Risk-adjusted quality measures are used to evaluate healthcare providers while controlling for factors beyond their control. Existing healthcare provider profiling approaches typically assume that the risk adjustment is perfect and the between-provider variation in quality measures is entirely due to the quality of care. However, in practice, even with very good models for risk adjustment, some between-provider variation will be due to incomplete risk adjustment, which should be recognized in assessing and monitoring providers. Otherwise, conventional methods disproportionately identify larger providers as outliers, even though their provider effects need not be "extreme.'' Motivated by efforts to evaluate the quality of care provided by transplant centers, we develop a composite evaluation score based on a novel individualized empirical null method, which robustly accounts for overdispersion due to unobserved risk factors, models the marginal variance of standardized scores as a function of the effective center size, and only requires the use of publicly-available center-level statistics. The evaluations of United States kidney transplant centers based on the proposed composite score are substantially different from those based on conventional methods. Simulations show that the proposed empirical null approach more accurately classifies centers in terms of quality of care, compared to existing methods.
翻译:现有的保健提供者概况分析方法通常假定,风险调整是完美的,质量措施的提供者之间的差别完全是由于护理质量的缘故。然而,在实践中,即使风险调整模式非常良好,但有些提供者之间的差别是由于风险调整不完全,这在评估和监测提供者时应当得到承认。否则,传统方法不相称地将较大的提供者确定为外部提供者,尽管其提供者的影响不必是“极端的 ”,但受移植中心所提供护理质量评价努力的驱动,我们根据一种新的个体化的经验无效方法制定了综合评价评分,该方法有力地说明由于未观察到的风险因素造成的过度分散,将标准化分数的边际差异作为有效中心规模的函数,只需要使用公开可得的中心级别统计数据。美国肾移植中心根据拟议的复合得分进行的评价与根据传统方法进行的评价大不相同。模拟表明,拟议的实证无效方法在护理质量方面比现有方法更准确地分类中心。