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构建质量指标漏斗图指南:以重症监护病房患者死亡率为例的研究。

Guidelines on constructing funnel plots for quality indicators: A case study on mortality in intensive care unit patients.

机构信息

1 Academic Medical Center, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.

2 Dutch National Intensive Care Registry (NICE), Amsterdam, The Netherlands.

出版信息

Stat Methods Med Res. 2018 Nov;27(11):3350-3366. doi: 10.1177/0962280217700169. Epub 2017 Mar 23.

DOI:10.1177/0962280217700169
PMID:28330409
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6193208/
Abstract

Funnel plots are graphical tools to assess and compare clinical performance of a group of care professionals or care institutions on a quality indicator against a benchmark. Incorrect construction of funnel plots may lead to erroneous assessment and incorrect decisions potentially with severe consequences. We provide workflow-based guidance for data analysts on constructing funnel plots for the evaluation of binary quality indicators, expressed as proportions, risk-adjusted rates or standardised rates. Our guidelines assume the following steps: (1) defining policy level input; (2) checking the quality of models used for case-mix correction; (3) examining whether the number of observations per hospital is sufficient; (4) testing for overdispersion of the values of the quality indicator; (5) testing whether the values of quality indicators are associated with institutional characteristics; and (6) specifying how the funnel plot should be constructed. We illustrate our guidelines using data from the Dutch National Intensive Care Evaluation registry. We expect that our guidelines will be useful to data analysts preparing funnel plots and to registries, or other organisations publishing quality indicators. This is particularly true if these people and organisations wish to use standard operating procedures when constructing funnel plots, perhaps to comply with the demands of certification.

摘要

漏斗图是一种图形工具,用于评估和比较一组医疗保健专业人员或医疗机构在质量指标上的临床表现与基准。漏斗图的构建不正确可能导致错误的评估和错误的决策,可能会产生严重的后果。我们为数据分析师提供了针对二进制质量指标(表示为比例、风险调整率或标准化率)评估构建漏斗图的基于工作流程的指导。我们的指南假设以下步骤:(1)定义政策水平输入;(2)检查用于病例组合校正的模型的质量;(3)检查每个医院的观察数量是否足够;(4)测试质量指标值的过分散性;(5)测试质量指标值是否与机构特征相关;(6)指定如何构建漏斗图。我们使用荷兰国家重症监护评估登记处的数据来说明我们的指南。我们希望我们的指南对准备漏斗图的数据分析师以及发布质量指标的登记处或其他组织有用。如果这些人和组织希望在构建漏斗图时使用标准操作程序,也许是为了遵守认证的要求,那么这尤其如此。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d12/6193208/c9c0692719d9/10.1177_0962280217700169-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d12/6193208/0468f09c586b/10.1177_0962280217700169-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d12/6193208/feafac4a7e46/10.1177_0962280217700169-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d12/6193208/726649d2e979/10.1177_0962280217700169-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d12/6193208/c9c0692719d9/10.1177_0962280217700169-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d12/6193208/0468f09c586b/10.1177_0962280217700169-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d12/6193208/feafac4a7e46/10.1177_0962280217700169-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d12/6193208/726649d2e979/10.1177_0962280217700169-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d12/6193208/c9c0692719d9/10.1177_0962280217700169-fig4.jpg

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