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. 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.
667ba4b153401797def767cf
Yeonsu Kwon
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Kwon
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Jiho Kim
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Jiho Kim
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Gyubok Lee
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Gyubok Lee
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Seongsu Bae
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2024-06-27T15:40:29.446Z
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Seongsu Bae
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seongsubae
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Daeun Kyung
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Daeun Kyung
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dek924
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Wonchul Cha
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Tom Pollard
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Alistair Johnson
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2024-06-27T15:41:29.764Z
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Alistair Johnson
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alistairewj
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Edward Choi
admin_assigned
2024-06-27T15:41:37.070Z
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Edward Choi
user
forgetnight
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. 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.
A new dataset and framework, EHRCon and CheckEHR, ensure consistency between structured and unstructured data in EHRs using manual annotations and large language models.
structured data clinical notes EHRCon OMOP CDM schema CheckEHR large language models