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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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Assessing EHR Data for Use in Clinical Improvement and Research.

Ann M Lyons1, Jonathan Dimas, Stephanie J Richardson

  • 1Ann M. Lyons is a medical informaticist at the University of Utah, Salt Lake City. Jonathan Dimas is the global medical affairs scientist at bioMérieux in Salt Lake City. Stephanie J. Richardson is retired from faculty and administrative positions at both the University of Utah College of Nursing and the Rocky Mountain University of Health Professions, Provo, UT. Katherine Sward is a professor of nursing in the University of Utah College of Nursing as well as an adjunct professor of biomedical informatics in the School of Medicine. Contact author: Ann M. Lyons, ann.lyons@hsc.utah.edu . The authors have disclosed no potential conflicts of interest, financial or otherwise.

The American Journal of Nursing
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PubMed
Summary

Nurses can improve clinical care and research by learning to evaluate and clean electronic health record (EHR) data. This guide offers a systematic process for assessing EHR data quality for secondary analysis.

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Area of Science:

  • Nursing Informatics
  • Health Data Science
  • Clinical Research

Background:

  • Electronic health records (EHRs) offer valuable data for clinical improvement and nursing research.
  • However, EHR data quality often falls short of clinical and research requirements due to inherent errors and omissions.
  • Secondary analysis of EHR data requires careful data acquisition and quality assessment.

Purpose of the Study:

  • To introduce nurses to the secondary analysis of electronic health record (EHR) data.
  • To outline a theory-based process for evaluating and cleaning EHR data for research and clinical improvement.
  • To provide practical guidance on determining data fitness for use.

Main Methods:

  • Description of data acquisition steps for EHR data.
  • Presentation of a systematic, theory-based process for data quality evaluation.
  • Utilizing six data quality dimensions: completeness, correctness, concordance, plausibility, currency, and relevance.

Main Results:

  • A structured approach to examining EHR data quality is presented.
  • The process helps identify and address errors and omissions in EHR data.
  • Case studies illustrate common data quality problems and their resolutions.

Conclusions:

  • Nurses and researchers can enhance the reliability of EHR data through systematic quality evaluation and cleaning.
  • This methodology ensures that EHR data are fit for secondary analysis in clinical improvement and research.
  • Adopting these practices improves the utility of EHR data for evidence-based healthcare.