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Purpose <p>Electronic health record (EHR) data have become essential and commonplace in epidemiological and clinical research. In this narrative review on the use of EHR data in epidemiology, I discuss appropriate research questions, common biases, and potential sensitivity analyses focusing on recent work that has been done to improve the internal and external validity of EHR-based studies.</p> Recent Findings <p>An appropriate research question addresses issues of EHR-data availability and accessibility, while patient selection forces into healthcare may result in a sample that lacks representativeness. Natural language processing tools are becoming widespread and tailored to EHR use for operationalizing unstructured data. Common biases identified in the literature include misclassification and measurement error, informed presence bias, selection bias and sampling error, and residual confounding. </p> Summary <p>EHR data are unlike other observational data sources and carry assumptions about patient selection and clinical documentation that can impact the validity of the analyses. Potential sensitivity analyses including quantitative bias analysis can help to understand the impact of one or more of these biases on the study findings.</p>

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Electronic Health Records in Epidemiology: Appropriate Questions, Common Biases, and Potential Sensitivity Analyses

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月5日

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  • Neal D. Goldstein

摘要

Purpose

Electronic health record (EHR) data have become essential and commonplace in epidemiological and clinical research. In this narrative review on the use of EHR data in epidemiology, I discuss appropriate research questions, common biases, and potential sensitivity analyses focusing on recent work that has been done to improve the internal and external validity of EHR-based studies.

Recent Findings

An appropriate research question addresses issues of EHR-data availability and accessibility, while patient selection forces into healthcare may result in a sample that lacks representativeness. Natural language processing tools are becoming widespread and tailored to EHR use for operationalizing unstructured data. Common biases identified in the literature include misclassification and measurement error, informed presence bias, selection bias and sampling error, and residual confounding.

Summary

EHR data are unlike other observational data sources and carry assumptions about patient selection and clinical documentation that can impact the validity of the analyses. Potential sensitivity analyses including quantitative bias analysis can help to understand the impact of one or more of these biases on the study findings.