EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records

Electronic Health Records (EHRs) are integral for storing comprehensive patient medical records, combining structured data (e.g., medications) with detailed clinical notes (e.g., physician notes). These elements are essential for straightforward data retrieval and provide deep, contextual insights into patient care. However, they often suffer from discrepancies due to unintuitive EHR system designs and human errors, posing serious risks to patient safety. To address this, we developed EHRCon, a new dataset and task specifically designed to ensure data consistency between structured tables and unstructured notes in EHRs. EHRCon was crafted in collaboration with healthcare professionals using the MIMIC-III EHR dataset, and includes manual annotations of 3,943 entities across 105 clinical notes checked against database entries for consistency. EHRCon has two versions, one using the original MIMIC-III schema, and another using the OMOP CDM schema, in order to increase its applicability and generalizability.

电子健康记录（EHRs）对于存储全面的病人医疗记录至关重要，它将结构化数据（例如，药物信息）与详细的临床笔记（例如，医师笔记）相结合。这些元素对于直接的数据检索至关重要，并为病人护理提供深入、具体的洞察。然而，由于不直观的EHR系统设计和人为错误，它们往往存在差异，对病人安全构成严重风险。为了解决这一问题，我们开发了EHRCon，这是一个全新的数据集和任务，旨在确保EHRs中结构化表格与非结构化笔记之间的数据一致性。EHRCon是在与医疗保健专业人员合作下，基于MIMIC-III EHR数据集构建的，并包含对105篇临床笔记的3,943个实体的手动标注，这些标注均经过与数据库条目的一致性核对。EHRCon有两个版本，一个使用原始的MIMIC-III架构，另一个使用OMOP CDM架构，以提高其适用性和普适性。