A zero-inflated Poisson mixed model to analyze diagnosis related groups with majority of same-day hospital stays.
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.
Wang
K
K
School of Public Health, Curtin University of Technology, GPO Box U 1987, Perth WA 6845, Australia.
Yau
Kelvin K W
KK
Lee
Andy H
AH
D000465:Algorithms; D003198:Computer Simulation; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data*; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D012044:Regression Analysis; D013997:Time Factors