While advancements in vehicular and wireless communication technologies are shaping the future of our transportation system, emergency medical services (EMS) are not receiving enough research attention. Their operations are still plagued by response delays that can often be life-threatening. Dispatching and redeployment systems identify the best practices regarding the allocation of the resources to emergencies and stations. Yet, the existing systems are unfortunately insufficient, and there is a growing need to embrace new technological solutions. This research introduces a smart system for EMS by leveraging the modular vehicle technology initially developed for transit systems. The proposed system relies on the design of vehicular modules that can couple and decouple to transfer patients from one module to another during transport. A fleet of medical transport vehicles is deployed to cooperate with the life support vehicles by providing, for example, transport and hospital admission tasks, thus allowing life support vehicles to answer pending emergency calls earlier. This is especially useful when there is a large demand for EMS (e.g. under the COVID-19 pandemic or other disasters such as the recent explosion in Beirut). This paper introduces a mathematical programming model to determine the optimal assignment decisions in a deterministic setting. This work is a proof of concept that demonstrates the applicability of the modular vehicle technology to EMS, evaluating the upper bound EMS performance that can be ultimately reached. A sensitivity analysis is conducted to provide insights and recommendations that are useful when selecting the weighting coefficients for the optimization function, to ensure a more efficient implementation of the modular vehicle technology for EMS. Also, the results of a comparative analysis show that the proposed system can adapt and offer larger benefits, in terms of response times and times to hospital, as demand increases and/or resources become more limited.
Keywords: Emergency medical services; Health care; Logistics; Modular systems; Optimization.
© 2022 The Authors.