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.

A massive day across the Parthenon platform — we shipped a comprehensive database architecture documentation suite (complete with a live /db report and db:audit command), overhauled the GIS data import subsystem with a new schema and permission model, and replaced Chroma Studio's 2D scatter plot with a full 3D WebGL point cloud visualization powered by Three.js and a server-side PCA→UMAP projection pipeline.

A massive day across the Acumenus suite — 60 commits touching six repositories. The throughlines: a platform-wide push to standardize authentication using the MediCosts pattern, TypeScript migrations gaining serious momentum in multiple apps, and meaningful OHDSI clinical data work in Parthenon including surfacing 52.6 million clinical notes through a new Patient Profile tab.

We ran every analysis type in Parthenon — estimation, prediction, SCCS, evidence synthesis, pathways, characterization, and incidence rates — against our full Acumenus CDM with 1 million synthetic patients. Thirteen seeded analyses. Seven different OHDSI methodologies. One session.

This post covers what happened when we moved from the 2,694-patient Eunomia demo dataset to production scale, the bugs that only surface at a million patients, and the hard-won lessons about propensity score modeling on synthetic data.

The OHDSI HADES ecosystem is remarkable. CohortMethod, PatientLevelPrediction, SelfControlledCaseSeries — these R packages encode decades of pharmacoepidemiology methodology into reusable software. In theory, you point them at an OMOP CDM database, call a few functions, and get publication-ready causal inference results.

In practice, getting these packages to run correctly in a modern production environment required solving problems that no documentation warned us about.

This is the story of what we encountered building Parthenon's R runtime — a Plumber API sidecar that executes HADES analyses against a 1-million-patient CDM — and the specific, reproducible bugs we had to fix before a single analysis could complete.