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• W2079006507 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." @default.
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• W2079006507 date "2002-06-01" @default.
• W2079006507 modified "2023-10-06" @default.
• W2079006507 title "A zero-inflated Poisson mixed model to analyze diagnosis related groups with majority of same-day hospital stays" @default.
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• W2079006507 doi "https://doi.org/10.1016/s0169-2607(01)00171-7" @default.
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