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

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.

Figure 1.. Comparison of the Regional Electronic Health Record and US Census Estimates, by Age Group and Area Deprivation Index

EHR data are reflective of the NEOCARE database, which includes patients from Cleveland Clinic Health System and MetroHealth System in northeast Ohio. Older patients were overrepresented in electronic health records data (larger hollow and diagonally-striped blocks in the EHR rows, as compared with the US Census rows). Representation across the socioeconomic spectrum (quintiles of the Area Deprivation Index) was consistent. EHR: Electronic health record. US Census: US Census Block Group.