文章基本信息

标题：Risk-adjustment of diabetes health outcomes improves the accuracy of performance benchmarking
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作者：Eleanor Danek ; Arul Earnest ; Natalie Wischer 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2018
卷号：8
期号：1
页码：10261
DOI：10.1038/s41598-018-28101-w
语种：English
出版社：Springer Nature
摘要：Benchmarking clinical performance by comparing diabetes health outcomes across healthcare providers drives quality improvement. Non-care related patient risk factors are likely to confound clinical performance, but few studies have tested this. This cross-sectional study is the first Australian investigation to analyse the effect of risk-adjustment for non-care related patient factors on benchmarking. Data from 4,670 patients with type 2 (n = 3,496) or type 1 (n = 1,174) were analysed across 49 diabetes centres. Diabetes health outcomes (HbA1c levels, LDL-cholesterol levels, systolic blood pressure and rates of severe hypoglycaemia) were risk-adjusted for non-care related patient factors using multivariate stepwise linear and logistic regression models. Unadjusted and risk-adjusted funnel plots were constructed for each outcome to identify low-performing and high-performing outliers. Unadjusted funnel plots identified 27 low-performing outliers and 15 high-performing outliers across all diabetes health outcomes. After risk-adjustment, 22 (81%) low-performing outliers and 13 (87%) high-performing outliers became inliers. Additionally, one inlier became a low-performing outlier. Risk-adjustment of diabetes health outcomes significantly reduced false positives and false negatives for outlier performance, hence providing more accurate information to guide quality improvement activity.