Optimizing Drug-Drug Interaction Alerts Using a Multidimensional Approach
Optimizing Drug-Drug Interaction Alerts Using a Multidimensional Approach
复制标题
DOI：
10.1542/peds.2017-4111
复制
发表时间：
2019-03-01
期刊：
影响因子：
8
通讯作者：
Hoffman, James M.
中科院分区：
文献类型：
--
作者：
Daniels, Calvin C.;Burlison, Jonathan D.;Hoffman, James M.
警告：过度警报是与监测药物相互作用（DDI）的临床决策支持系统相关的常见问题。为了减少在我们医院的低价值中断DDI警报的数量，我们实施了一个迭代的，多方面的质量改进工作，其中包括一个跨学科的咨询小组，警报指标，并测量感知的临床value.METHODS：警报数据分析表明，DDI是最常见的中断用药警报。成立了一个跨学科警报咨询小组，为警报的完善和警报数据的持续审查提供专家咨询和监督。将警报数据分类为药物类别并进行分析，以确定DDI警报进行细化。细化策略包括警报抑制和修改的警报是contextuous.RESULTS：分类DDI警报的历史分析的基础上，26警报细化实施，占所有警报的47%。警报优化工作导致以下中断DDI警报数量大幅减少：所有临床医生减少40%（每100个订单中有22.9 - 14个），主治医生减少82%（每100个订单中有6.5 - 1.2个）。两个病人的安全事件相关的警报refinements在项目periods.CONCLUSIONS：我们的质量改进工作完善了47%的所有DDI警报，在历史分析期间发射，显着减少DDI警报的数量在54周内，并建立了一个持续的警报细化模型。
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.