CC BY-NC-ND 4.0 · Appl Clin Inform 2019; 10(01): 123-128
DOI: 10.1055/s-0039-1677738
Review Article
Georg Thieme Verlag KG Stuttgart · New York

Using Electronic Health Records to Identify Adverse Drug Events in Ambulatory Care: A Systematic Review

Chenchen Feng
1  Tulane University School of Medicine, Tulane University, New Orleans, Louisiana, United States
David Le
1  Tulane University School of Medicine, Tulane University, New Orleans, Louisiana, United States
Allison B. McCoy
2  Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States
› Author Affiliations
Funding Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number TL1TR001418. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Further Information

Publication History

13 September 2018

18 December 2018

Publication Date:
20 February 2019 (online)



Objective We identified the methods used and determined the roles of electronic health records (EHRs) in detecting and assessing adverse drug events (ADEs) in the ambulatory setting.

Methods We performed a systematic literature review by searching PubMed and Google Scholar for studies on ADEs detected in the ambulatory setting involving any EHR use published before June 2017. We extracted study characteristics from included studies related to ADE detection methods for analysis.

Results We identified 30 studies that evaluated ADEs in an ambulatory setting with an EHR. In 27 studies, EHRs were used only as the data source for ADE identification. In two studies, the EHR was used as both a data source and to deliver decision support to providers during order entry. In one study, the EHR was a source of data and generated patient safety reports that researchers used in the process of identifying ADEs. Methods of identification included manual chart review by trained nurses, pharmacists, and/or physicians; prescription review; computer monitors; electronic triggers; International Classification of Diseases codes; natural language processing of clinical notes; and patient phone calls and surveys. Seven studies provided examples of search phrases, laboratory values, and rules used to identify ADEs.

Conclusion The majority of studies examined used EHRs as sources of data for ADE detection. This retrospective approach is appropriate to measure incidence rates of ADEs but not adequate to detect preventable ADEs before patient harm occurs. New methods involving computer monitors and electronic triggers will enable researchers to catch preventable ADEs and take corrective action.

Protection of Human and Animal Subjects

No human and/or animal subjects were used in this review.

Supplementary Material