Developing a taxonomy for research in adverse drug events: potholes and signposts

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. The taxonomy addresses the definition of ADE, types, seriousness, error, and causality.

[1]  James G. Anderson,et al.  Evaluating the potential effectiveness of using computerized information systems to prevent adverse drug events , 1997, AMIA.

[2]  S D Small,et al.  Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. , 1995, JAMA.

[3]  J C Russo,et al.  Medication error prevention by clinical pharmacists in two children's hospitals. , 1987, Pediatrics.

[4]  G. Davey Smith,et al.  Should there be a moratorium on the use of cholesterol lowering drugs? , 1992, BMJ.

[5]  N. Laird,et al.  Incidence of Adverse Drug Events and Potential Adverse Drug Events: Implications for Prevention , 1995 .

[6]  Jonathan M. Teich,et al.  The impact of computerized physician order entry on medication error prevention. , 1999, Journal of the American Medical Informatics Association : JAMIA.

[7]  N. Dickey,et al.  Systems analysis of adverse drug events. , 1996, JAMA.

[8]  J. Cornell,et al.  Adverse Events Related to Drugs and Drug Withdrawal in Nursing Home Residents , 1993, Journal of the American Geriatrics Society.

[9]  I. Edwards,et al.  Adverse drug reactions: definitions, diagnosis, and management , 2000, The Lancet.

[10]  R S Evans,et al.  Computerized surveillance of adverse drug events in hospital patients* , 1991, Quality and Safety in Health Care.

[11]  Top-priority actions for preventing adverse drug events in hospitals. Recommendations of an expert panel. , 1996, American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists.

[12]  L. Kohn,et al.  To Err Is Human : Building a Safer Health System , 2007 .

[13]  C. Naranjo,et al.  Comparison of two recently published algorithms for assessing the probability of adverse drug reactions. , 1982, British journal of clinical pharmacology.

[14]  D. Classen,et al.  Adverse drug events in hospitalized patients. Excess length of stay, extra costs, and attributable mortality. , 1997, JAMA.

[15]  R S Evans,et al.  Prevention of adverse drug events through computerized surveillance. , 1992, Proceedings. Symposium on Computer Applications in Medical Care.

[16]  D. Greenblatt,et al.  A method for estimating the probability of adverse drug reactions , 1981, Clinical pharmacology and therapeutics.

[17]  D. Kessler.  Introducing MEDWatch: a new approach to reporting medication and device adverse effects and product problems , 1994 .

[18]  D A Kessler,et al.  Introducing MEDWatch. A new approach to reporting medication and device adverse effects and product problems. , 1993, General hospital psychiatry.

[19]  D. Bates,et al.  Systems analysis of adverse drug events. ADE Prevention Study Group. , 1995, JAMA.

[20]  D W Bates,et al.  Incidence and preventability of adverse drug events in nursing homes. , 2000, The American journal of medicine.