A zero-inflated Poisson mixed model to analyze diagnosis related groups with majority of same-day hospital stays

Abstract

With increasing trend of same-day procedures and operations performed for hospital admissions, it is important to analyze those Diagnosis Related Groups (DRGs) consisting of mainly same-day separations. A zero-inflated Poisson (ZIP) mixed model is presented to identify health- and patient-related characteristics associated with length of stay (LOS) and to model variations in LOS within such DRGs. Random effects are introduced to account for inter-hospital variations and the dependence of clustered LOS observations via the generalized linear mixed models (GLMM) approach. Parameter estimation is achieved by maximizing an appropriate log-likelihood function using the EM algorithm to obtain approximate residual maximum likelihood (REML) estimates. An S-Plus macro is developed to provide a unified ZIP modeling approach. The determination of pertinent factors would benefit hospital administrators and clinicians to manage LOS and expenditures efficiently.