37 posts tagged with "ohdsi"

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.

A massive day on the Parthenon platform: we shipped the full six-chapter Cohort Wizard UI from scratch in a single push, and completed a methodical, non-destructive migration of Imaging, Genomics, GIS, and Oncology extension structures into the localhost OMOP schema. These two workstreams — one facing researchers building cohorts, one facing the data layer powering them — represent a significant leap forward in Parthenon's end-to-end outcomes research story.

The Patient Profile's Labs Panel has been one of Parthenon's persistent frustrations: a table with Ranges and Status columns that were perpetually empty, and a display format that forced clinicians to mentally reconstruct a lab value's trajectory from a column of numbers. Today we shipped the fix — a Recharts line chart with a shaded reference-range band, status-colored measurement dots, and a hybrid data layer that finally makes lab reference ranges work across every CDM source.

Eight days ago, we shipped the Patient Similarity Engine — a multi-modal system that scores patients across six clinical dimensions using weighted Jaccard, z-scored lab distances, and pathogenicity-tiered genomic matching. Two days later, we generated embeddings for a million patients. The engine worked. Researchers could find patients like a seed patient, compare cohorts, and export results.

But it wasn't research-grade. The Jaccard similarity was binary — two patients with Type 1 DM and Type 2 DM got zero credit even though they share the ancestor "Diabetes mellitus" in the SNOMED hierarchy. The cohort comparison showed a radar chart with divergence percentages, but couldn't tell you which covariates were driving the imbalance or how the distributions actually differed. There was no propensity scoring, no temporal analysis, no phenotype discovery, and no way to fuse multiple data modalities into a single principled similarity measure.

Tonight, in a single session, we shipped eight interconnected upgrades that transform the Patient Similarity Engine from a useful clinical tool into a research platform that exceeds the analytical capabilities of OHDSI Atlas, Oracle Healthcare's "Patients Like Mine," and every open-source OMOP similarity system we've been able to find.

This is the story of what we built, why each piece matters, and how they work together.

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.

A landmark day for platform observability and data pipeline reliability. We shipped a fully wired Jobs monitoring page that surfaces all 13+ tracked job types, broke through a major ETL performance ceiling on the SynPUF dataset (17 hours → 14 minutes for drug_era builds), and closed out a cohort generation audit that uncovered eight discrete bugs across the SQL builders, API layer, and frontend.

A massive day on the ingestion front — 87 commits landed in Parthenon today, almost entirely focused on building out a brand-new end-to-end data ingestion pipeline. We now have a fully wired system for creating ingestion projects, uploading raw files, staging them into a schema-isolated PostgreSQL environment, and handing off to Aqueduct for ETL. This has been a long time coming.

A massive day on Parthenon with 193 commits landing across the platform. The headlining work: a near-complete publication/manuscript workflow that takes study analyses all the way to a formatted, auto-numbered document preview, plus a long-overdue rename of the R Analytics Runtime to Darkstar — the name it's been running under in Docker all along.

If you've worked in the OHDSI ecosystem, you know the pain: Atlas for cohort definitions, Achilles Results Viewer for characterization, a DQD dashboard for data quality, spreadsheets for feasibility assessments, and a prayer that everyone's looking at the same release of the same data. Ares changes that. Today we're announcing Ares v2 — Parthenon's network-level data observatory — a single unified module that replaces the fragmented constellation of OHDSI data characterization tools with 10 purpose-built analytical panels, 60+ API endpoints, and a clinical UI designed for researchers who need answers, not workarounds.

This is the biggest feature release in Parthenon's history.

Today was one of those satisfying days where two major workstreams converged: we pushed the Ares data quality module from skeleton to a fully featured analytics suite with four distinct intelligence phases, and we permanently fixed a cluster of compounding bugs that had been making Achilles characterization runs fragile on large real-world datasets. Both efforts move Parthenon meaningfully closer to being a production-grade OHDSI research platform.