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Journal of the American Medical Informatics Association 9:S80-S85 (2002)
© 2002 American Medical Informatics Association


Article

Developing a Taxonomy for Research in Adverse Drug Events: Potholes and Signposts

Jonathan R. Nebeker, MS, MD, John F. Hurdle, MD, PhD, Jennifer M. Hoffman, PharmD, Beverly Roth, PharmD, Charlene R. Weir, PhD and Matthew H. Samore, MD

Affiliations of the authors: Veterans Administration Salt Lake City Health Care System Geriatrics Research, Education, and Care Center (GRECC), Salt Lake City Utah (JRN, JFH, JMH, BR, CRW); University of Utah Health Science Center, Salt Lake City Utah (JRN, JFH, CRW, MHS).

Abstract

Computerized decision support and order entry shows great promise for reducing adverse drug events (ADEs). The evaluation of these solutions depends on a framework of definitions and classifications that is clear and practical. Unfortunately the literature does not always provide a clear path to defining and classifying adverse drug events. While not a systematic review, this paper uses examples from the literature to illustrate problems that investigators will confront as they develop a conceptual framework for their research. It also proposes a targeted taxonomy that can facilitate a clear and consistent approach to the research of ADEs and aid in the comparison to results of past and future studies. This paper outlines the ambiguity in definitions of ADEs that has arisen from the conflation of regulatory and quality terminology. It proposes a typology for ADEs by drug and disease effect and outlines problems inherent in the study of ADEs related to disease effects, errors, and omitted therapies. The paper also highlights difficulty in assessing seriousness and causality and the problems with commonly used scales for these assessments. Finally, although national or international agreement on taxonomy for ADEs is a distant or unachievable goal, individual investigations and the literature as a whole will be improved by prospective, explicit classification of ADEs and inclusion of the study’s approach to classification in publications.







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Copyright © 2002 by the American Medical Informatics Association.