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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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Electronic Health Record Data Quality and Performance Assessments: Scoping Review.

Yordan P Penev1,2, Timothy R Buchanan1,2, Matthew M Ruppert1,2,3

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JMIR Medical Informatics
|November 6, 2024
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Summary
This summary is machine-generated.

Improving electronic health record (EHR) data quality and performance is crucial for medical research. This review highlights best practices and the growing role of AI in enhancing EHR data for better clinical insights.

Keywords:
EHRclinical informaticsdata performancedata qualitydata scienceelectronic health recordperformancerecordreview methodologyreview methodsscopingsearchsynthesis

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Area of Science:

  • Medical Informatics
  • Health Data Science
  • Clinical Research Methodology

Background:

  • Electronic Health Records (EHRs) offer vast potential for medical research via accessible databases.
  • Realizing this potential is hindered by challenges in EHR data quality (DQ) and performance assessment.

Purpose of the Study:

  • To review and standardize best practices for EHR data quality and performance assessments.
  • To establish a replicable standard for researchers in the field.

Main Methods:

  • Systematic PubMed search for original research articles on EHR DQ and performance.
  • Data collected until May 7, 2023.

Main Results:

  • 26 articles were reviewed, with common limitations including reporting inconsistencies, poor replicability, and limited generalizability.
  • Key DQ indicators were completeness, conformance, and plausibility; correctness/accuracy was primary for performance.
  • Artificial intelligence (AI) techniques show promise in improving EHR dataset quality and performance.

Conclusions:

  • Standardization and incentivization of EHR DQ and performance assessments are necessary.
  • AI-based techniques can enhance EHR data quality and performance, unlocking their full potential for medical research and practice.