EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records
This addresses data consistency issues in EHRs for healthcare professionals, but it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of discrepancies between structured tables and unstructured notes in Electronic Health Records, which pose risks to patient safety, by developing EHRCon, a dataset with 4,101 manually annotated entities across 105 clinical notes, and introducing CheckEHR, a framework that shows promising results for consistency verification.
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 4,101 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. Furthermore, leveraging the capabilities of large language models, we introduce CheckEHR, a novel framework for verifying the consistency between clinical notes and database tables. CheckEHR utilizes an eight-stage process and shows promising results in both few-shot and zero-shot settings. The code is available at https://github.com/dustn1259/EHRCon.