Eijkenaar Frank, van Vliet René C J A
Institute of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, the Netherlands.
Med Decis Making. 2014 Feb;34(2):192-205. doi: 10.1177/0272989X13498825. Epub 2013 Aug 6.
Profiling is increasingly being used to generate input for improvement efforts in health care. For these efforts to be successful, profiles must reflect true provider performance, requiring an appropriate statistical model. Sophisticated models are available to account for the specific features of performance data, but they may be difficult to use and explain to providers.
To assess the influence of the statistical model on the performance profiles of primary care providers. Data Source. Administrative data (2006–2008) on 2.8 million members of a Dutch health insurer who were registered with 1 of 4396 general practitioners.
Profiles are constructed for 6 quality measures and 5 resource use measures, controlling for differences in case mix. Models include ordinary least squares, generalized linear models, and multilevel models. Separately for each model, providers are ranked on z scores and classified as outlier if belonging to the 10% with the worst or best performance. The impact of the model is evaluated using the weighted kappa for rankings overall, percentage agreement on outlier designation, and changes in rankings over time.
Agreement among models was relatively high overall (kappa typically .0.85). Agreement on outlier designation was more variable and often below 80%, especially for high outliers. Rankings were more similar for processes than for outcomes and expenses. Agreement among annual rankings per model was low for all models.
Differences among models were relatively small, but the choice of statistical model did affect the rankings. In addition, most measures appear to be driven largely by chance, regardless of the model that is used. Profilers should pay careful attention to the choice of both the statistical model and the performance measures.
概况分析越来越多地被用于为医疗保健改进工作提供输入信息。为使这些工作取得成功，概况必须反映真实的医疗服务提供者表现，这就需要一个合适的统计模型。有复杂的模型可用于考虑绩效数据的特定特征，但它们可能难以使用且难以向医疗服务提供者解释。
评估统计模型对初级医疗服务提供者绩效概况的影响。数据来源。来自一家荷兰健康保险公司280万成员的管理数据（2006 - 2008年），这些成员在4396名全科医生中的一位处登记。
针对6项质量指标和5项资源使用指标构建概况，控制病例组合差异。模型包括普通最小二乘法、广义线性模型和多层次模型。对于每个模型，分别根据z分数对医疗服务提供者进行排名，如果属于表现最差或最佳的10%则被归类为异常值。使用总体排名的加权kappa、异常值指定的百分比一致性以及随时间的排名变化来评估模型的影响。
模型之间的总体一致性相对较高（kappa通常为0.85）。异常值指定的一致性变化更大，且通常低于80%，尤其是对于高异常值。过程方面的排名比结果和费用方面的排名更相似。每个模型的年度排名之间的一致性对所有模型来说都很低。
模型之间的差异相对较小，但统计模型的选择确实会影响排名。此外，无论使用何种模型，大多数指标似乎很大程度上是由随机因素驱动的。概况分析者应仔细关注统计模型和绩效指标的选择。
