2 posts tagged with "snomed"

Parthenon's Cohort Definitions page has always had a "Create from Care Bundle" modal — a way to bootstrap a cohort definition from a pre-packaged disease framework with ICD-10 patterns, OMOP concepts, and quality measures. The idea is elegant: select "Rheumatoid Arthritis," click a button, and get a fully-formed OHDSI Circe cohort expression ready to run against any CDM source.

But when I opened the modal this weekend, I saw only ten bundles. Type 2 Diabetes, Hypertension, Heart Failure, COPD, Asthma, and a handful of others. Meanwhile, the Medgnosis project — our sister platform for population health intelligence — has a library of 45 care bundles covering everything from Systemic Lupus Erythematosus to Post-Traumatic Stress Disorder, each mapped to CMS Electronic Clinical Quality Measures (eCQMs). The data was sitting there in three SQL migration files. Parthenon just didn't know about it.

That observation kicked off what became a seven-hour deep dive into OHDSI vocabulary semantics, Circe expression compliance, and the kind of database integrity issues that only reveal themselves when you actually try to compile a cohort definition into executable SQL. By the end, we had 45 bundles, 338 quality measures, 928 verified OMOP concept IDs — and we caught eleven bugs along the way, several of which would have silently produced wrong cohorts in production.

This is the story of how we got there.

Parthenon's Vocabulary Search now provides 100% navigational coverage of all 105,324 standard SNOMED CT Condition concepts through 27 curated clinical groupings — achieving functional parity with MedDRA's System Organ Class navigation while preserving SNOMED's superior clinical granularity. This is the story of diagnosing the SNOMED-OMOP domain boundary problem, engineering a cross-domain hierarchy builder, curating a clinically intelligent grouping layer, and systematically closing every coverage gap until no standard concept was left behind.