PRomop: A Decision-Ready Longitudinal Patient Health Record on the OMOP Common Data Model

Abstract

Objective: Health systems and biopharma face a gap between holding patient data and acting on it: records are fragmented, manually mapped, and structured for storage rather than decisions, so every application re-derives patient state. We present PRomop, an open-source longitudinal record that closes this gap. Materials and Methods: PRomop builds on the OMOP Common Data Model (CDM 5.4) with oncology extensions and adds PatientRecord, a flattened projection collapsing each patient's longitudinal history into a single decision-ready 304-column row. State derivations - lines of therapy, disease status, normalized biomarkers - are computed once at projection time, so analytics, trial matching, and standard-of-care evaluation read one substrate. Results: PRomop is deployed by two oncology organizations - the independently governed HealthTree Foundation (~14,000 patients) and CancerBot (~3,500), a HealthKey-owned deployment - matching against 19,500 recruiting trials across five cancer types. A 20-criterion eligibility search requiring 27-39 joins over raw OMOP reduces to zero against the projection. On a synthetic 1000-patient breast-cancer cohort, eligibility screening averaged 0.30 ms via PatientRecord versus 11.0 ms from raw OMOP, a ~36.8x speedup. Discussion: The projection's significance is as a foundation for other applications: it lowers each one's marginal cost by computing error-prone clinical derivation once and removing it from every consumer. Line-of-therapy inference showed decision-readiness demands embedded clinical reasoning, and that the projection is a living artifact requiring maintenance. Conclusion: A flattened, decision-ready projection over a standards-based longitudinal record is a deployed pattern for turning fragmented data into actionable infrastructure, while remaining OMOP-conformant. Benchmarks measured a ~36.8x eligibility-screening speedup.