Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA.
Associates in Process Improvement, 2000 Red Hawk Road, Wimberly, TX 78676, USA.
Clin Perinatol. 2023 Jun;50(2):321-341. doi: 10.1016/j.clp.2023.02.004. Epub 2023 Mar 27.
Effective quality improvement (QI) depends on rigorous analysis of time-series data through methods such as statistical process control (SPC). As use of SPC has become more prevalent in health care, QI practitioners must also be aware of situations that warrant special attention and potential modifications to common SPC charts, which include skewed continuous data, autocorrelation, small persistent changes in performance, confounders, and workload or productivity measures. This article reviews these situations and provides examples of SPC approaches for each.
有效的质量改进（QI）取决于通过统计过程控制（SPC）等方法对时间序列数据进行严格分析。随着 SPC 在医疗保健中的应用越来越普遍，QI 从业者还必须意识到需要特别注意的情况以及对常见 SPC 图表进行潜在修改的情况，这些情况包括偏态连续数据、自相关、性能的小持续变化、混杂因素以及工作量或生产力措施。本文回顾了这些情况，并为每种情况提供了 SPC 方法的示例。
