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

• Published in: Drug safety Vol. 46; no. 8; pp. 725 - 742
• Main Authors: Davis, Sharon E., Zabotka, Luke, Desai, Rishi J., Wang, Shirley V., Maro, Judith C., Coughlin, Kevin, Hernández-Muñoz, José J., Stojanovic, Danijela, Shah, Nigam H., Smith, Joshua C.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2023
• Subjects: Adverse Drug Reaction Reporting Systems; Data Mining; Drug Safety and Pharmacovigilance; Electronic Health Records; Humans; Medicine; Medicine & Public Health; Pharmacology/Toxicology; Pharmacovigilance; Scoping Review
• ISSN: 0114-5916; 1179-1942
• DOI: 10.1007/s40264-023-01325-0

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.