An evaluation of the effect of ambulance strategy on patient mortality outcome from hospital systems: A simulation study in the context of industrial disaster response

Abstract

The management of emergencies entails efficient coordination and execution of human and material resources to lessen the effects of a disaster while simultaneously protecting both life and property, including critical infrastructure. The objective of this study is to evaluate the effectiveness and impact of ambulance dispatch strategies in a disaster response scenario. The researchers focused on a case where a mobile field hospital (MFH) was deployed to assist a regional hospital in the aftermath of an explosion in the oil and gas industry. The strategy employed by the researchers includes utilization of the agent-based modeling and simulation (ABMS) tool (NetLogo 6.3.0). This approach involves the representation of agents within a system governed by simple rules and objectives and their interactions with each other and their environment. Furthermore, the model framework was sampled and analyzed using the NetLogo BehaviorSpace, and appropriate Python libraries were then utilized to plot and perform sensitivity analysis (SA) on the dataset (Pandas, SAlib, NumPy, Matplotlib). The assessment revealed the value and promise of employing a combination of ABMS and SA techniques for the evaluation and comprehension of the impact of emergency dispatch strategies in disaster response in the oil and gas industry. The study emphasized the significance of the assessment of emergency dispatch strategies in dedicated industries. The methodologies adopted revealed the need for effective resource management, recognizing protocols for emergency scenarios while lessening deaths and simultaneously improving overall response time. The researchers propose further study and fine-tuning of their model to improve the simulation accuracy and optimize the results based on the availability of local data. There is strong evidence of substantial value in the utilization of NetLogo (ABMS) and the use of Python libraries as modeling, simulation, and evaluative instruments for emergency response in the oil and gas industries. Findings from this study established that deploying MFHs during disasters is advantageous in reducing mortality outcomes and underlined how essential policy assessment can be to refining disaster response strategies.