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

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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.

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Authors and Affiliations

  1. Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Ave, Suite 1475, Nashville, TN, 37203, USA

    Sharon E. Davis & Joshua C. Smith

  2. Vanderbilt University School of Medicine, Nashville, TN, USA

    Sharon E. Davis & Joshua C. Smith

  3. Brigham and Women’s Hospital, Boston, MA, USA

    Luke Zabotka, Rishi J. Desai & Shirley V. Wang

  4. Harvard Medical School, Boston, MA, USA

    Rishi J. Desai, Shirley V. Wang & Judith C. Maro

  5. Harvard Pilgrim Health Care Institute, Boston, MA, USA

    Judith C. Maro & Kevin Coughlin

  6. US FDA, Silver Spring, MD, USA

    José J. Hernández-Muñoz & Danijela Stojanovic

  7. School of Medicine, Stanford University, Stanford, CA, USA

    Nigam H. Shah

  8. Stanford Health Care, Palo Alto, CA, USA

    Nigam H. Shah

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  1. Sharon E. Davis
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  10. Joshua C. Smith
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Corresponding author

Correspondence to Joshua C. Smith.

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Funding

This project was supported by Task Order 7540119F19002 under Master Agreement 75F40119D10037 from the US FDA. The FDA reviewed and approved this manuscript, however they had no role in data collection, management, or analysis. The views expressed represent those of the authors and do not necessarily represent the official views of the FDA.

Conflict of interest

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 declare no competing interests.

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Not applicable.

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Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Author contributions

SED and JCS designed the study and drafted the initial manuscript. SED, JCS, LZ, and KC contributed to the abstract screening and data extraction for the included studies. RJD, SVW JCM, JJH, DS, and NHS provided critical review of the included studies and interpretation of the results. All authors contributed to the final version of the manuscript and approved submission.

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Davis, S.E., Zabotka, L., Desai, R.J. et al. Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review. Drug Saf 46, 725–742 (2023). https://doi.org/10.1007/s40264-023-01325-0

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