Optimal allocation of emergency medical resources in mass casualty incidents

Abstract

In the aftermath of a mass casualty incident (MCI), demands for emergency medical services (EMS) often overwhelm emergency response capacity. To maximize lifesaving capacity, the effective use of emergency medical resources is important. This thesis addresses the resource allocation problems faced during the emergency response to an MCI, and develops methodologies for solving these problems through the application of optimization approaches. We begin by studying how best to assign treatment and evacuation priorities to victims in an MCI. We model the primary prioritization problem as an ambulance routing problem, with the goal of providing the greatest amount of good to the maximum number of patients. The problem is then solved by applying a column generation approach. Next we study ambulance relocations during MCIs. During the emergency response to an MCI, an EMS system may dispatch multiple numbers of ambulances to the scene, potentially leading to significant capacity loss for future EMS demands. To minimize this loss, we developed an ambulance relocation model. The model provides a template for relocating those ambulances which have not been dispatched to the MCI and are thus available to meet future EMS demands. Through such a relocation model we can thus improve coverage with the reduced number of ambulances. Lastly, we developed an algorithm to determine how many ambulances to send to a given MCI. The algorithm was designed to make the calculations necessary for making dispatch decisions while maximizing the quality of EMS operations for victims in an MCI and future demands. Note that the solutions to the problem of patient prioritization and ambulance relocations during an MCI are critical factors for the quality of EMS during the MCI. Therefore the algorithm developed for ambulance dispatching has been constructed by integrating both of the algorithms utilized in dealing with the first two ambulance allocation problems.