A H Lee1, K K Yau. 1. School of Public Health, Curtin University of Technology, Perth, Australia. Andy.Lee@curtin.edu.au
Abstract
OBJECTIVES: To identify factors associated with hospital length of stay (LOS) and to model variations in LOS within Diagnosis Related Groups (DRGs). METHODS: A proportional hazards frailty modelling approach is proposed that accounts for patient transfers and the inherent correlation of patients clustered within hospitals. The investigation is based on patient discharge data extracted for a group of obstetrical DRGs. RESULTS: Application of the frailty approach has highlighted several significant factors after adjustment for patient casemix and random hospital effects. In particular, patients admitted for childbirth with private medical insurance coverage have higher risk of prolonged hospitalization compared to public patients. CONCLUSIONS: The determination of pertinent factors provides important information to hospital management and clinicians in assessing the risk of prolonged hospitalization. The analysis also enables the comparison of inter-hospital variations across adjacent DRGs.
OBJECTIVES: To identify factors associated with hospital length of stay (LOS) and to model variations in LOS within Diagnosis Related Groups (DRGs). METHODS: A proportional hazards frailty modelling approach is proposed that accounts for patient transfers and the inherent correlation of patients clustered within hospitals. The investigation is based on patient discharge data extracted for a group of obstetrical DRGs. RESULTS: Application of the frailty approach has highlighted several significant factors after adjustment for patient casemix and random hospital effects. In particular, patients admitted for childbirth with private medical insurance coverage have higher risk of prolonged hospitalization compared to public patients. CONCLUSIONS: The determination of pertinent factors provides important information to hospital management and clinicians in assessing the risk of prolonged hospitalization. The analysis also enables the comparison of inter-hospital variations across adjacent DRGs.