在统计过程控制分析中重新建立控制极限:稳定转移算法
Thomas Woodcock1, Imogen O'Connor2, Derek Bell3
1School of Public Health, Imperial College London, London, England, UK thomas.woodcock99@imperial.ac.uk.
BMJ quality & safety
|November 30, 2025
概括
稳定转换算法客观地确定何时更新统计过程控制 (SPC) 图的控制极限,以提高医疗保健质量. 这种方法可以防止过早的极限变化,确保对时间序列数据的可靠数据分析.
科学领域:
- 改善医疗保健质量 改善医疗保健质量
- 统计过程控制 统计过程控制
- 时间序列分析时间序列分析
背景情况:
- 统计过程控制 (SPC) 图表对于分析时间序列数据的医疗保健质量改善 (QI) 计划至关重要.
- 一个主要的挑战是缺乏一种标准化的方法来更新一旦建立的控制极限.
- 现有的方法可能导致过早或延迟的调整,损害数据完整性.
研究的目的:
- 引入稳定转移算法,客观地识别最佳点,以重新建立SPC图表上的控制极限.
- 确保算法遵守SPC理论,避免过早的极限重置,并保持QI从业人员的灵活性.
- 提供透明和自动化工具,用于管理QI分析中的控制极限.
主要方法:
- 开发了基于已建立的SPC轮班规则的稳定轮班算法.
- 进行了模拟研究,以评估算法的性能,以防止过早恢复极限.
- 在一个案例研究中,将算法应用于557个事故和紧急护理措施的时间序列.
主要成果:
- 模拟结果表明,该算法在避免过早重新设置控制限制方面比简单的转换规则更有效.
- 案例研究表明,算法的应用并没有导致过度的额外规则违反,这表明保持了过程表示.
- 算法成功地将时间序列数据根据控制极限稳定性划分为不同的时期.
结论:
- 稳定变速算法为SPC分析中更新控制极限提供了有价值,自动化和严格的解决方案.
- 它通过为大规模时间序列数据分析提供一致的方法来支持QI从业者和研究人员.
- 该算法提高了SPC图表的可靠性和透明度,以改善医疗保健质量.
相关概念视频
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