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

Chenchen Feng; David Le; Allison B. McCoy

doi:10.1055/s-0039-1677738

Applied Clinical Informatics, Table of Contents

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

¹Tulane University School of Medicine, Tulane University, New Orleans, Louisiana, United States

,

David Le

¹Tulane University School of Medicine, Tulane University, New Orleans, Louisiana, United States

,

Allison B. McCoy

²Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States

› Author Affiliations

Abstract

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.

Keywords

review - drug-related side effects and adverse reactions - adverse drug reaction reporting systems - medication errors - medical records systems - computerized - ambulatory care

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References

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Supplementary Material