30 posts tagged with "ai"

For twenty years, the question "which patients are most like this one?" has haunted clinical informatics. Molecular tumor boards want to know: of the 300 patients in our pancreatic cancer corpus, which ones had the same pathogenic variants, the same comorbidity profile, the same treatment history — and what happened to them? Population health researchers want to seed cohort definitions not from abstract inclusion criteria but from a concrete index patient. And every clinician who has ever stared at a complex case has wished for a button that says show me others like this.

Today, Parthenon ships that button. The Patient Similarity Engine is a multi-modal matching system that scores patients across six clinical dimensions — demographics, conditions, measurements, drugs, procedures, and genomic variants — with user-adjustable weights, dual algorithmic modes, bidirectional cohort integration, and tiered privacy controls. It works across any OMOP CDM source in the platform, from the 361-patient Pancreatic Cancer Corpus to the million-patient Acumenus CDM.

This post tells the story of why it was needed, what we studied before building it, how it works under the hood, and what we learned along the way.

In Greek mythology, the great temple atop the Acropolis housed not just Athena, but an entire pantheon of divine figures — each wielding a unique gift. Parthenon, our unified OHDSI outcomes research platform, follows the same philosophy. Behind the scenes, four mythological women power the intelligence layer that transforms raw clinical data into actionable research: Hecate, Phoebe, Ariadne, and Arachne.

After months of a perpetually red CI pipeline, today marks a turning point for Parthenon: 92 commits, a full-spectrum codebase review, a complete AtlanticHealth patient synthesis pipeline, and — most satisfying of all — every CI job green. Here's how we got there.

Abby can now orchestrate complex multi-step research workflows with independent steps running in parallel. High-risk tools (modify concept sets, update cohort criteria, execute SQL) join the toolkit with safety validation. Dry run mode simulates actions before execution. Workflow templates encode OHDSI best practices into one-click study designs.

Abby can now take actions, not just answer questions. "Build me a diabetes cohort" generates a reviewable multi-step plan — create concept sets, define the cohort, generate the patient count — that executes with one click after user approval. Every action is logged with checkpoint data for rollback. Phase 4 adds supervised autonomy with safety rails.

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.

Abby now learns from the entire research community. When a researcher builds a successful diabetes cohort, that pattern becomes available to every other researcher. Questions asked three or more times across users automatically become institutional FAQs with vetted answers. Data quality findings discovered by one team warn all teams. Phase 6 completes the Abby 2.0 cognitive architecture.

Abby now has two brains. Simple queries stay local on MedGemma (fast, free). Complex reasoning escalates to Claude via API (powerful, cloud). A PHI sanitizer blocks any patient data from leaving the network, and a cost tracker with circuit breaker keeps spending within budget. Researchers get smarter answers on hard questions without compromising privacy or breaking the bank.

Abby now understands that metformin is a drug used for Type 2 diabetes mellitus, which is a subtype of diabetes mellitus. She traverses OMOP concept hierarchies, finds siblings and related concepts, and warns researchers when they're building on sparse data. Phase 3 turns keyword matching into relational understanding.

Today marked a significant architectural milestone for Abby, Parthenon's AI research assistant: the completion of Phase 1 of the Abby 2.0 memory overhaul. Eighty-six commits landed today, all focused on one goal — giving Abby a durable, queryable memory backed by PostgreSQL rather than ChromaDB, and surfacing user research context directly in the chat interface.