LAUSR

DDI alerts were identified and refined by using an interdisciplinary advisory group, metrics analysis, and surveys of clinicians’ perceived value to optimize alerts. OBJECTIVES: Excessive alerts are a common concern associated with clinical decision support systems that monitor drug-drug interactions (DDIs). To reduce the number of low-value interruptive DDI alerts at our hospital, we implemented an iterative, multidimensional quality improvement effort, which included an interdisciplinary advisory group, alert metrics, and measurement of perceived clinical value. METHODS: Alert data analysis indicated that DDIs were the most common interruptive medication alert. An interdisciplinary alert advisory group was formed to provide expert advice and oversight for alert refinement and ongoing review of alert data. Alert data were categorized into drug classes and analyzed to identify DDI alerts for refinement. Refinement strategies included alert suppression and modification of alerts to be contextually aware. RESULTS: On the basis of historical analysis of classified DDI alerts, 26 alert refinements were implemented, representing 47% of all alerts. Alert refinement efforts resulted in the following substantial decreases in the number of interruptive DDI alerts: 40% for all clinicians (22.9–14 per 100 orders) and as high as 82% for attending physicians (6.5–1.2 per 100 orders). Two patient safety events related to alert refinements were reported during the project period. CONCLUSIONS: Our quality improvement effort refined 47% of all DDI alerts that were firing during historical analysis, significantly reduced the number of DDI alerts in a 54-week period, and established a model for sustained alert refinements.