ICES, Toronto, ON, Canada (P.C.A., J.F., B.Y., M.K.K.).
Institute of Health Policy, Management and Evaluation (P.C.A., M.K.K.), University of Toronto, ON, Canada.
Circ Cardiovasc Qual Outcomes. 2020 Dec;13(12):e006968. doi: 10.1161/CIRCOUTCOMES.120.006968. Epub 2020 Nov 26.
Provider profiling involves comparing the performance of hospitals on indicators of quality of care. Typically, provider profiling examines the performance of hospitals on each quality indicator in isolation. Consequently, one cannot formally examine whether hospitals that have poor performance on one indicator also have poor performance on a second indicator.
We used Bayesian multivariate response random effects logistic regression model to simultaneously examine variation and covariation in multiple binary indicators across hospitals. We considered 7 binary patient-level indicators of quality of care for patients presenting to hospital with a diagnosis of acute stroke. We examined between-hospital variation in these 7 indicators across 86 hospitals in Ontario, Canada.
The number of patients eligible for each indicator ranged from 1321 to 14 079. There were 7 pairs of indicators for which there was a strong correlation between a hospital's performance on each of the 2 indicators. Twenty-nine of the 86 hospitals had a probability higher than 0.90 of having worse performance than average on at least 4 of the 7 indicators. Seven of the 86 of hospitals had a probability higher than 0.90 of having worse performance than average on at least 5 indicators. Fourteen of the 86 of hospitals had a probability higher than 0.50 of having worse performance than average on at least 6 indicators. No hospitals had a probability higher than 0.50 of having worse performance than average on all 7 indicators.
These findings suggest that there are a small number of hospitals that perform poorly on at least half of the quality indicators, and that certain indicators tend to cluster together. The described methods allow for targeting quality improvement initiatives at these hospitals.
提供者分析涉及比较医院在护理质量指标上的表现。通常，提供者分析单独检查每家医院在每个质量指标上的表现。因此，人们不能正式检查在一个指标上表现不佳的医院是否在第二个指标上也表现不佳。
我们使用贝叶斯多元响应随机效应逻辑回归模型同时检查医院之间多个二进制质量指标的变异和协变。我们考虑了 7 个患者水平的急性脑卒中患者护理质量的二进制指标。我们检查了加拿大安大略省 86 家医院的这 7 个指标的医院间变异。
符合每个指标的患者人数从 1321 到 14079 不等。有 7 对指标，医院在每个指标上的表现之间存在很强的相关性。29 家医院中至少有 4 家医院有超过 0.90 的概率表现不佳，至少有 7 家医院中有超过 0.90 的概率表现不佳，至少有 5 家医院中有超过 0.90 的概率表现不佳，至少有 6 家医院中有超过 0.50 的概率表现不佳。没有医院有超过 0.50 的概率在所有 7 个指标上表现不佳。
这些发现表明，有一小部分医院在至少一半的质量指标上表现不佳，并且某些指标往往集中在一起。所描述的方法允许针对这些医院的质量改进计划。
