Reformulating provider profiling by grouping providers treating similar patients prior to evaluating performance.
Reformulating provider profiling by grouping providers treating similar patients prior to evaluating performance.
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DOI：
10.1093/biostatistics/kxac019
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发表时间：
2022-06
期刊：
影响因子：
2.1
通讯作者：
Gabriella C. Silva;R. Gutman
中科院分区：
文献类型：
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作者：
Gabriella C. Silva;R. Gutman
比较医疗服务提供者表现的标准方法依赖于分层逻辑回归模型，该模型根据入院时的患者特征进行调整。当提供者治疗不同的患者群体并且模型指定错误时，这些模型的估计可能会产生误导。为了解决这一限制，我们提出了一种新颖的分析方法，该方法可以识别治疗相似患者群体的提供者组，然后评估每个组中提供者的表现。使用一般位置模型的贝叶斯多级有限混合来识别提供商组。为了将我们提出的分析方法与标准方法的性能进行比较，我们使用来自马萨诸塞州 119 个熟练护理机构的患者级别数据。我们使用模拟和观察到的结果数据来探索这些分析方法在不同设置中的性能。在模拟中，我们提出的方法将提供者分类为具有相似患者入院特征的组。此外，由于不同提供者之间的患者特征存在有限的重叠以及结果模型的错误指定，使用我们的方法获得的提供者水平估计更准确地识别出与基于标准回归的方法相比表现不佳和表现优异的提供者。
Standard approaches to comparing health providers' performance rely on hierarchical logistic regression models that adjust for patient characteristics at admission. Estimates from these models may be misleading when providers treat different patient populations and the models are misspecified. To address this limitation, we propose a novel profiling approach that identifies groups of providers treating similar populations of patients and then evaluates providers' performance within each group. The groups of providers are identified using a Bayesian multilevel finite mixture of general location models. To compare the performance of our proposed profiling approach to standard methods, we use patient-level data from 119 skilled nursing facilities in Massachusetts. We use simulated and observed outcome data to explore the performance of these profiling methods in different settings. In simulations, our proposed method classifies providers to groups with similar patients' admission characteristics. In addition, in the presence of limited overlap in patient characteristics across providers and misspecifications of the outcome model, the provider-level estimates obtained using our approach identified providers that under- and overperformed compared to the standard regression-based approaches more accurately.