04 / Normalization
Connect with us →A uniform model of care delivery.
Without a uniform model, opportunity detection becomes fragmented and unreliable. The Medigy Opportunity Atlas standardizes the DDL upfront — every entity maps to real CMS columns.
For:BD leadersProductData teams
Core entities
Providers
rndrng_npirndrng_prvdr_type— Specialtyrndrng_prvdr_ent_cd— Org Hierarchy
Patients / Cohorts
condition_namebody_systemtier— 1=Flagship, 2=Core, 3=Baselinetotal_beneficiaries
Geography
state_abbrlocality_namepw_gpci— Geographic Practice Cost Index
Plannedhrrcounty_fips
Encounters
source_type— CMS_GEO, CMS_DME, CMS_HOSPITAL_OP, CMS_HOSPITAL_INPThcpcs_code / apc_cd / drg_cd
Cost
total_allowed_amttotal_medicare_paymentallowed_per_patient— Economic Density metric
Why this matters
- — Cross-dataset joins are trivial when keys are aligned
- — Cohort definitions stay consistent across analyses
- — Performance metrics are comparable across geographies and providers
- — New data sources plug in without re-engineering downstream logic
A 'Sleep Apnea' cohort uses the same ICD-10/HCPCS logic whether you are looking at CMS_GEO diagnostic data or CMS_DME_CPAP therapy data.
What this means for you
SELECT state_abbr, total_beneficiaries, allowed_per_patient FROM mat_condition_state_breakdown WHERE condition_name = 'Heart Failure' ORDER BY allowed_per_patient DESC;
What this means for you
When BD asks "show me CHF cohorts in HRR 423 with low CCM utilization," that question resolves to a single SQL join — not a two-week data engineering project.
See this in your data
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