Where to place emergency ambulance vehicles? Using a capacitated maximum covering location model with real call data

Abstract

Background: Timeliness of emergency medical services (EMS) is critical for patient survival. Identifying optimal locations for ambulance vehicles could increase the chance of timely service delivery. This study incorporates Geographical Information Systems (GIS) with a mathematical optimization technique to improve the 5-minute coverage of EMS demands. Methods: This study was conducted in the county of Mashhad, the northeast of Iran, including 94 ambulance vehicles distributed across 74 EMS stations. Locations of demands were extracted using analysis of one-year EMS call data. Network analysis was employed to estimate the travel times. A maximal covering location problem (MCLP) model with a capacity threshold for vehicles was implemented using the CPLEX optimizer. To make the proposed model more practical in the context of EMS, we added a constraint to the model formulation to maintain an acceptable service level for all EMS calls. Two scenarios were implemented: (1) a relocation model of existing vehicles among existing stations and (2) an optimal allocation model of EMS vehicles and stations using a list of candidate locations.Results: Using the relocation model, the proportion of calls for service within the 5-minute coverage standard increased from 69% to 75%, ensuring all urban and rural service demands to be reached within 16 and 48 minutes, respectively. Our allocation model revealed that the coverage proportion could rise to 84% of the total call for service by adding ten vehicles and eight new stations.Conclusions: Incorporating GIS techniques with optimization modeling has the potential to improve population health outcomes in real-world decision making regarding the accessibility and equity of health resource allocation including EMS services.