Davidson Gestur, Moscovice Ira, Remus Denise
University of Minnesota, Minneapolis, MN 55414, USA.
Health Care Financ Rev. 2007 Fall;29(1):45-57.
We construct statistical models to assess whether hospital size will impact the ability to identify "true" hospital ranks in pay-for-performance (P4P) programs. We use Bayesian hierarchical models to estimate the uncertainty associated with the ranking of hospitals by their raw composite score values for three medical conditions: acute myocardial infarction (AMI), heart failure (HF), and community acquired pneumonia (PN). The results indicate a dramatic inverse relationship between the size of the hospital and its expected range of ranking positions for its true or stabilized mean rank. The smallest hospitals among the augmented dataset would likely experience five to seven times more uncertainty concerning their true ranks.
我们构建统计模型，以评估医院规模是否会影响在按绩效付费（P4P）项目中识别“真正”医院排名的能力。我们使用贝叶斯分层模型来估计与医院排名相关的不确定性，这些排名是根据三种医疗状况（急性心肌梗死（AMI）、心力衰竭（HF）和社区获得性肺炎（PN））的原始综合评分值得出的。结果表明，医院规模与其真实或稳定平均排名的预期排名位置范围之间存在显著的反比关系。在扩充数据集中，规模最小的医院在其真实排名方面可能会面临多五到七倍的不确定性。
