Department of Neurology, Amsterdam University Medical Centres, Amsterdam, The Netherlands.
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Zuid-Holland, The Netherlands.
BMJ Qual Saf. 2021 Jan;30(1):38-45. doi: 10.1136/bmjqs-2019-009929. Epub 2020 Feb 7.
Hospitals and providers receive feedback information on how their performance compares with others, often using funnel plots to detect outliers. These funnel plots typically use binary outcomes, and continuous variables are dichotomised to fit this format. However, information is lost using a binary measure, which is only sensitive to detect differences in higher values (the tail) rather than the entire distribution. This study therefore aims to investigate whether different outlier hospitals are identified when using a funnel plot for a binary vs a continuous outcome. This is relevant for hospitals with suboptimal performance to decide whether performance can be improved by targeting processes for all patients or a subgroup with higher values.
We examined the door-to-needle time (DNT) of all (6080) patients with acute ischaemic stroke treated with intravenous thrombolysis in 65 hospitals in 2017, registered in the Dutch Acute Stroke Audit. We compared outlier hospitals in two funnel plots: the median DNT versus the proportion of patients with substantially delayed DNT (above the 90th percentile (P90)), whether these were the same or different hospitals. Two sensitivity analyses were performed using the proportion above the median and a continuous P90 funnel plot.
The median DNT was 24 min and P90 was 50 min. In the binary funnel plot for the proportion of patients above P90, 58 hospitals had average performance, whereas in the funnel plot around the median 14 of these hospitals had significantly higher median DNT (24%). These hospitals can likely improve their DNT by focusing on care processes for all patients, not shown by the binary outcome funnel plot. Similar results were shown in sensitivity analyses.
Using funnel plots for continuous versus binary outcomes identify different outlier hospitals, which may enhance hospital feedback to direct more targeted improvement initiatives.
医院和医疗服务提供者会收到有关其绩效与他人相比的反馈信息，通常使用漏斗图来检测异常值。这些漏斗图通常使用二分类结果，连续变量被二分类以适应这种格式。然而，使用二进制测量会丢失信息，这种方法仅对检测较高值（尾部）的差异敏感，而不能检测整个分布的差异。因此，本研究旨在探讨使用二分类和连续结果的漏斗图时，是否会识别出不同的异常值医院。对于绩效不理想的医院，这一点很重要，可以决定是否可以通过针对所有患者或较高值患者亚组来改善绩效。
我们检查了 2017 年在 65 家荷兰急性卒中审计注册的 6080 例接受静脉溶栓治疗的急性缺血性卒中患者的门到针时间（DNT）。我们比较了两种漏斗图中的异常值医院：中位数 DNT 与显著延迟 DNT（超过第 90 百分位数（P90）的患者比例），这些是否为相同或不同的医院。使用中位数以上的比例和连续 P90 漏斗图进行了两次敏感性分析。
中位数 DNT 为 24 分钟，P90 为 50 分钟。在超过 P90 的患者比例的二分类漏斗图中，58 家医院的表现平均，而在中位数周围的漏斗图中，其中 14 家医院的中位数 DNT 显著较高（24%）。这些医院可能可以通过关注所有患者的护理流程来改善 DNT，而不是通过二分类结果漏斗图显示。敏感性分析也得到了类似的结果。
使用连续和二分类结果的漏斗图可以识别出不同的异常值医院，这可以增强医院反馈，以指导更有针对性的改进举措。
