Toward Healthcare Improvements Using Data Envelopment Analysis: The Case of Emergency Medical Services

The healthcare system is no stranger to resource challenges in the face of unlimited demand to fulfill healthcare objectives of satisfying patients, maintaining service quality, and maximizing profit. An emergency medical services (EMSs) system plays a crucial role in stabilizing and transporting seriously injured patients to hospitals within healthcare systems. The EMS function is influenced by several criteria, such as call rate, traffic condition, setup, and operating costs. Therefore, the optimal design of EMS systems, including determining the location of emergency medical bases and allocating ambulances, helps improve service performance. This chapter explains the methodology and empirical results of a mathematical modeling and simulation-based optimization approach aimed at identifying the optimal location of emergency medical centers and assigning ambulances to the selected centers to maximize survival rates and minimize the total cost of the EMS system. A case study of the city of Isfahan in Iran is presented to demonstrate the applicability and efficacy of the proposed approach. The simulation-based optimization model was implemented in four selected municipal regions of Isfahan to obtain an appropriate design for emergency center locations and ambulances allocation with three types of patients (classified by the urgency of help required) and two types of ambulances. Six scenarios were defined to simulate the model in a dynamic environment and measure the survival rate and total cost of each scenario. In view of the survival rate and costs, data envelopment analysis (DEA) was then used to rank scenarios and select the best ones. The patient type was found to have a significant effect on the DEA rankings of the different input scenarios. An analysis across scenarios showed that adding portable stations in the regions that have the highest percentage of urgent patient calls can help increase the survival rate at a lower cost.