An optimisation-based decision support model to dynamically coordinate the pre-hospital response of emergency services’ resources to multiple mass casualty inciden

Abstract

Effective coordinated responses to Mass Casualty Incidents (MCIs), which can occur suddenly and without notice, play a vital role in saving lives and reducing suffering. MCIs can result in a number of casualties with different levels of injury severity, requiring immediate lifesaving intervention. The complexity involved in responding to MCIs increases significantly due to the dynamic nature of such events as new information becomes available during the response. New information may include 1) an update on the number of casualties at an incident site, 2) identifying any casualties with deteriorating health requiring immediate lifesaving interventions, 3) the occurrence of a new incident site or sites as the response unfolds, resulting in additional casualties requiring lifesaving interventions, and/or 4) the response to an incident site or sites is completed, resulting in a number of emergency responders becoming available to be deployed to another incident site or sites. Due to the importance of effective coordinated responses to MCIs, this thesis develops a novel dynamic optimisation-based decision support model to coordinate the emergency services’ response to MCIs. The model comprises a pre-hospital response framework (PHRF) and an MCI environment, and a coordination and management interface that facilitates information exchanges between the environment and framework. The PHRF consists of optimisation-based algorithms, including a greedy heuristic algorithm, a genetic algorithm, and a neighbourhood search algorithm. The application of these algorithms results in the generation of a pre-determined attendance (PDA) response plan followed by an initial optimised post-PDA response plan, and then optimised post-PDA response plans based on new information that becomes available as the MCI response unfolds. Within the PHRF, an approach has been developed to manage the seamless transition from one optimised post-PDA response plan to another. Collectively, the aforementioned plans provide a continuous, coordinated response of the emergency services’ resources to be implemented in the MCI environment. The PHRF is coupled with an MCI environment that provides a realistic road network of the affected geographical area at which the actual key locations, including incident sites, ambulance stations and fire and rescue stations, and hospitals, are accurately identified. In addition, comprehensive v health profiles of casualties are modelled, which can be used dynamically to simulate casualties’ health, including their deterioration, during the response to MCIs. In relation to the application of the decision support model, two case study areas have been considered to simulate the coordinated emergency response to multiple MCIs. Central London represents the first case study area considered and was chosen due to it being a densely populated area, coupled with having a significant number of emergency resources and hospitals. Further, in recent times, it has been subjected to a number of MCI ‘terrorism’ events, including the 2005 London bombings. Birmingham city centre was selected for the second case study area due to being the UK’s second most populous city, and this area enables the consideration of the emergency response to a different city layout and locations of emergency services’ resources and hospitals. As a result of the model’s application, key findings are reported. Also, the results generated from the model are verified using grounding and calibration techniques. In addition, based on an evaluation of the performance of the model, its strengths and weaknesses are identified. Finally, areas of possible future work are recommended to improve the developed decision support model.