M
Medigy
05 / SQL Transformations
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Everything is queryable.

All logic lives in SQL. Every metric, cohort, and opportunity signal is defined as a view — not hidden in compiled code.

For:BD leadersProductData teams

Canonical views

cost_of_care_by_cohort_and_geoprovider_performance_by_conditionutilization_patterns_by_site_of_carequality_vs_cost_frontierreimbursement_alignment_tablesreadmission_rates_by_drgscreening_gaps_by_populationadherence_by_therapeutic_class

Sample query

-- Find HRRs where CHF readmissions exceed benchmark
-- AND CCM/RPM utilization is below the 25th percentile.
SELECT
  geo.hrr,
  geo.hrr_name,
  perf.readmission_rate_30d,
  perf.benchmark_rate,
  util.ccm_utilization_pct,
  util.rpm_utilization_pct,
  cost.avoidable_cost_pmpm
FROM provider_performance_by_condition AS perf
JOIN utilization_patterns_by_site_of_care AS util USING (hrr, condition)
JOIN cost_of_care_by_cohort_and_geo      AS cost USING (hrr, cohort_id)
JOIN geography                            AS geo  USING (hrr)
WHERE perf.condition = 'CHF'
  AND perf.readmission_rate_30d > perf.benchmark_rate
  AND util.ccm_utilization_pct < 0.10
ORDER BY cost.avoidable_cost_pmpm DESC
LIMIT 25;

Why SQL-native matters

  • — Transparent: any analyst can read and audit the logic
  • — Modifiable: customer teams extend without filing tickets
  • — Compatible: works with any SQL-capable tool or AI agent
  • — Portable: the same view runs on DuckDB, Postgres, or your warehouse
What this means for you
Your analysts can fork a view, adjust the definition for your business, and ship a new opportunity signal in an afternoon — not a quarter.
See this in your data

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