Clinical Informatics, ChristianaCare, Wilmington, DE, USA.
Health Informatics J. 2024 Jan-Mar;30(1):14604582241234252. doi: 10.1177/14604582241234252.
Clinical decision support (CDS) alerts are designed to work according to a set of clearly defined criteria and have the potential to improve clinical care. To efficiently and proactively review abnormally functioning CDS alerts, we postulate that the introduction of a dashboard with statistical process control (SPC) charting will lead to effective detection of erratic alert behavior. We identified custom CDS alerts from an academic medical center that were recorded and monitored in a longitudinal fashion and the data warehouses where this information was stored. We created a dashboard of alert frequency using SPC charts, applied SPC rules for classification of variation, and validated dashboard data. From June-August 2022, the dashboard effectively pulled in data to visually depict alert behavior. SPC-defined parameters for standard deviation from the mean were applied to visualizations and allowed for rapid review of alerts with greatest variation. These alerts were subsequently investigated, and it was determined that they were functioning correctly. The most profound abnormalities detected during implementation reflected changes in practice and not system errors, though further investigation into thresholds for statistical significance will benefit this field. We conclude that SPC visualizations are a time-efficient and effective method of identifying CDS malfunctions.
临床决策支持 (CDS) 警报旨在根据一组明确规定的标准运行，并有可能改善临床护理。为了高效、主动地审查异常功能的 CDS 警报，我们假设引入带有统计过程控制 (SPC) 图表的仪表板将能够有效检测到不规则的警报行为。我们从一家学术医疗中心中确定了定制的 CDS 警报，这些警报以纵向方式进行记录和监测，相关信息存储在数据仓库中。我们创建了一个使用 SPC 图表显示警报频率的仪表板，应用了 SPC 规则对变化进行分类，并验证了仪表板数据。从 2022 年 6 月至 8 月，该仪表板有效地提取数据以直观地描绘警报行为。应用了平均值标准差的 SPC 定义参数进行可视化处理，以便快速审查变化最大的警报。随后对这些警报进行了调查，确定它们运行正常。在实施过程中检测到的最显著异常反映了实践中的变化，而不是系统错误，但进一步研究统计显著性的阈值将有益于这一领域。我们得出结论，SPC 可视化是识别 CDS 故障的一种高效、有效的方法。
