Performance Analysis in Healthcare Sector Using Stochastic Data Envelopment Analysis with Undesirable Outputs

Data envelopment analysis (DEA) has been widely used to assess the technical performances of decision-making units with inputs and outputs. However, there are few research studies on performance assessment in service-oriented (without inputs) systems in the presence of contextual variables. In the theoretical part of the paper, we develop a without-input-DEA model with contextual variables for situations where we face variability and uncertainty in the data. In the application part, the work process in healthcare sector is considered as a service-oriented process with contextual variables, desirable and undesirable outputs. A two-step procedure consisting of the use of the proposed model along with a regression analysis is developed to evaluate performance in the healthcare sector and the impact of contextual variables on efficiency. The results revealed that 27% of the hospitals studied were classified as inefficient. We also found that hospitals are inefficient in terms of mortality rate and infectious waste. Regarding the Number of inpatients, on average, the performance of the hospitals was acceptable. The coefficient was 0.0046, indicating a direct impact on efficiency.