Emergency medical service resource allocation in a mass casualty incident by integrating patient prioritization and hospital selection problems

Mass casualty incidents often cause a shortage of resources for emergency medical services such as ambulances and emergency departments. These resources must be effectively managed to save as many lives as possible. Critical decisions in operating emergency medical service systems include the prioritization of patients for ambulance transport and the selection of destination hospitals. We develop a stochastic dynamic model that integrates patient transport prioritization and hospital selection problems. Policy solutions from the model are compared with other plausible heuristics, and our experimental results show that our policy solution outperforms other alternatives. More importantly, we show that there are considerable benefits from optimally selecting hospitals, which suggests that this decision is just as important as the patient prioritization decision. Motivated by the finding, we propose a heuristic policy that considers both patient prioritization and hospital selection. Experimental results demonstrate strong performance of our heuristic policy compared with existing heuristics. In addition, the proposed approach offers practical advantages. Whereas the existing heuristic policies use patient information, our heuristic policy requires information on the hospital state, which is more readily available and reliable.