Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review

Sharon E. Davis, Luke Zabotka, Rishi J. Desai, Shirley V. Wang, Judith C. Maro, Kevin Coughlin, José J. Hernández-Muñoz, Danijela Stojanovic, Nigam H. Shah and Joshua C. Smith ()
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Sharon E. Davis: Vanderbilt University Medical Center
Luke Zabotka: Brigham and Women’s Hospital
Rishi J. Desai: Brigham and Women’s Hospital
Shirley V. Wang: Brigham and Women’s Hospital
Judith C. Maro: Harvard Medical School
Kevin Coughlin: Harvard Pilgrim Health Care Institute
José J. Hernández-Muñoz: US FDA
Danijela Stojanovic: US FDA
Nigam H. Shah: Stanford University
Joshua C. Smith: Vanderbilt University Medical Center

Drug Safety, 2023, vol. 46, issue 8, No 3, 725-742

Abstract: Abstract Introduction Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance. Methods To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices. Results We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations. Conclusion Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance.

Date: 2023
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DOI: 10.1007/s40264-023-01325-0

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