title: EHRCon: Dataset for Checking Consistency between Unstructured Notes and
    Structured Tables in Electronic Health Records
summary: 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.
paper_authors._id: 667ba4b153401797def767cf
paper_authors.name: Yeonsu Kwon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-27T15:40:16.476Z
paper_authors._id: 627a9619f967ef475abe659a
paper_authors.avatarUrl: /avatars/ed3983bd3bc574689fb2fc304f6cdb87.svg
paper_authors.fullname: Kwon
paper_authors.type: user
paper_authors.user: Yeonsu
paper_authors._id: 667ba4b153401797def767d0
paper_authors.name: Jiho Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-26T08:03:06.146Z
paper_authors._id: 6630eaa52d2b3451e7c0f61e
paper_authors.avatarUrl: /avatars/d22517d4833f4255fd62fda5280bd8ca.svg
paper_authors.fullname: Jiho Kim
paper_authors.type: user
paper_authors.user: jiho283
paper_authors._id: 667ba4b153401797def767d1
paper_authors.name: Gyubok Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-27T15:40:22.946Z
paper_authors._id: 641a8db4ec1bc76e2da429ee
paper_authors.avatarUrl: /avatars/6fc39d69111bd5b84206482923b2f386.svg
paper_authors.fullname: Gyubok Lee
paper_authors.type: user
paper_authors.user: glee4810
paper_authors._id: 667ba4b153401797def767d2
paper_authors.name: Seongsu Bae
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-27T15:40:29.446Z
paper_authors._id: 6184c8a995163d959496398e
paper_authors.avatarUrl: /avatars/efab3f49df22790191c681c0b60ef2ec.svg
paper_authors.fullname: Seongsu Bae
paper_authors.type: user
paper_authors.user: seongsubae
paper_authors._id: 667ba4b153401797def767d3
paper_authors.name: Daeun Kyung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-27T15:40:35.669Z
paper_authors._id: 645cd00f5ebf379fd6d7a4c1
paper_authors.avatarUrl: /avatars/be8375ed32ac234919e26a2450bf9d38.svg
paper_authors.fullname: Daeun Kyung
paper_authors.type: user
paper_authors.user: dek924
paper_authors._id: 667ba4b153401797def767d4
paper_authors.name: Wonchul Cha
paper_authors._id: 667ba4b153401797def767d5
paper_authors.name: Tom Pollard
paper_authors._id: 667ba4b153401797def767d6
paper_authors.name: Alistair Johnson
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-27T15:41:29.764Z
paper_authors._id: 62d1958b2ad06bbc8923451e
paper_authors.avatarUrl: /avatars/67268a58fdcacc910b9adbebb47c3c40.svg
paper_authors.fullname: Alistair Johnson
paper_authors.type: user
paper_authors.user: alistairewj
paper_authors._id: 667ba4b153401797def767d7
paper_authors.name: Edward Choi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-27T15:41:37.070Z
paper_authors._id: 65e8fb912dcdb39d1934260c
paper_authors.avatarUrl: /avatars/358504b57f2a600753e9bc459091061b.svg
paper_authors.fullname: Edward Choi
paper_authors.type: user
paper_authors.user: forgetnight
paper_title: EHRCon: Dataset for Checking Consistency between Unstructured Notes and
    Structured Tables in Electronic Health Records
paper_summary: 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.
paper_ai_keywords: EHRs (Electronic Health Records) EHRCon MIMIC-III OMOP CDM large language models CheckEHR