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如何利用永不发生事件数据来反映或提高医院的安全绩效?

How can Never Event data be used to reflect or improve hospital safety performance?

机构信息

Department of Respiratory Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

Nuffield Department of Anaesthesia, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

出版信息

Anaesthesia. 2021 Dec;76(12):1616-1624. doi: 10.1111/anae.15476. Epub 2021 May 1.

DOI:10.1111/anae.15476
PMID:33932033
Abstract

The absolute number of Never Events is used by UK regulators to help assess hospital safety performance, without account of hospital workload. We applied funnel plots, as an established means of taking workload into account, to published Never Event data for 151 acute Trusts in NHS England, matched to finished consultant episodes for 3 years, 2017-2020. Trusts with excess event rates should have the most Never Events if absolute number is a valid way to judge performance. The absolute number of Never Events was correlated with workload (r  = 0.51, p < 0.001), but the five Trusts above the upper 95% confidence limit did not have the highest number of Never Events. However, a limitation to interpretation was that the data were skewed; 12 out of 151 Trusts lay below the lower 95% limit. This skew probably arises because funnel plots pool all Never Events and workload data; whereas, ideally, different Never Events should use as denominator only the relevant workload actions that could cause them. We conclude that the manner in which Never Event data are currently used by regulators, in part to judge or rate hospitals, is mathematically invalid. The focus should shift from identifying 'outlier' hospitals to reducing the overall national mean Never Event rate through shared learning and an integrated system-wide approach.

摘要

英国监管机构使用绝对数量的“永不事件”来帮助评估医院的安全绩效,而不考虑医院的工作量。我们应用了漏斗图,这是一种已被证实的考虑工作量的方法,将英国国民保健服务体系英格兰的 151 家急性信托基金的已发表的“永不事件”数据与 2017 年至 2020 年的 3 年完成顾问期相匹配。如果绝对数量是判断绩效的有效方法,那么事件发生率过高的信托基金应该有最多的“永不事件”。“永不事件”的绝对数量与工作量相关(r=0.51,p<0.001),但在上限 95%置信区间之上的五家信托基金并没有发生最多的“永不事件”。然而,解释的一个限制是数据存在偏倚;151 家信托基金中有 12 家位于下限 95%置信区间以下。这种偏倚可能是由于漏斗图汇总了所有的“永不事件”和工作量数据;而理想情况下,不同的“永不事件”应该只使用可能导致这些事件的相关工作量操作作为分母。我们得出结论,监管机构目前使用“永不事件”数据的方式,部分是为了评估或评级医院,在数学上是无效的。重点应该从识别“异常”医院转移到通过共享学习和整体系统方法降低全国平均“永不事件”率。

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