通过统计过程控制检测非典型的警报行为:临床决策支持警报频率可视化
Kevin E Kindler1, Peter J Martinson1
1Clinical Informatics, ChristianaCare, Wilmington, DE, USA.
Health informatics journal
|February 17, 2024
概括
在仪表板上绘制的统计过程控制 (SPC) 图表有效地检测出异常的临床决策支持 (CDS) 警报行为. 这种方法有效地识别了CDS故障,提高了医疗保健质量.
科学领域:
- 医疗信息学 医疗信息学
- 改善临床质量 改善临床质量
- 数据可视化 数据可视化
背景情况:
- 临床决策支持 (CDS) 警报旨在通过预定义的标准来增强患者的护理.
- 积极监测CDS警报功能对于保持系统完整性和临床有效性至关重要.
- 现有的审查警报表现的方法在检测微妙或不稳定的行为方面可能缺乏效率.
研究的目的:
- 评估一个包含统计过程控制 (SPC) 图表的仪表板的有效性,以识别故障的CDS警报.
- 建立一个主动和高效的系统来监测定制CDS警报的性能.
- 区分系统错误和影响警报行为的实践相关变化.
主要方法:
- 从一个学术医疗中心的定制CDS警报中收集了纵向数据.
- 使用SPC图表开发了一个仪表板,以可视化警报频率和行为.
- 应用了SPC规则来分类变异,并验证了仪表板数据.
- 研究了与平均值有显著偏差的警报.
主要成果:
- SPC图表仪表板成功可视化了2022年6月至8月的警报行为.
- 该系统迅速识别出具有最大变异的警报,随后确认其正确运行.
- 检测到的最显著的异常表明了临床实践的变化,而不是系统功能障碍.
- 建议对统计显著性值进行进一步的研究.
结论:
- SPC可视化提供了一种节省时间和有效的方法来检测CDS警报故障.
- 仪表板有助于主动识别系统异常,支持持续质量改进.
- 这种方法有助于区分系统错误与实践变化,优化CDS警报管理.
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