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Using Computer Simulations to Support A Risk-Based Approach For Hospital Evacuation

Terrorist actions, such as the attacks on the London Underground and the Madrid train bombings, as well as fires, such as the destruction of the Station Night Club in Rhode Island, have focussed public attention on the evacuation of public buildings. Partly in consequence, there have been a number of recent legislative changes across Europe and the United States. This legislation encourages a risk-based approach to evacuation. Existing risk assessment techniques, including FMECA and fault trees, provide means of reasoning about potential fire hazards. They can also be extended to analyse the risks that occupants may not escape from a damaged building. However, it can be difficult to validate the findings from such analyses because group and individual behaviours have a profound impact on egress times. For instance, it is hard to assess the likelihood and consequence of the flocking behaviours that occur during mass evacuations. Live exercises address these limitations by providing direct insights into the behaviours of building occupants. However, these drills seldom recreate the conditions that hold during real emergencies, especially when occupants know that they are participating in an exercise. Ethical problems also restrict these drills. For example, patients’ health can be jeopardised if they are evacuated from centres of care in a hospital. It can also be difficult to hold drills that might disrupt the 24/7 activities of power distribution and financial service companies. This paper, therefore, describes the development of the Glasgow-Hospital Evacuation Simulator (G-HES). G-HES is an interactive, stochastic software system that can be used to simulate the evacuation of large public buildings. It supports a ‘risk-based’ approach to evacuation and can be calibrated using observations from ‘live’ evacuation exercises. Managers can use it to explore the consequences of different staffing levels and evacuation procedures. Monte Carlo techniques provide means of calculating mean and worst-case evacuation times under these different conditions. The evacuation of a local general hospital is used as a case study. This decision is justified by the difficulty of performing such evacuations and by the relatively high number of fires that occur in hospital buildings each year1.