用于自动估计缺席和持续药物警报的事件分析:新的方法论
Janina A Bittmann1, Camilo Scherkl2, Andreas D Meid2
1Internal Medicine IX: Department of Clinical Pharmacology and Pharmacoepidemiology, Cooperation Unit Clinical Pharmacy, Heidelberg University, Medical Faculty Heidelberg/Heidelberg University Hospital, Heidelberg, Germany.
一种自动事件分析方法有效地估计了计算机化医生订单输入系统中的药物警报接受率. 这种方法处理大量数据集,使得能够更好地监控临床决策支持系统的警报.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持系统 临床决策支持系统
- 药物安全 药物安全
背景情况:
- 在集成临床决策支持系统 (CDSS) 的计算机化医生订单输入 (CPOE) 系统中估计药物警报接受度至关重要.
- 事件分析提供了一个有前途的方法,特别是在手动审查耗时的非交互式警报中.
研究的目的:
- 引入和评估一种新的自动事件分析方法,用于评估药物警报的接受性.
- 将这种方法应用于来自带有被动警报的CPOE-CDSS的大数据集.
主要方法:
- 相关的药物警报数据与海德堡大学医院3.5个月的处方变化.
- 定义的警报是"持续"如果连续显示和"缺席"如果不再显示在正在进行的处方.
主要成果:
- 在1670例患者病例中分析了11,428个警报.
- 56.1%的警报成为"缺席",根据警报类型有显著的变化 (例如,药物相互作用的80.9%,PIM警报的39.9%).
结论:
- 自动事件分析方法有效评估被动的,不中断的警报.
- 能够处理大型纵向数据集,推导持续/缺席警报比率,并促进警报监控.
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