AI 中文总结
研究针对医疗系统存储与利用患者数据的差距，提出PRomop。它扩展OMOP通用数据模型，通过扁平投影生成可决策的患者记录。经两个组织部署及合成数据测试，能加速试验匹配和资格筛选，为下游应用提供共享基础，证明该架构实用。
AI 中文摘要
目的：医疗系统和生物制药公司在存储患者数据和据此采取行动之间存在持续差距。记录分散在不同提供者之间，为存储而非决策而构建，每个下游应用独立重建患者临床状态。我们提出PRomop（基于OMOP的患者记录），一个旨在弥合这一差距的开源纵向患者记录。材料和方法：PRomop用肿瘤学扩展扩展了OMOP通用数据模型（CDM 5.4），并引入PatientRecord，一种扁平投影，将每个患者的纵向病史压缩成一行286列的可用于决策的记录。临床状态，包括治疗线、疾病状态和标准化生物标志物，在投影期间推导一次，并物化以供分析、临床试验匹配和护理标准评估重用。我们报告了在合成数据上的生产部署和受控基准测试。结果：PRomop由两个独立管理的肿瘤学组织——HealthTree基金会（14000名血癌患者）和CancerBot（3500名）——部署，支持五种癌症类型中19500项正在积极招募的试验的试验匹配。一个代表性的20标准资格查询，在原始OMOP上需要27 - 39次连接，而在PatientRecord上则无需连接（分析估计：减少30倍 - 200倍）。在使用100名Synthea生成的乳腺癌患者的基准测试中，资格筛选平均为0.92毫秒，而原始OMOP为20.7毫秒，加速了23.9倍。讨论和结论：PatientRecord为下游应用提供了一个共享的、可用于决策的基础，消除了重复的临床状态推导，同时保持了与OMOP的一致性。PRomop表明，基于标准纵向记录的扁平投影是一种用于分析、人工智能/机器学习、临床试验匹配和决策支持的实用的、已部署的架构。
英文摘要
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.
