EHR2Trace: Auditable EHR Data Infrastructure for Patient World Models and Clinical Agents

EHR2Trace：用于患者世界模型和临床智能体的可审计电子健康记录（EHR）数据基础设施

Abstract: Patient world models and clinical agents aim to predict changes in patients’ health and support clinical work. Developing these systems requires reliable histories of patient conditions, treatments, and the information available at each decision. Electronic health records (EHRs) contain these histories, but differences in how events are recorded make them difficult to use consistently.

摘要： 患者世界模型和临床智能体旨在预测患者健康状况的变化并辅助临床工作。开发这些系统需要可靠的患者病情、治疗方案以及在每个决策点所获信息的历史记录。电子健康记录（EHR）包含了这些历史信息，但由于事件记录方式的差异，使得它们难以被一致性地使用。

We present EHR2Trace, a system that converts EHRs from different sources into traceable patient events for model training and evaluation. It links events to source records, separates event time from information availability, and distinguishes medication orders, dispensing, and administration. A shared event representation supports both OMOP and MEDS exports, with automated validation and reproducible builds.

我们提出了 EHR2Trace，这是一个将来自不同来源的 EHR 转换为可追溯患者事件的系统，用于模型训练和评估。它将事件与源记录相关联，将事件发生时间与信息可用性区分开来，并明确区分了药物医嘱、配药和给药过程。共享的事件表示支持 OMOP 和 MEDS 导出，并具备自动化验证和可重复构建的功能。

Across three clinical datasets, EHR2Trace converted 846.4 million events, with every applicable check passing except one unit-consistency check on MIMIC-IV, and detected all 28 injected faults. A controlled prediction experiment showed that assigning later diagnoses to admission time substantially inflated measured performance, and that a model trained on such data lost accuracy when deployed on histories filtered by availability.

在三个临床数据集上，EHR2Trace 转换了 8.464 亿条事件，除 MIMIC-IV 上的一项单位一致性检查外，所有适用检查均通过，并检测出了所有 28 个注入的故障。一项受控预测实验表明，将后续诊断分配给入院时间会大幅虚高测量出的性能，且在基于可用性过滤的历史记录上部署时，使用此类数据训练的模型会出现准确率下降。

EHR2Trace provides a reusable data foundation for patient world models and clinical agents, helping researchers inspect patient histories, check conversion decisions, and evaluate models with explicit data rules.

EHR2Trace 为患者世界模型和临床智能体提供了一个可重用的数据基础，帮助研究人员检查患者历史记录、核对转换决策，并利用明确的数据规则评估模型。