title: Factors influencing patients' length of stay.
abstract: This study was conducted to evaluate the ability of AN-DRG version 3.1 to predict variation in patients' length of stay in hospital (LOS) and identify other factors that can influence the LOS by using routinely collected hospital morbidity data. A total of 18 DRGs that comprised 4,589 episodes were analysed. Multiple regression was used to model length of stay as a function of a number of independent variables. Overall only 37.6% of variation in mean length of stay could be explained. DRGs predicted 30% of the total variation. Other factors such as age, payment classification, source of referral, specialty of doctor, and ethnic group also influenced patient length of stay. It was concluded that the limited explanation was a consequence of a lack of a better indicator of severity within DRGs.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Curtin University of Technology, Australia.
authors.forename: Y
authors.initials: Y
authors.lastname: Liu
authors.forename: M
authors.initials: M
authors.lastname: Phillips
authors.forename: J
authors.initials: J
authors.lastname: Codde
mesh_terms: D003953:Diagnosis-Related Groups / Q000145:classification* / Q000706:statistics & numerical data; D017050:Episode of Care; D005163:Factor Analysis, Statistical; D006302:Health Services Research / Q000379:methods; D006784:Hospitals, Teaching / Q000706:statistics & numerical data*; D006786:Hospitals, Urban / Q000706:statistics & numerical data*; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D008511:Medicine / Q000145:classification / Q000706:statistics & numerical data; D012044:Regression Analysis; D013038:Specialization; D014600:Utilization Review / Q000379:methods* / Q000706:statistics & numerical data; D014904:Western Australia