A Non-Acceptable Claim (NAC) is an insurance claim for an unnecessary hospital stay. This study establishes a statistical model which 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 non-acceptable. The data obtained from each claim record include: primary diagnosis code, age, sex, length of stay, admission type, and type of service. A full Bayesian hierarchical logistic regression model is used with regression coefficients that are random across the primary diagnosis codes. The NAC prediction model assumes that the logits are linear functions of the data with normally distributed errors. The model is first estimated on one data set and then validated with data collected at a later time. Using the model data, the posterior distributions of the parameters are estimated by the Gibbs sampler. A novel use of the Hastings-Metropolis algorithm is used to obtain the posterior distribution of the logits. A set of estimated regression coefficients is derived for each primary diagnosis code for the model data. The signs and magnitudes of the estimated parameters are consistent with initial notions of non-acceptable utilization. For example, the probability that a claim is NAC decreases with an increase in the length of the hospital stay, while the probability increases if the type of service is medical. NACs are predicted by the posterior probabilities of a claim being a NAC. The hierarchical Bayesian model provides better fits and predictions than standard methods which pool across primary diagnosis codes. These predictions allow results to be summarized by hospital, insured group, or other aggregate forms. The integration of the model into an insurer's cost containment program is simple and inexpensive to maintain.Ph.D.Health and Environmental SciencesHealth care managementPure SciencesStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/129305/2/9423302.pd
