Abstract (eng)
Due to an increasing number of mass casualty incidents, their high complexity and uniqueness, decision makers need Operations Research-based policy models for training emergency staff on planning and scheduling at the incident site. Niessner and Rauner developed a discrete event simulation policy model. By calculating realistic small, simple, urban to rather big, complex, and remote mass casualty emergency scenarios, this policy model helps to enhance the quality of planning and outcome. Furthermore, the organization of an advanced medical post can be improved in order to decrease fatalities as well as quickly treat and transport injured individuals to hospitals.
The purpose of this master thesis is to analyze the best strategies to manage staff of ambulance services to quickly evacuate an emergency site and to minimize the number of fatalities. Using a realistic predetermined disaster scenario, players act in the experiment during three runs as on site commanders to decide on sending staff to triage, to different treatment rooms for care and on-site transportation, as well as to transportation to hospitals.
We investigated to what extent players succeed in the game and improve over time. Furthermore, we examined differences in learning effects among player groups such as students and practitioners. We can disclose a significant increase in performance through practice / exercise from Run 1 to Run 2 (learning effects), existence of eagerness to experiment in last Run and difference among player groups’ results such as students and practitioners. Furthermore, we examined that best game results were not achieved by chance, but that good subjects always managed a good performance and that their self-assessment matched with their actual performance results. In addition we reveal female players were more challenged by the management game than male players.