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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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Quantitating and assessing interoperability between electronic health records.

Elmer V Bernstam1,2, Jeremy L Warner3, John C Krauss4

  • 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

Journal of the American Medical Informatics Association : JAMIA
|January 11, 2022
PubMed
Summary
This summary is machine-generated.

Electronic health record (EHR) systems show limited interoperability for clinical data, even within the same vendor. Quantifying EHR interoperability is crucial for improving data sharing and healthcare delivery.

Keywords:
common data elementsdata aggregationdata managementdata warehousingelectronic health recordsinformation storage and retrieval

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Area of Science:

  • Health Informatics
  • Medical Data Standards
  • Clinical Data Interoperability

Background:

  • Electronic health records (EHRs) generate vast amounts of machine-readable data.
  • Variations in EHR vendor choices and site-specific implementations hinder data interoperability.
  • Standardization of clinically relevant data is often lacking compared to billing data.

Purpose of the Study:

  • To quantify the interoperability of real-world EHR implementations for clinically relevant structured data.
  • To assess the degree of data standardization across different EHR vendors and within specific vendor products.
  • To provide a quantitative measure for evaluating EHR system interoperability.

Main Methods:

  • Analysis of de-identified, aggregated data from 68 oncology sites using 5 different EHR vendor products.
  • Calculation of inter- and intra-EHR vendor interoperability scores using 6 key medications and 6 laboratory tests.
  • Development of a quantitative measure for EHR interoperability.

Main Results:

  • Mean intra-EHR vendor interoperability score was 0.68 (or 0.57 unweighted).
  • Mean inter-system interoperability score was 0.22 (or 0.20 unweighted).
  • Sites using the same EHR vendor demonstrated higher average interoperability.

Conclusions:

  • Clinically relevant data elements are infrequently standardized, despite existing standards.
  • Interoperability is not guaranteed even with the same EHR product implementation.
  • Quantitative interoperability measures can guide EHR selection, analysis, and optimization.