  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. 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.
