &SourceNode,
+) -> BTreeMap<String, ColumnMetaPayload> {
+    let mut meta: BTreeMap<String, ColumnMetaPayload> = BTreeMap::new();
+    for (column, description) in source.column_descriptions() {
+        meta.entry(column.clone()).or_default().description = Some(description.clone());
+    }
+    for (column, test) in attached_column_tests(current, source.id()) {
         meta.entry(column.to_owned()).or_default().tests.push(test);
     }
     meta
 }
 
+/// Resolve a unit-test GIVEN's `ref(...)` to its manifest node
+/// (cute-dbt#235). Unlike [`resolve_target_model`] (a unit test's
+/// `model:` target is always a model), a given's ref resolves over
+/// dbt's full refable set — models, seeds, and snapshots (the committed
+/// jaffle-shop fixture's `ref('raw_customers')` seed given is the real
+/// wire shape; fusion validates given inputs as any ref/source/this,
+/// `dbt-parser` `resolve_unit_tests.rs` @ `9977b6cb…`). Deterministic
+/// under a leaf-name collision: the lexicographically smallest node id
+/// wins (`model.*` sorts before `seed.*`/`snapshot.*`).
+fn resolve_given_ref_node<'m>(current: &'m Manifest, ref_name: &str) -> Option<&'m Node> {
+    const REFABLE: [&str; 3] = ["model", "seed", "snapshot"];
+    current
+        .nodes()
+        .values()
+        .filter(|node| {
+            REFABLE.contains(&node.resource_type()) && leaf_segment(node.id().as_str()) == ref_name
+        })
+        .min_by(|a, b| a.id().cmp(b.id()))
+}
+
 /// Filter a model's full column-metadata map down to the columns that
 /// actually appear in one rendered fixture table. `None` table (sql /
 /// opaque / unloaded-external fixture — no grid, no headers) ⇒ empty map
@@ -1794,21 +1851,32 @@ fn build_test_payload(
             let (rows, table, format) =
                 resolve_fixture_payload(g.rows(), g.format(), g.fixture(), loaded);
             let is_this = g.input() == "this";
-            // cute-dbt#165 — the model that OWNS this given's columns: the
-            // target model for `this` (prior model state), else the
-            // resolved `ref(...)` input model. `source(...)` inputs and
-            // unresolvable refs contribute nothing (empty map → key
-            // omitted).
+            // cute-dbt#165/#235 — the node that OWNS this given's
+            // columns: the target model for `this` (prior model state),
+            // the resolved refable node (model / seed / snapshot) for a
+            // `ref(...)` input, or the manifest source for a
+            // `source(...)` input. Unresolvable inputs contribute
+            // nothing (empty map → key omitted → no trigger, never an
+            // empty bubble).
             let column_meta = if is_this {
                 column_meta_for_table(target_column_meta, table.as_ref())
-            } else {
-                parse_ref_name(g.input())
-                    .and_then(|ref_name| resolve_target_model(current, &NodeId::new(ref_name)))
-                    .map(|input_model| {
-                        let meta = column_meta_for_model(current, input_model);
+            } else if let Some(ref_name) = parse_ref_name(g.input()) {
+                resolve_given_ref_node(current, ref_name)
+                    .map(|input_node| {
+                        let meta = column_meta_for_model(current, input_node);
                         column_meta_for_table(&meta, table.as_ref())
                     })
                     .unwrap_or_default()
+            } else if let Some((source_name, table_name)) = parse_source_ref(g.input()) {
+                current
+                    .source_by_name(source_name, table_name)
+                    .map(|source| {
+                        let meta = column_meta_for_source(current, source);
+                        column_meta_for_table(&meta, table.as_ref())
+                    })
+                    .unwrap_or_default()
+            } else {
+                BTreeMap::new()
             };
             GivenPayload {
                 input: g.input().to_owned(),
@@ -5367,6 +5435,222 @@ mod tests {
         );
     }
 
+    #[test]
+    fn build_test_payload_attaches_seed_column_meta_for_a_seed_ref_given() {
+        // cute-dbt#235 — dbt's ref() resolves over the refable set
+        // (models, seeds, snapshots), and a unit-test given may ref a
+        // seed (the committed jaffle-shop fixture's
+        // `ref('raw_customers')` is exactly this shape). The given's
+        // column tooltips must ride the SEED's declared metadata the
+        // same way a model-ref given rides its input model's.
+        let mut seed_desc = BTreeMap::new();
+        seed_desc.insert("customer_id".to_owned(), "Seed primary key".to_owned());
+        let seed = Node::new(
+            NodeId::new("seed.shop.raw_customers"),
+            "seed",
+            checksum("seed"),
+            None,
+            None,
+            DependsOn::default(),
+            None,
+            NodeConfig::default(),
+            None,
+            BTreeMap::new(),
+        )
+        .with_column_descriptions(seed_desc);
+        let target = model_node("model.shop.dim_x", "x", Some("select 1"));
+        let manifest = manifest_for(
+            vec![
+                target,
+                seed,
+                column_test_node(
+                    "test.shop.unique_raw_customers_customer_id",
+                    "seed.shop.raw_customers",
+                    "customer_id",
+                    TestMetadata::new("unique", None, Value::Null),
+                ),
+            ],
+            vec![],
+        );
+        let ut = UnitTest::new(
+            "t",
+            NodeId::new("dim_x"),
+            vec![UnitTestGiven::new(
+                "ref('raw_customers')",
+                json!([{ "customer_id": 1, "undocumented": 2 }]),
+                Some("dict".to_owned()),
+                None,
+            )],
+            UnitTestExpect::new(json!([{ "id": 1 }]), Some("dict".to_owned()), None),
+            None,
+            DependsOn::default(),
+            None,
+            None,
+            None,
+        );
+        let payload = build_test_payload(
+            "unit_test.shop.t",
+            &ut,
+            &CteGraph::default(),
+            &InScopeSet::new(),
+            None,
+            None,
+            None,
+            None,
+            &manifest,
+            &BTreeMap::new(),
+        );
+        let given_meta = &payload.given[0].column_meta;
+        assert_eq!(
+            given_meta
+                .get("customer_id")
+                .and_then(|m| m.description.as_deref()),
+            Some("Seed primary key"),
+            "a seed-ref given resolves the seed's column descriptions",
+        );
+        assert_eq!(
+            given_meta.get("customer_id").map(|m| m.tests.as_slice()),
+            Some(&[bare_test("unique")][..]),
+            "column-scoped tests attached to the seed ride along",
+        );
+        assert!(
+            !given_meta.contains_key("undocumented"),
+            "honest degrade: an undeclared fixture column carries no meta",
+        );
+    }
+
+    #[test]
+    fn build_test_payload_attaches_source_column_meta_for_a_source_given() {
+        // cute-dbt#235 — a `source('a','b')` given resolves the SOURCE's
+        // declared column descriptions (fusion `ManifestSource.columns`,
+        // verified on the committed playground fixture's
+        // `synthea_raw.patients.Id`) plus any column-scoped data tests
+        // attached to the source node.
+        let mut src_desc = BTreeMap::new();
+        src_desc.insert("Id".to_owned(), "Unique patient identifier".to_owned());
+        let source = SourceNode::new(
+            NodeId::new("source.shop.raw.patients"),
+            "raw",
+            "patients",
+            None,
+            "main",
+            None,
+            None,
+        )
+        .with_column_descriptions(src_desc);
+        let target = model_node("model.shop.dim_x", "x", Some("select 1"));
+        let manifest = manifest_with_sources(
+            vec![
+                target,
+                column_test_node(
+                    "test.shop.not_null_raw_patients_Id",
+                    "source.shop.raw.patients",
+                    "Id",
+                    TestMetadata::new("not_null", None, Value::Null),
+                ),
+            ],
+            vec![],
+            vec![source],
+        );
+        let ut = UnitTest::new(
+            "t",
+            NodeId::new("dim_x"),
+            vec![UnitTestGiven::new(
+                "source('raw', 'patients')",
+                json!([{ "Id": "a-1", "FIRST": "Ada" }]),
+                Some("dict".to_owned()),
+                None,
+            )],
+            UnitTestExpect::new(json!([{ "id": 1 }]), Some("dict".to_owned()), None),
+            None,
+            DependsOn::default(),
+            None,
+            None,
+            None,
+        );
+        let payload = build_test_payload(
+            "unit_test.shop.t",
+            &ut,
+            &CteGraph::default(),
+            &InScopeSet::new(),
+            None,
+            None,
+            None,
+            None,
+            &manifest,
+            &BTreeMap::new(),
+        );
+        let given_meta = &payload.given[0].column_meta;
+        assert_eq!(
+            given_meta.get("Id").and_then(|m| m.description.as_deref()),
+            Some("Unique patient identifier"),
+            "a source given resolves the source's column descriptions",
+        );
+        assert_eq!(
+            given_meta.get("Id").map(|m| m.tests.as_slice()),
+            Some(&[bare_test("not null")][..]),
+            "column-scoped tests attached to the source ride along",
+        );
+        assert!(
+            !given_meta.contains_key("FIRST"),
+            "honest degrade: an undescribed source column carries no meta",
+        );
+    }
+
+    #[test]
+    fn source_given_with_no_declared_columns_keeps_column_meta_empty() {
+        // cute-dbt#235 honest degrade at the payload level: a source
+        // with no declared columns contributes NOTHING — the empty map
+        // is omitted from the wire and the JS renders no trigger
+        // (never an empty bubble).
+        let source = SourceNode::new(
+            NodeId::new("source.shop.raw.events"),
+            "raw",
+            "events",
+            None,
+            "main",
+            None,
+            None,
+        );
+        let manifest = manifest_with_sources(
+            vec![model_node("model.shop.dim_x", "x", Some("select 1"))],
+            vec![],
+            vec![source],
+        );
+        let ut = UnitTest::new(
+            "t",
+            NodeId::new("dim_x"),
+            vec![UnitTestGiven::new(
+                "source('raw', 'events')",
+                json!([{ "event_id": 1 }]),
+                Some("dict".to_owned()),
+                None,
+            )],
+            UnitTestExpect::new(json!([{ "id": 1 }]), Some("dict".to_owned()), None),
+            None,
+            DependsOn::default(),
+            None,
+            None,
+            None,
+        );
+        let payload = build_test_payload(
+            "unit_test.shop.t",
+            &ut,
+            &CteGraph::default(),
+            &InScopeSet::new(),
+            None,
+            None,
+            None,
+            None,
+            &manifest,
+            &BTreeMap::new(),
+        );
+        assert!(
+            payload.given[0].column_meta.is_empty(),
+            "no declared source columns → empty (omitted) column_meta",
+        );
+    }
+
     #[test]
     fn column_meta_is_omitted_from_the_wire_when_empty() {
         // A model with NO column metadata: the payload key must be absent
diff --git a/src/domain/manifest.rs b/src/domain/manifest.rs
index ee366b91..4de67adb 100644
--- a/src/domain/manifest.rs
+++ b/src/domain/manifest.rs
@@ -711,6 +711,15 @@ pub struct SourceNode {
     /// though fusion always populates it.
     #[serde(default)]
     relation_name: Option<String>,
+    /// Authored per-column descriptions (cute-dbt#235) — only columns
+    /// with a non-empty description appear, mirroring
+    /// [`Node::column_descriptions`] (the cute-dbt#165 ingestion rule).
+    /// Both engines serialize source `columns` like node columns
+    /// (fusion `ManifestSource.columns` via `serialize_dbt_columns`,
+    /// `dbt-schemas` `manifest_nodes.rs` @ `9977b6cb…`). Feeds the
+    /// given-table column-header tooltips for `source(...)` inputs.
+    #[serde(default)]
+    column_descriptions: BTreeMap<String, String>,
 }
 
 impl SourceNode {
@@ -733,9 +742,22 @@ impl SourceNode {
             schema: schema.into(),
             database,
             relation_name,
+            column_descriptions: BTreeMap::new(),
         }
     }
 
+    /// Attach authored per-column descriptions (cute-dbt#235) — the
+    /// [`Node::with_column_descriptions`] builder precedent. The adapter
+    /// passes only non-empty prose (empty-string unset shapes dropped).
+    #[must_use]
+    pub fn with_column_descriptions(
+        mut self,
+        column_descriptions: BTreeMap<String, String>,
+    ) -> Self {
+        self.column_descriptions = column_descriptions;
+        self
+    }
+
     /// Source id (`source.<package>.<source_name>.<name>`).
     #[must_use]
     pub fn id(&self) -> &NodeId {
@@ -779,6 +801,15 @@ impl SourceNode {
     pub fn relation_name(&self) -> Option<&str> {
         self.relation_name.as_deref()
     }
+
+    /// Authored per-column descriptions (cute-dbt#235) — only columns
+    /// with a non-empty description appear. Empty for sources without a
+    /// columns block (and for every pre-#235 serialization — tolerant
+    /// `#[serde(default)]`).
+    #[must_use]
+    pub fn column_descriptions(&self) -> &BTreeMap<String, String> {
+        &self.column_descriptions
+    }
 }
 
 /// `metadata` block — currently consumed only for the
@@ -1576,6 +1607,39 @@ mod tests {
         assert_eq!(s, back);
     }
 
+    #[test]
+    fn source_node_defaults_column_descriptions_empty_and_builder_attaches() {
+        // cute-dbt#235 — constructor leaves the map empty; the builder
+        // attaches descriptions (the Node::with_column_descriptions
+        // precedent for source(...)-given column tooltips).
+        let s = sample_source("source.shop.raw.patients", "raw", "patients");
+        assert!(s.column_descriptions().is_empty());
+        let mut descs = BTreeMap::new();
+        descs.insert("Id".to_owned(), "Unique patient identifier".to_owned());
+        let s = s.with_column_descriptions(descs.clone());
+        assert_eq!(s.column_descriptions(), &descs);
+    }
+
+    #[test]
+    fn source_node_column_descriptions_round_trip_and_tolerate_absence() {
+        // cute-dbt#235 — a pre-#235 serialization (key absent) must still
+        // deserialize (tolerant `#[serde(default)]`), and a populated map
+        // must survive the round trip.
+        let json = r#"{
+            "id": "source.shop.raw.orders",
+            "source_name": "raw",
+            "name": "orders",
+            "schema": "main"
+        }"#;
+        let s: SourceNode = serde_json::from_str(json).unwrap();
+        assert!(s.column_descriptions().is_empty());
+        let mut descs = BTreeMap::new();
+        descs.insert("order_id".to_owned(), "Raw order key".to_owned());
+        let s = s.with_column_descriptions(descs);
+        let back: SourceNode = serde_json::from_str(&serde_json::to_string(&s).unwrap()).unwrap();
+        assert_eq!(s, back);
+    }
+
     #[test]
     fn source_node_optional_fields_round_trip_as_none() {
         // The fusion-style minimal entry: identifier / database /
diff --git a/templates/interaction.js b/templates/interaction.js
index e6de2cd0..317a5213 100644
--- a/templates/interaction.js
+++ b/templates/interaction.js
@@ -1582,13 +1582,13 @@
       h += "</div>";
     }
     if (info.description) h += '<div class="ct-desc">' + escapeHtml(info.description) + "</div>";
+    // cute-dbt#235 — model tests ride the column tooltip's own
+    // .ct-test/.ct-key/.ct-vals anatomy (the shared ctTestHtml builder),
+    // so the model tip and the column tip stay visually consistent.
+    // The shelf card keeps its light-surface .dt-* chips (testChipsHtml).
     if (info.model_tests && info.model_tests.length) {
       h += '<div class="ct-tests-label">Model tests</div>';
-      h += '<div class="mt-mtests">';
-      info.model_tests.forEach(function (tt) {
-        h += '<div class="dt-row">' + testChipsHtml(tt) + "</div>";
-      });
-      h += "</div>";
+      h += '<div class="ct-tests">' + info.model_tests.map(ctTestHtml).join("") + "</div>";
     }
     el.innerHTML = h;
     el.hidden = false;
@@ -2862,6 +2862,31 @@
               : t.detail ? " " + t.detail : "";
     return t.name + extra;
   }
+  // One test row in the shared tooltip anatomy: accent `.ct-key` name +
+  // one `.ct-val` chip per argument. cute-dbt#202 — a multi-arg detail
+  // splits into one chip per argument (the ct-vals/ct-val dress the
+  // values branch uses; the ct-detail run is retired); the shipped
+  // single-value detail forms — relationships `m.f`, accepted_range
+  // `0–100` — carry no comma, so they stay one chip. cute-dbt#235 —
+  // shared by the column tooltip AND the model-ref tip's model-test
+  // rows, so the two bubbles cannot drift apart visually. Every
+  // interpolation passes through escapeHtml (payload strings never
+  // parse as HTML).
+  function ctTestHtml(t) {
+    var inner = '<span class="ct-key">' + escapeHtml(t.name) + "</span>";
+    if (t.values && t.values.length) {
+      inner += '<span class="ct-vals">'
+        + t.values.map(function (v) { return '<span class="ct-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
+        + "</span>";
+    } else if (t.detail) {
+      inner += '<span class="ct-vals">'
+        + String(t.detail).split(/,\s*/).map(function (a) {
+            return '<span class="ct-val">' + escapeHtml(a) + "</span>";
+          }).join("")
+        + "</span>";
+    }
+    return '<div class="ct-test">' + inner + "</div>";
+  }
   function decorateColHeader($th, name, meta) {
     if (!meta || (!meta.description && !(meta.tests && meta.tests.length))) return;
     var tests = meta.tests || [];
@@ -2897,26 +2922,7 @@
     var h = '<div class="ct-desc">' + escapeHtml(desc || name) + "</div>";
     if (tests.length) {
       h += '<div class="ct-tests-label">Data tests</div><div class="ct-tests">'
-         + tests.map(function (t) {
-             var inner = '<span class="ct-key">' + escapeHtml(t.name) + "</span>";
-             if (t.values && t.values.length) {
-               inner += '<span class="ct-vals">'
-                 + t.values.map(function (v) { return '<span class="ct-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
-                 + "</span>";
-             } else if (t.detail) {
-               // cute-dbt#202 — a multi-arg detail splits into one chip per
-               // argument (the ct-vals/ct-val dress the values branch uses;
-               // the ct-detail run is retired). The shipped detail forms —
-               // relationships `m.f`, accepted_range `0–100` — carry no
-               // comma, so a single-value detail stays one chip.
-               inner += '<span class="ct-vals">'
-                 + String(t.detail).split(/,\s*/).map(function (a) {
-                     return '<span class="ct-val">' + escapeHtml(a) + "</span>";
-                   }).join("")
-                 + "</span>";
-             }
-             return '<div class="ct-test">' + inner + "</div>";
-           }).join("")
+         + tests.map(ctTestHtml).join("")
          + "</div>";
     }
     el.innerHTML = h;
diff --git a/templates/report.css b/templates/report.css
index 40f20a24..a83abc00 100644
--- a/templates/report.css
+++ b/templates/report.css
@@ -1302,11 +1302,16 @@ th.has-col-meta:focus-visible { outline: 2px solid var(--accent); outline-offset
 .model-tooltip .ct-desc { font-size: 1.3em; line-height: 1.5; }
 .model-tooltip .mt-mat { font-size: 0.72rem; text-transform: uppercase; letter-spacing: 0.05em; color: #f0c674; border: 1px solid currentColor; border-radius: 999px; padding: 0.02rem 0.5rem; }
 .model-tooltip .mt-tag { font-size: 0.72rem; color: rgba(255,255,255,0.82); background: rgba(255,255,255,0.12); border-radius: 999px; padding: 0.02rem 0.5rem; }
-/* model-level tests inside the model-ref tip (the dark-context dress for
-   the shelf card's .dt-* chips). */
-.model-tooltip .mt-mtests { display: flex; flex-direction: column; gap: 0.3rem; margin-top: 0.3rem; }
-.model-tooltip .dt-key { color: color-mix(in oklab, var(--accent) 60%, white); }
-.model-tooltip .dt-val { background: rgba(255,255,255,0.14); color: rgba(255,255,255,0.92); }
+/* cute-dbt#235 — model-level tests inside the model-ref tip ride the
+   COLUMN tooltip's own .ct-test/.ct-key/.ct-vals anatomy (the shared
+   ctTestHtml builder — never a parallel chip system; the .mt-mtests/
+   .dt-* dress is retired here, the shelf card keeps its light-surface
+   .dt-* chips). Test names keep the readable-on-dark accent treatment
+   the retired .model-tooltip .dt-key carried; the value chips inherit
+   .col-tooltip .ct-val verbatim. The base .col-tooltip .ct-key color
+   is cute-dbt#233's lane — only this model-scoped override lives here
+   (same specificity, later in file, so it wins inside the model tip). */
+.model-tooltip .ct-key { color: color-mix(in oklab, var(--accent) 60%, white); }
 .ov-tooltip .ov-grp + .ov-grp { margin-top: 0.55rem; }
 .ov-tooltip .ov-grp-name { font-family: var(--font-mono); font-size: 0.78em; text-transform: uppercase; letter-spacing: 0.06em; opacity: 0.6; margin-bottom: 0.25rem; }
 .ov-tooltip .ov-row { display: flex; gap: 0.5rem; align-items: baseline; padding-left: 0.45rem; white-space: nowrap; }
diff --git a/tests/headless_toggle.rs b/tests/headless_toggle.rs
index 6c222b9f..c4448112 100644
--- a/tests/headless_toggle.rs
+++ b/tests/headless_toggle.rs
@@ -6008,13 +6008,16 @@ fn rich_hover_cards_model_pill_format_badge_and_overrides() {
         "Staged source rows",
         "the model tip carries the authored description",
     );
+    // cute-dbt#235 — the model tip's test rows ride the COLUMN tooltip's
+    // .ct-tests/.ct-test/.ct-key anatomy (the shared ctTestHtml builder;
+    // the .mt-mtests/.dt-* dress is retired in the tip).
     assert_eq!(
         eval_string(
             &tab,
-            &format!("{MODEL_TIP}.querySelector('.mt-mtests .dt-key').textContent")
+            &format!("{MODEL_TIP}.querySelector('.ct-tests .ct-key').textContent")
         ),
         "unique",
-        "the model tip lists the model-level data tests",
+        "the model tip lists the model-level data tests in ct-key anatomy",
     );
     // ===== focusout hides; hover reveals again (mouse path) =====
     let _ = eval(&tab, &format!("{PILL}.blur()"));
@@ -7950,3 +7953,500 @@ fn expected_meta_row_reads_left_without_an_expect_diff() {
 
     let _ = tab.close(true);
 }
+
+// ===== cute-dbt#235 — given column-header tooltips for seed/source =====
+//
+// The #165 given-side column tooltips only ever resolved `ref(...)`-to-
+// MODEL and `this` inputs; a seed-ref given (the committed jaffle-shop
+// fixture's `ref('raw_customers')` shape) and a `source('a','b')` given
+// carried NO column metadata, so their headers offered no tooltip while
+// the expected table's did. These guards pin the widened payload
+// (models | seeds | snapshots via the refable resolver; sources via the
+// manifest `sources` map) + the honest degrade (a metadata-less column
+// renders NO trigger — never an empty bubble).
+
+/// Baseline-mode render whose manifest also carries `sources` entries —
+/// the `render_to_file` twin for `source(...)`-given tests.
+fn render_with_sources_to_file(
+    filename: &str,
+    nodes: Vec<Node>,
+    sources: Vec<SourceNode>,
+    tests: Vec<(&str, UnitTest)>,
+    model_ids: &[&str],
+    changed_ids: &[&str],
+) -> String {
+    let all_ids: Vec<String> = tests.iter().map(|(id, _)| (*id).to_owned()).collect();
+    let m = manifest(nodes, tests)
+        .with_sources(sources.into_iter().map(|s| (s.id().clone(), s)).collect());
+    let in_scope: InScopeSet = all_ids.into_iter().collect();
+    let models: ModelInScopeSet = model_ids.iter().map(|id| NodeId::new(*id)).collect();
+    let changed: InScopeSet = changed_ids.iter().map(|s| (*s).to_owned()).collect();
+    let out = tmp(filename);
+    let _ = std::fs::remove_file(&out);
+    render_report(
+        &out,
+        &m,
+        &in_scope,
+        &models,
+        &changed,
+        &HashMap::new(),
+        &HashMap::new(),
+        &HashMap::new(),
+        &HashMap::new(),
+        "baseline.json",
+        ScopeSource::Baseline,
+        DEFAULT_REPORT_TITLE,
+        None,
+    )
+    .expect("render writes the report");
+    let p = out.to_str().expect("report path is valid UTF-8");
+    format!("file://{p}")
+}
+
+/// A `seed` node (resource_type "seed") — dbt's ref() resolves over the
+/// refable set (models, seeds, snapshots), so a given may ref it.
+fn seed_node(full_id: &str) -> Node {
+    Node::new(
+        NodeId::new(full_id),
+        "seed",
+        Checksum::new("sha256", "ck"),
+        None,
+        None,
+        DependsOn::default(),
+        None,
+        NodeConfig::default(),
+        None,
+        BTreeMap::new(),
+    )
+}
+
+#[test]
+#[ignore = "requires Chrome; runs explicitly in the headless-zero-egress CI job via `-- --ignored`"]
+fn given_header_tooltips_resolve_seed_and_source_metadata() {
+    // RED on pre-#235 main: both givens rendered 0 decorated headers
+    // (the seed/source arms contributed no column_meta) while the
+    // expected table's headers tooltipped — the regression Christopher
+    // hit reviewing PR #230. Hover AND keyboard focus must both reveal
+    // (the #146/#161 contract the th trigger carries since #166).
+    let mut seed_desc = BTreeMap::new();
+    seed_desc.insert("customer_id".to_owned(), "Seed primary key".to_owned());
+    let seed = seed_node("seed.shop.raw_customers").with_column_descriptions(seed_desc);
+    let mut src_desc = BTreeMap::new();
+    src_desc.insert("Id".to_owned(), "Unique patient identifier".to_owned());
+    let source = SourceNode::new(
+        NodeId::new("source.shop.raw.patients"),
+        "raw",
+        "patients",
+        None,
+        "main",
+        None,
+        None,
+    )
+    .with_column_descriptions(src_desc);
+
+    let ut = UnitTest::new(
+        "seed_source_cols".to_owned(),
+        NodeId::new("dim_x"),
+        vec![
+            UnitTestGiven::new(
+                "ref('raw_customers')".to_owned(),
+                serde_json::json!([{ "customer_id": 1, "undocumented": 2 }]),
+                Some("dict".to_owned()),
+                None,
+            ),
+            UnitTestGiven::new(
+                "source('raw', 'patients')".to_owned(),
+                serde_json::json!([{ "Id": "a-1", "FIRST": "Ada" }]),
+                Some("dict".to_owned()),
+                None,
+            ),
+        ],
+        UnitTestExpect::new(
+            serde_json::json!([{ "id": 1 }]),
+            Some("dict".to_owned()),
+            None,
+        ),
+        None,
+        DependsOn::default(),
+        None,
+        None,
+        None,
+    );
+
+    let url = render_with_sources_to_file(
+        "headless_seed_source_col_tooltips.html",
+        vec![
+            model_node("model.shop.dim_x"),
+            seed,
+            column_test_node(
+                "test.shop.unique_raw_customers_customer_id",
+                "seed.shop.raw_customers",
+                "customer_id",
+                TestMetadata::new("unique", None, serde_json::Value::Null),
+            ),
+            column_test_node(
+                "test.shop.not_null_raw_patients_Id",
+                "source.shop.raw.patients",
+                "Id",
+                TestMetadata::new("not_null", None, serde_json::Value::Null),
+            ),
+        ],
+        vec![source],
+        vec![("unit_test.shop.dim_x.seed_source_cols", ut)],
+        &["model.shop.dim_x"],
+        &[], // 0 changed → auto-All mode → the test is selected with content
+    );
+
+    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");
+    select_model(&tab, "dim_x");
+
+    // ===== given 0 (seed ref): the described+tested column decorates =====
+    const SEED_SEC: &str = "document.querySelectorAll('.given-section')[0]";
+    assert_eq!(
+        eval(
+            &tab,
+            &format!("{SEED_SEC}.querySelectorAll('th.has-col-meta').length")
+        )
+        .as_u64(),
+        Some(1),
+        "the seed-ref given decorates exactly its described column \
+         (customer_id yes, undocumented no)",
+    );
+    const SEED_TH: &str =
+        "document.querySelectorAll('.given-section')[0].querySelector('th.has-col-meta')";
+    assert_eq!(
+        eval_string(&tab, &format!("{SEED_TH}.getAttribute('data-col-name')")),
+        "customer_id",
+        "the decorated seed-given header is the seed's described column",
+    );
+    // Keyboard path (the #146/#161 contract): focus reveals the bubble.
+    let _ = eval(&tab, &format!("{SEED_TH}.focus()"));
+    const BUBBLE: &str = "document.getElementById('col-tooltip')";
+    assert!(
+        !eval_bool(&tab, &format!("{BUBBLE}.hidden")),
+        "focusing the seed-given header reveals the bubble",
+    );
+    assert_eq!(
+        eval_string(
+            &tab,
+            &format!("{BUBBLE}.querySelector('.ct-desc').textContent")
+        ),
+        "Seed primary key",
+        "the bubble carries the SEED's authored column description",
+    );
+    assert_eq!(
+        eval_string(
+            &tab,
+            &format!(
+                "Array.from({BUBBLE}.querySelectorAll('.ct-key'))\
+                 .map(function(k){{return k.textContent;}}).join('|')"
+            ),
+        ),
+        "unique",
+        "the column-scoped test attached to the seed rides the bubble",
+    );
+    let _ = eval(&tab, &format!("{SEED_TH}.blur()"));
+    // Mouse path: hover reveals too.
+    let _ = eval(
+        &tab,
+        &format!("{SEED_TH}.dispatchEvent(new MouseEvent('mouseover', {{bubbles: true}}))"),
+    );
+    assert!(
+        !eval_bool(&tab, &format!("{BUBBLE}.hidden")),
+        "hovering the seed-given header reveals the bubble",
+    );
+    let _ = eval(
+        &tab,
+        &format!("{SEED_TH}.dispatchEvent(new MouseEvent('mouseout', {{bubbles: true}}))"),
+    );
+
+    // ===== given 1 (source): the described source column decorates =====
+    const SRC_SEC: &str = "document.querySelectorAll('.given-section')[1]";
+    assert_eq!(
+        eval(
+            &tab,
+            &format!("{SRC_SEC}.querySelectorAll('th.has-col-meta').length")
+        )
+        .as_u64(),
+        Some(1),
+        "the source given decorates exactly its described column (Id yes, FIRST no)",
+    );
+    const SRC_TH: &str =
+        "document.querySelectorAll('.given-section')[1].querySelector('th.has-col-meta')";
+    assert_eq!(
+        eval_string(&tab, &format!("{SRC_TH}.getAttribute('data-col-name')")),
+        "Id",
+        "the decorated source-given header is the source's described column",
+    );
+    let _ = eval(&tab, &format!("{SRC_TH}.focus()"));
+    assert_eq!(
+        eval_string(
+            &tab,
+            &format!("{BUBBLE}.querySelector('.ct-desc').textContent")
+        ),
+        "Unique patient identifier",
+        "the bubble carries the SOURCE's authored column description",
+    );
+    assert_eq!(
+        eval_string(
+            &tab,
+            &format!(
+                "Array.from({BUBBLE}.querySelectorAll('.ct-key'))\
+                 .map(function(k){{return k.textContent;}}).join('|')"
+            ),
+        ),
+        "not null",
+        "the column-scoped test attached to the source rides the bubble",
+    );
+    let _ = eval(&tab, &format!("{SRC_TH}.blur()"));
+
+    // ===== honest degrade: metadata-less columns are NOT triggers =====
+    // The seed given's `undocumented` column and the source given's
+    // `FIRST` column have no description and no tests: no trigger class,
+    // and a hover on them must never open an empty bubble. (No tabindex
+    // assertion: DataTables gives every orderable header keyboard focus
+    // for SORTING — the honest-degrade contract is no tooltip trigger +
+    // no empty bubble, not non-focusability.)
+    assert!(
+        eval_bool(
+            &tab,
+            &format!(
+                "{SEED_SEC}.querySelector('th[title=\"undocumented\"]')\
+                 .classList.contains('has-col-meta') === false"
+            ),
+        ),
+        "a given column without metadata gets no tooltip affordance",
+    );
+    let _ = eval(
+        &tab,
+        &format!(
+            "{SEED_SEC}.querySelector('th[title=\"undocumented\"]')\
+             .dispatchEvent(new MouseEvent('mouseover', {{bubbles: true}}))"
+        ),
+    );
+    assert!(
+        eval_bool(
+            &tab,
+            &format!("(function(){{var el={BUBBLE}; return el === null || el.hidden;}})()"),
+        ),
+        "hovering a metadata-less given header never opens an empty bubble",
+    );
+
+    let _ = tab.close(true);
+}
+
+#[test]
+#[ignore = "requires Chrome; runs explicitly in the headless-zero-egress CI job via `-- --ignored`"]
+fn given_header_honest_degrade_metadata_less_given_has_no_triggers() {
+    // cute-dbt#235 (issue Discovery) — a given whose source genuinely
+    // lacks per-column metadata (here a seed with NO declared columns,
+    // the committed jaffle-shop `raw_customers` shape) must render a
+    // plain grid: zero tooltip triggers, and no empty bubble on hover.
+    let ut = UnitTest::new(
+        "bare".to_owned(),
+        NodeId::new("dim_x"),
+        vec![UnitTestGiven::new(
+            "ref('bare_seed')".to_owned(),
+            serde_json::json!([{ "a": 1, "b": 2 }]),
+            Some("dict".to_owned()),
+            None,
+        )],
+        UnitTestExpect::new(
+            serde_json::json!([{ "id": 1 }]),
+            Some("dict".to_owned()),
+            None,
+        ),
+        None,
+        DependsOn::default(),
+        None,
+        None,
+        None,
+    );
+    let url = render_to_file(
+        "headless_bare_given_no_triggers.html",
+        vec![
+            model_node("model.shop.dim_x"),
+            seed_node("seed.shop.bare_seed"),
+        ],
+        vec![("unit_test.shop.dim_x.bare", ut)],
+        &["model.shop.dim_x"],
+        &[],
+    );
+
+    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");
+    select_model(&tab, "dim_x");
+
+    assert_eq!(
+        eval(
+            &tab,
+            "document.querySelectorAll('.given-section th.has-col-meta').length"
+        )
+        .as_u64(),
+        Some(0),
+        "a metadata-less given renders ZERO tooltip triggers",
+    );
+    let _ = eval(
+        &tab,
+        "document.querySelector('.given-section th')\
+         .dispatchEvent(new MouseEvent('mouseover', {bubbles: true}))",
+    );
+    assert!(
+        eval_bool(
+            &tab,
+            "(function(){var el=document.getElementById('col-tooltip');\
+             return el === null || el.hidden;})()"
+        ),
+        "no empty bubble ever opens over a metadata-less given header",
+    );
+
+    let _ = tab.close(true);
+}
+
+#[test]
+#[ignore = "requires Chrome; runs explicitly in the headless-zero-egress CI job via `-- --ignored`"]
+fn model_badge_tooltip_rides_column_tooltip_chip_anatomy() {
+    // cute-dbt#235 concern 2 — the model-ref tip's test rows must use
+    // the COLUMN tooltip's own anatomy: .ct-tests container, .ct-test
+    // rows, accent .ct-key names (the readable-on-dark color-mix form),
+    // .ct-vals/.ct-val chips for key→value arguments. RED on pre-#235
+    // main: the rows rendered the shelf card's .dt-row/.dt-key/.dt-val
+    // dress instead (flat divergent styling).
+    let ut = UnitTest::new(
+        "anatomy".to_owned(),
+        NodeId::new("dim_x"),
+        vec![UnitTestGiven::new(
+            "ref('stg_src')".to_owned(),
+            serde_json::json!([{ "x": 1 }]),
+            Some("dict".to_owned()),
+            None,
+        )],
+        UnitTestExpect::new(
+            serde_json::json!([{ "id": 1 }]),
+            Some("dict".to_owned()),
+            None,
+        ),
+        None,
+        DependsOn::default(),
+        None,
+        None,
+        None,
+    );
+    let url = render_to_file(
+        "headless_model_tip_anatomy.html",
+        vec![
+            model_node("model.shop.dim_x"),
+            model_node("model.shop.stg_src")
+                .with_model_metadata(Some("Staged source rows".to_owned()), vec![]),
+            // A detail-bearing model-level test (accepted_range → the
+            // "0–100" chip) + a bare one (name-only row, no chips).
+            model_test_node(
+                "test.shop.range_stg_src",
+                "model.shop.stg_src",
+                TestMetadata::new(
+                    "accepted_range",
+                    None,
+                    serde_json::json!({ "min_value": 0, "max_value": 100 }),
+                ),
+            ),
+            model_test_node(
+                "test.shop.unique_stg_src",
+                "model.shop.stg_src",
+                TestMetadata::new("unique", None, serde_json::Value::Null),
+            ),
+        ],
+        vec![("unit_test.shop.dim_x.anatomy", ut)],
+        &["model.shop.dim_x"],
+        &[],
+    );
+
+    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");
+    select_model(&tab, "dim_x");
+
+    const PILL: &str =
+        "document.querySelectorAll('.given-section')[0].querySelector('.table-title .gt-model')";
+    let _ = eval(&tab, &format!("{PILL}.focus()"));
+    const MODEL_TIP: &str = "document.getElementById('model-tooltip')";
+    assert!(
+        !eval_bool(&tab, &format!("{MODEL_TIP}.hidden")),
+        "focusing the pill reveals the model tip",
+    );
+
+    // The shared anatomy: a .ct-tests container with one .ct-test row
+    // per model test, sorted by display name.
+    assert_eq!(
+        eval(
+            &tab,
+            &format!("{MODEL_TIP}.querySelectorAll('.ct-tests .ct-test').length")
+        )
+        .as_u64(),
+        Some(2),
+        "each model test renders one .ct-test row inside .ct-tests",
+    );
+    assert_eq!(
+        eval_string(
+            &tab,
+            &format!(
+                "Array.from({MODEL_TIP}.querySelectorAll('.ct-key'))\
+                 .map(function(k){{return k.textContent;}}).join('|')"
+            ),
+        ),
+        "accepted range|unique",
+        "test names render as .ct-key elements (sorted display names)",
+    );
+    // Key→value pairs: the accepted_range arguments render as the
+    // column tooltip's .ct-val chips (never a parallel chip system).
+    assert_eq!(
+        eval_string(
+            &tab,
+            &format!(
+                "Array.from({MODEL_TIP}.querySelectorAll('.ct-vals .ct-val'))\
+                 .map(function(v){{return v.textContent;}}).join('|')"
+            ),
+        ),
+        "0\u{2013}100",
+        "the detail argument renders as a .ct-val chip",
+    );
+    // Accent treatment: the model tip's .ct-key computes to the
+    // readable-on-dark color-mix form (compared against an in-page
+    // reference element so no oklab numbers are hardcoded).
+    assert!(
+        eval_bool(
+            &tab,
+            &format!(
+                "(function(){{\
+                 var ref = document.createElement('span');\
+                 ref.style.color = 'color-mix(in oklab, var(--accent) 60%, white)';\
+                 document.body.appendChild(ref);\
+                 var got = getComputedStyle({MODEL_TIP}.querySelector('.ct-key')).color;\
+                 var want = getComputedStyle(ref).color;\
+                 ref.remove();\
+                 return got === want;}})()"
+            ),
+        ),
+        "the model tip's test names carry the color-mix accent treatment",
+    );
+    // The retired dress must be gone from the tip (the shelf card keeps
+    // its light-surface .dt-* chips — out of this tip's scope).
+    assert_eq!(
+        eval(
+            &tab,
+            &format!(
+                "{MODEL_TIP}.querySelectorAll('.dt-row, .dt-key, .dt-val, .mt-mtests').length"
+            )
+        )
+        .as_u64(),
+        Some(0),
+        "no .dt-*/.mt-mtests remnants inside the model tip",
+    );
+
+    let _ = tab.close(true);
+}
diff --git a/tests/snapshots/render_integration__rendered_chrome_jaffle_shop.snap b/tests/snapshots/render_integration__rendered_chrome_jaffle_shop.snap
index bbfba411..4a4f9c6f 100644
--- a/tests/snapshots/render_integration__rendered_chrome_jaffle_shop.snap
+++ b/tests/snapshots/render_integration__rendered_chrome_jaffle_shop.snap
@@ -1319,11 +1319,16 @@ th.has-col-meta:focus-visible { outline: 2px solid var(--accent); outline-offset
 .model-tooltip .ct-desc { font-size: 1.3em; line-height: 1.5; }
 .model-tooltip .mt-mat { font-size: 0.72rem; text-transform: uppercase; letter-spacing: 0.05em; color: #f0c674; border: 1px solid currentColor; border-radius: 999px; padding: 0.02rem 0.5rem; }
 .model-tooltip .mt-tag { font-size: 0.72rem; color: rgba(255,255,255,0.82); background: rgba(255,255,255,0.12); border-radius: 999px; padding: 0.02rem 0.5rem; }
-/* model-level tests inside the model-ref tip (the dark-context dress for
-   the shelf card's .dt-* chips). */
-.model-tooltip .mt-mtests { display: flex; flex-direction: column; gap: 0.3rem; margin-top: 0.3rem; }
-.model-tooltip .dt-key { color: color-mix(in oklab, var(--accent) 60%, white); }
-.model-tooltip .dt-val { background: rgba(255,255,255,0.14); color: rgba(255,255,255,0.92); }
+/* cute-dbt#235 — model-level tests inside the model-ref tip ride the
+   COLUMN tooltip's own .ct-test/.ct-key/.ct-vals anatomy (the shared
+   ctTestHtml builder — never a parallel chip system; the .mt-mtests/
+   .dt-* dress is retired here, the shelf card keeps its light-surface
+   .dt-* chips). Test names keep the readable-on-dark accent treatment
+   the retired .model-tooltip .dt-key carried; the value chips inherit
+   .col-tooltip .ct-val verbatim. The base .col-tooltip .ct-key color
+   is cute-dbt#233's lane — only this model-scoped override lives here
+   (same specificity, later in file, so it wins inside the model tip). */
+.model-tooltip .ct-key { color: color-mix(in oklab, var(--accent) 60%, white); }
 .ov-tooltip .ov-grp + .ov-grp { margin-top: 0.55rem; }
 .ov-tooltip .ov-grp-name { font-family: var(--font-mono); font-size: 0.78em; text-transform: uppercase; letter-spacing: 0.06em; opacity: 0.6; margin-bottom: 0.25rem; }
 .ov-tooltip .ov-row { display: flex; gap: 0.5rem; align-items: baseline; padding-left: 0.45rem; white-space: nowrap; }
@@ -3367,13 +3372,13 @@ html[data-style="swiss"] .suppress-chip { border-radius: 0; }
       h += "</div>";
     }
     if (info.description) h += '<div class="ct-desc">' + escapeHtml(info.description) + "</div>";
+    // cute-dbt#235 — model tests ride the column tooltip's own
+    // .ct-test/.ct-key/.ct-vals anatomy (the shared ctTestHtml builder),
+    // so the model tip and the column tip stay visually consistent.
+    // The shelf card keeps its light-surface .dt-* chips (testChipsHtml).
     if (info.model_tests && info.model_tests.length) {
       h += '<div class="ct-tests-label">Model tests</div>';
-      h += '<div class="mt-mtests">';
-      info.model_tests.forEach(function (tt) {
-        h += '<div class="dt-row">' + testChipsHtml(tt) + "</div>";
-      });
-      h += "</div>";
+      h += '<div class="ct-tests">' + info.model_tests.map(ctTestHtml).join("") + "</div>";
     }
     el.innerHTML = h;
     el.hidden = false;
@@ -4647,6 +4652,31 @@ html[data-style="swiss"] .suppress-chip { border-radius: 0; }
               : t.detail ? " " + t.detail : "";
     return t.name + extra;
   }
+  // One test row in the shared tooltip anatomy: accent `.ct-key` name +
+  // one `.ct-val` chip per argument. cute-dbt#202 — a multi-arg detail
+  // splits into one chip per argument (the ct-vals/ct-val dress the
+  // values branch uses; the ct-detail run is retired); the shipped
+  // single-value detail forms — relationships `m.f`, accepted_range
+  // `0–100` — carry no comma, so they stay one chip. cute-dbt#235 —
+  // shared by the column tooltip AND the model-ref tip's model-test
+  // rows, so the two bubbles cannot drift apart visually. Every
+  // interpolation passes through escapeHtml (payload strings never
+  // parse as HTML).
+  function ctTestHtml(t) {
+    var inner = '<span class="ct-key">' + escapeHtml(t.name) + "</span>";
+    if (t.values && t.values.length) {
+      inner += '<span class="ct-vals">'
+        + t.values.map(function (v) { return '<span class="ct-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
+        + "</span>";
+    } else if (t.detail) {
+      inner += '<span class="ct-vals">'
+        + String(t.detail).split(/,\s*/).map(function (a) {
+            return '<span class="ct-val">' + escapeHtml(a) + "</span>";
+          }).join("")
+        + "</span>";
+    }
+    return '<div class="ct-test">' + inner + "</div>";
+  }
   function decorateColHeader($th, name, meta) {
     if (!meta || (!meta.description && !(meta.tests && meta.tests.length))) return;
     var tests = meta.tests || [];
@@ -4682,26 +4712,7 @@ html[data-style="swiss"] .suppress-chip { border-radius: 0; }
     var h = '<div class="ct-desc">' + escapeHtml(desc || name) + "</div>";
     if (tests.length) {
       h += '<div class="ct-tests-label">Data tests</div><div class="ct-tests">'
-         + tests.map(function (t) {
-             var inner = '<span class="ct-key">' + escapeHtml(t.name) + "</span>";
-             if (t.values && t.values.length) {
-               inner += '<span class="ct-vals">'
-                 + t.values.map(function (v) { return '<span class="ct-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
-                 + "</span>";
-             } else if (t.detail) {
-               // cute-dbt#202 — a multi-arg detail splits into one chip per
-               // argument (the ct-vals/ct-val dress the values branch uses;
-               // the ct-detail run is retired). The shipped detail forms —
-               // relationships `m.f`, accepted_range `0–100` — carry no
-               // comma, so a single-value detail stays one chip.
-               inner += '<span class="ct-vals">'
-                 + String(t.detail).split(/,\s*/).map(function (a) {
-                     return '<span class="ct-val">' + escapeHtml(a) + "</span>";
-                   }).join("")
-                 + "</span>";
-             }
-             return '<div class="ct-test">' + inner + "</div>";
-           }).join("")
+         + tests.map(ctTestHtml).join("")
          + "</div>";
     }
     el.innerHTML = h;