Assessment of efficiency levels of training and research hospitals in Turkey and the factors affecting their efficiencies

Abstract

ObjectivesThis study's aim was to compare the efficiency levels of training and research hospitals in Turkey during 2014–2017 and find the factors affecting their efficiency scores. MethodsTo achieve the above objective, input oriented and variable returns to scale (VRS), data envelopment analysis (DEA), and Tobit regression model were used. The number of beds (BD), the number of intensive care beds (IBD), and the number of specialist doctors (SD) were used as the input variables of DEA while the number of polyclinic admissions (PA), number of inpatients (InP), and number of A, B, and C type surgeries as the output variables. The Tobit regression model was created by using some control variables as independent variables and transformed DEA scores as dependent variables. Average length of stay (ALS), bed turnover rate (BTR), bed occupancy rate (BOR), surgery number per specialist doctor (SNSD), polyclinic admission number per specialist doctor (PANSD), and metropolitan city status (MCS) were used as control variables. ResultsStatistically significant control variables of BTR (p < 0.001), SNSD (p = 0.001), and MCS (p = 0.015) affect the transformed DEA score while ALS, BOR, and PANSD do not (p > 0.05). ConclusionsThe research revealed that efficiency increased as BTR and SNSD increased, and that MCS also had a positive effect on efficiency. Policy makers should therefore consider the province's MCS status while allocating resources to relevant educational research hospitals for improving their efficiency. Likewise, policy makers should pay more attention to increasing BTR and SNSD compared to other variables.