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Service Location Optimization Model for Improving Rural Emergency Medical Services

Approximately 35,000 fatalities are attributed to accidents on U.S. highways each year and more than half of them occurred in rural areas. With such a high percentage of fatalities, rural areas are in critical need of timely and reliable Emergency Medical Services (EMS). EMS provide important prehospital care to victims before they are transferred to a hospital. After an accident occurs, the time it takes for victims to receive care from EMS is crucial to their survival. Compared with urban EMS, rural EMS face multiple challenges. One of them is how to properly site EMS stations to provide cost-effective services in rural areas. The goals of this paper include analyzing the spatial patterns of EMS station and incident locations, and optimizing rural EMS station locations. The data were collected from South Dakota, a rural state. This dataset was used to perform spatial analysis and to develop and evaluate an EMS location optimization model. The location optimization model aims to maximize the rural EMS coverage while taking service equity into consideration. The model was solved by a genetic algorithm toolbox in R. The proposed model provides an important and practical tool for rural EMS officials to select new EMS stations or relocate existing stations to improve service performance under budget and resource constraints.