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

Source: [EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records](<https://www.sota2.com/research/paper/68fa76731e209131cea6d110>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:54:27.044Z

Authors: Yeonsu Kwon, Jiho Kim, Gyubok Lee, Seongsu Bae, Daeun Kyung, Wonchul Cha, Tom Pollard, Alistair Johnson, Edward Choi

Published: 2024-06-24T06:26:50.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Note-table consistency verification on EHRCon Discharge notes](<https://www.sota2.com/research/sota/note-table-consistency-verification-on-ehrcon-discharge-notes.md>): Note-table consistency verification; EHRCon Discharge notes; Recall: 48.8.

- [Note-table consistency verification on EHRCon Physician notes](<https://www.sota2.com/research/sota/note-table-consistency-verification-on-ehrcon-physician-notes.md>): Note-table consistency verification; EHRCon Physician notes; Recall: 28.05.

- [Note-table consistency verification on EHRCon Nursing notes](<https://www.sota2.com/research/sota/note-table-consistency-verification-on-ehrcon-nursing-notes.md>): Note-table consistency verification; EHRCon Nursing notes; Recall: 38.07.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.16341>)

[PDF](<https://arxiv.org/pdf/2406.16341.pdf>)