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PRomop: A Decision-Ready Longitudinal Patient Health Record on the OMOP Common Data Model
One-line summary
A solar energy research paper on PRomop: A Decision-Ready Longitudinal Patient Health Record on the OMOP Common Data Model.
Engineering notes
Engineering notes will be added by the Power for Solar editorial team.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
Objective: Health systems and biopharma face a persistent gap between storing patient data and acting on it. Records are fragmented across providers, structured for storage rather than decision-making, and each downstream application independently reconstructs patient clinical state. We present PRomop (PatientRecord on OMOP), an open-source longitudinal patient record designed to close that gap. Materials and Methods: PRomop extends the OMOP Common Data Model (CDM 5.4) with oncology extensions and introduces PatientRecord, a flattened projection that collapses each patient's longitudinal history into a single decision-ready row of 286 columns. Clinical state--including lines of therapy, disease status, and normalized biomarkers--is derived once during projection and materialized for reuse by analytics, clinical trial matching, and standard-of-care evaluation. We report production deployments and a controlled benchmark on synthetic data. Results: PRomop is deployed by two independently governed oncology organizations--the HealthTree Foundation (14,000 blood-cancer patients) and CancerBot (3,500)--supporting trial matching across 19,500 actively recruiting trials in five cancer types. A representative 20-criterion eligibility query requiring 27-39 joins on raw OMOP requires none against PatientRecord (analytical estimate: 30x-200x reduction). In a benchmark using 100 Synthea-generated breast-cancer patients, eligibility screening averaged 0.92 ms versus 20.7 ms from raw OMOP, a 23.9x speedup. Discussion and Conclusion: PatientRecord provides a shared, decision-ready foundation for downstream applications, eliminating repeated clinical-state derivation while preserving OMOP conformance. PRomop demonstrates that a flattened projection over a standards-based longitudinal record is a practical, deployed architecture for analytics, AI/ML, clinical trial matching, and decision support.
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