A Hierarchical Bayesian Model for Predicting the Rate of Nonacceptable In-Patient Hospital Utilization
A Hierarchical Bayesian Model for Predicting the Rate of Nonacceptable In-Patient Hospital Utilization
复制标题
用于预测不可接受的住院医院利用率的分层贝叶斯模型
DOI：
10.1080/07350015.1999.10524792
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
P. Lenk
中科院分区：
文献类型：
--
作者：
M. Rosenberg;R. W. Andrews;P. Lenk
不可接受索赔(NAC)是对不必要的住院时间的保险索赔。这项研究建立了一个预测NAC率的统计模型。该模式是对目前依赖于对患者医疗记录进行详细审计的保险公司计划的补充。出院索赔记录被用作统计模型中的输入，以回溯地预测入院不可接受的概率。使用完全贝叶斯分层Logistic回归模型，回归系数在主要诊断代码中是随机的。与跨主要诊断代码的标准方法相比，该模型提供了更好的拟合和预测。
A nonacceptable claim (NAC) is an insurance claim for an unnecessary hospital stay. This study establishes a statistical model that predicts the NAC rate. The model supplements current insurer programs that rely on detailed audits of patient medical records. Hospital discharge claim records are used as inputs in the statistical model to predict retrospectively the probability that a hospital admission is nonacceptable. A full Bayesian hierarchical logistic regression model is used with regression coefficients that are random across the primary diagnosis codes. The model provides better fits and predictions than standard methods that pool across primary diagnosis codes.