23 posts tagged with "ohdsi"

A big day on the quality and resilience front: 34 commits landed in Parthenon focused on a comprehensive codebase health audit, a major expansion of our Playwright E2E test suite, and a fork of the StudyAgent submodule. No flashy new features today — instead, we did the unglamorous but essential work of making sure what we've already built actually works, is safe to change, and won't silently break in production.

The R runtime was the single most fragile component in the entire Parthenon stack. Every other service — PHP, Python AI, Solr, Redis, PostgreSQL — could handle concurrent requests gracefully. The R container could not. A single CohortMethod estimation on 1 million patients takes 5-30 minutes. During that time, the entire R process was locked — health checks timed out, status queries hung, and any other analysis request queued behind it with no feedback. This devlog covers the six-phase hardening effort that replaced the entire R runtime infrastructure in a single day.

In a single development session, we shipped three phases of a cognitive architecture that transforms Abby from a stateless RAG chatbot into a persistent, intelligent, context-aware research assistant. She now remembers who you are, routes complex questions to a more powerful brain, traverses clinical concept hierarchies, and warns you when your data has gaps. This post tells the complete story — the problems we solved, the architecture we built, and the engineering decisions behind 188 passing tests across 60+ new files.

What started as "Abby's responses are slow" turned into an 18-hour deep dive that touched every layer of the AI stack — from GPU driver backends to embedding model race conditions to the fundamental question of why a 4-billion-parameter medical LLM was confidently inventing researcher names. By the end, Abby went from 15-25 second hallucinated responses to 2-5 second grounded answers backed by 167,000 vectors of medical knowledge — and we found that an 8-megabyte systemd memory lock was silently killing 25% of all GPU inference requests.

Abby can now answer "What concept sets do we have for diabetes?" and "How many patients are in our CDM?" with real data — queried live from the Parthenon PostgreSQL database at response time. Eight contextual tools give her awareness of concept sets, cohort definitions, vocabulary concepts, Achilles characterization stats, data quality results, cohort generation counts, CDM summaries, and analysis executions.

A productive Sunday on the Parthenon platform — 70 commits landed today covering four distinct themes: hardening the real-time Commons presence system, fixing a persistent CSRF authentication bug, overhauling the Grafana observability stack, and significantly expanding the medical knowledge base powering Abby, our AI research assistant.

We stood up the full Parthenon analyses pipeline end-to-end: 11 comparative effectiveness studies across 10 disease areas, 46 generated cohorts, and 26 executed analyses including R-based CohortMethod propensity score matching on populations up to 68,000 patients. Along the way, we found and fixed every null-safety bug that only surfaces when real analysis results hit the frontend.

Today we transformed Abby from a capable AI assistant into an OHDSI domain expert backed by the largest curated outcomes research knowledge base we're aware of in any open-source platform. By the end of the day, Abby's ohdsi_papers ChromaDB collection held 79,070 SapBERT-embedded vectors spanning peer-reviewed research papers, the Book of OHDSI, HADES package documentation, and a decade of practitioner Q&A from the OHDSI forums.

Today we gave Parthenon's AI assistant a research library that most outcomes researchers would envy. Abby — our context-aware, privacy-preserving AI — now has 115,000+ SapBERT-embedded vectors spanning 2,258 peer-reviewed OHDSI papers, the complete Book of OHDSI, documentation from 30 HADES R packages, a decade of community forum Q&A, and 19 medical reference textbooks covering epidemiology, biostatistics, pharmacology, pathology, and clinical medicine.

This post tells the full story: why we built Abby, how the architecture works, what we harvested, what we learned about data quality in knowledge bases, and where we're headed next.

Today was a focused, high-output session centered entirely on one major architectural shift: evolving the GIS Explorer from a hardcoded COVID-19 dashboard into a fully generalized spatial analytics tool capable of visualizing any condition in the OMOP CDM. Eighteen commits across the full stack — Python AI service, Laravel backend, and React frontend — tell the story of a component suite that went from COVID-specific to condition-agnostic in a single day.