Opportunities, Pitfalls, and Alternatives in Adapting Electronic Health Records for Health Services Research

Glen B. Taksler, Jarrod E. Dalton, Adam T. Perzynski, Michael B. Rothberg, Alex Milinovich, Nikolas I. Krieger, Neal V. Dawson, Mary J. Roach, Michael D. Lewis and Douglas Einstadter
Additional contact information
Glen B. Taksler: Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
Jarrod E. Dalton: Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
Adam T. Perzynski: Center for Health Care Research and Policy, The MetroHealth System and Case Western Reserve University, Cleveland, OH, USA
Michael B. Rothberg: Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
Alex Milinovich: Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
Nikolas I. Krieger: Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
Neal V. Dawson: Center for Health Care Research and Policy, The MetroHealth System and Case Western Reserve University, Cleveland, OH, USA
Mary J. Roach: Center for Health Care Research and Policy, The MetroHealth System and Case Western Reserve University, Cleveland, OH, USA
Michael D. Lewis: Center for Health Care Research and Policy, The MetroHealth System and Case Western Reserve University, Cleveland, OH, USA
Douglas Einstadter: Center for Health Care Research and Policy, The MetroHealth System and Case Western Reserve University, Cleveland, OH, USA

Medical Decision Making, 2021, vol. 41, issue 2, 133-142

Abstract: Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets.

Keywords: data science; electronic health records; health services research; registries (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:41:y:2021:i:2:p:133-142

DOI: 10.1177/0272989X20954403

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