A Hierarchical Bayesian Model for Predicting the Rate of Nonacceptable In-Patient Hospital Utilization
建立了一个层次贝叶斯逻辑回归模型，利用医院出院索赔记录预测非必要住院索赔的概率，为保险公司审计提供补充工具。
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.