Salim M Saiyed1,2, Katherine R Davis3,4, David C Kaelber4,5,6,7. 1. Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States. 2. CaroMont Health, Gastonia, North Carolina, United States. 3. Department of Family Medicine, The MetroHealth System, Case Western Reserve University, Cleveland, Ohio, United States. 4. The Center for Clinical Informatics Research and Education, The MetroHealth System, Case Western Reserve University, Cleveland, Ohio, United States. 5. Department of Internal Medicine, The MetroHealth System, Case Western Reserve University, Cleveland, Ohio, United States. 6. Department of Pediatrics, The MetroHealth System, Case Western Reserve University, Cleveland, Ohio, United States. 7. Department of Population and Quantitative Health Sciences, The MetroHealth System, Case Western Reserve University, Cleveland, Ohio, United States.
Abstract
BACKGROUND: Concerns about the number of automated medication alerts issued within the electronic health record (EHR), and the subsequent potential for alarm fatigue, led us to examine strategies and methods to optimize the configuration of our drug alerts. OBJECTIVES: This article reports on comprehensive drug alerting rates and develops strategies across two different health care systems to reduce the number of drug alerts. METHODS: Standardized reports compared drug alert rates between the two systems, among 13 categories of drug alerts. Both health care systems made modifications to the out-of-box alerts available from their EHR and drug information vendors, focusing on system-wide approaches, when relevant, while performing more drug-specific changes when necessary. RESULTS: Drug alerting rates even after initial optimization were 38 alerts and 51 alerts per 100 drug orders, respectively. Eight principles were identified and developed to reflect the themes in the implementation and optimization of drug alerting. CONCLUSION: A team-based, systematic approach to optimizing drug-alerting strategies can reduce the number of drug alerts, but alert rates still remain high. In addition to strategic principles, additional tactical guidelines and recommendations need to be developed to enhance out-of-the-box clinical decision support for drug alerts. Georg Thieme Verlag KG Stuttgart · New York.
BACKGROUND: Concerns about the number of automated medication alerts issued within the electronic health record (EHR), and the subsequent potential for alarm fatigue, led us to examine strategies and methods to optimize the configuration of our drug alerts. OBJECTIVES: This article reports on comprehensive drug alerting rates and develops strategies across two different health care systems to reduce the number of drug alerts. METHODS: Standardized reports compared drug alert rates between the two systems, among 13 categories of drug alerts. Both health care systems made modifications to the out-of-box alerts available from their EHR and drug information vendors, focusing on system-wide approaches, when relevant, while performing more drug-specific changes when necessary. RESULTS: Drug alerting rates even after initial optimization were 38 alerts and 51 alerts per 100 drug orders, respectively. Eight principles were identified and developed to reflect the themes in the implementation and optimization of drug alerting. CONCLUSION: A team-based, systematic approach to optimizing drug-alerting strategies can reduce the number of drug alerts, but alert rates still remain high. In addition to strategic principles, additional tactical guidelines and recommendations need to be developed to enhance out-of-the-box clinical decision support for drug alerts. Georg Thieme Verlag KG Stuttgart · New York.
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