Assessing the performance characteristics of signals used by a clinical event monitor to detect adverse drug reactions in the nursing home

Handler, SM, Hanlon, JT, Perera, S, Saul, MI, Fridsma, DB, Visweswaran, S, Studenski, SA, Roumani, YF, Castle, NG, Nace, DA, Becich, MJ. Assessing the performance characteristics of signals used by a clinical event monitor to detect adverse drug reactions in the nursing home. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (Nov 2008) 278-82. PMID: 18998853 PMCID: PMC2656060

Adverse drug reactions (ADRs) are a common cause of morbidity and mortality in the nursing home (NH) setting. Traditional non-automated mechanisms for ADR detection are time-consuming, costly, and fail to detect the majority of ADRs. We describe the implementation and pharmacist evaluation of a clinical event monitor using signals previously developed by our research team to detect potential ADRs in the NH. The overall positive predictive value (PPV) for all signals combined was 81% (54/67), with individual signal PPVs ranging from 0- 100%. The PPVs were 53% (10/19) for the antidote signals category and 96% (44/46) for the laboratory/ medication combination signals category. The majority 75% (12/16) of the preventable ADRs were laboratory/medication combination signals. The results suggest that ADRs can be detected in the NH setting with a high degree of accuracy using a clinical event monitor that employs a set of signals derived by expert consensus.

Publication Year: 
2008
Publication Credits: 
Steven M. Handler, Joseph T. Hanlon, Subashan Perera, Melissa I. Saul, Douglas B. Fridsma, Stephanie A. Studenski, Yazan F. Roumani, Nicholas G. Castle, David A. Nace, Michael J. Becich
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