Profiling provider outcome quality for pay-for-performance in the presence of missing data: a simulation approach.
Profiling provider outcome quality for pay-for-performance in the presence of missing data: a simulation approach.
复制标题
在存在缺失数据的情况下分析提供商的结果质量以按绩效付费：一种模拟方法。
DOI：
10.1111/1475-6773.12038
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发表时间：
2013
影响因子：
3.4
通讯作者：
Bao,Yuhua
中科院分区：
文献类型：
--
作者：
Ryan,AndrewM;Bao,Yuhua
ObjectiveProvider profiling of outcome performance has become increasingly common in pay‐for‐performance programs. For chronic conditions, a substantial proportion of patients eligible for outcome measures may be lost to follow‐up, potentially compromising outcome profiling. In the context of primary care depression treatment, we assess the implications of missing data for the accuracy of alternative approaches to provider outcome profiling.DataWe used data from the Improving Mood‐Promoting Access to Collaborative Treatment trial and the Depression Improvement across Minnesota, Offering a New Direction initiative to generate parameters for a Monte Carlo simulation experiment.Study DesignThe patient outcome of interest is the rate of remission of depressive symptoms at 6 months among a panel of patients with major depression at baseline. We considered two alternative approaches to profiling this outcome: (1) a relative, or tournament style threshold, set at the 80th percentile of remission rate among all providers, and (2) an absolute threshold, evaluating whether providers exceed a specified remission rate (30 percent). We performed a Monte Carlo simulation experiment to evaluate the total error rate (proportion of providers who were incorrectly classified) under each profiling approach. The total error rate was partitioned into error from random sampling variability and error resulting from missing data. We then evaluated the accuracy of alternative profiling approaches under different assumptions about the relationship between missing data and depression remission.Principal FindingsOver a range of scenarios, relative profiling approaches had total error rates that were approximately 20 percent lower than absolute profiling approaches, and error due to missing data was approximately 50 percent lower for relative profiling. Most of the profiling error in the simulations was a result of random sampling variability, not missing data: between 11 and 21 percent of total error was attributable to missing data for relative profiling, while between 16 and 33 percent of total error was attributable to missing data for absolute profiling. Finally, compared with relative profiling, absolute profiling was much more sensitive to missing data that was correlated with the remission outcome.ConclusionsRelative profiling approaches for pay‐for‐performance were more accurate and more robust to missing data than absolute profiling approaches.
