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The problem considered in this research is the efficient allocation of resources in an emergency department during a large flow of patient consequent to a pandemic influenza breakout. Predicting the impact of a Pandemic Influenza is very complex due to the many unknown variables that may play a role to how severe a pandemic can be. Scenario planning is considered in this research to forecast different potential outcomes and help decision makers better understand the role of uncertainties and become prepared to make important decisions. The goal is to first create a forecast model to estimate the patient demand during the breakout period accessing an emergency department and employ it as input of a simulation model to replicate the dynamics of the system under a set of pandemic influenza scenarios. The results yielded by this approach will be used as decision tool for hospital managers to better utilize and allocate medical staff considering the fluctuant demand of the system on the zones of the emergency department: triage, red, yellow, green, and black. Emergency departments are already overwhelmed during everyday operations; thus, it is expected in a case of pandemic influenza, their operations will be challenged beyond their limits. Hospitals are the first responders in a case of pandemic influenza since they will admit and treat the first cases, also they will be the first to identify the new virus. It is critical for hospitals to plan and create strategies to more effectively face the large number of patients arriving, and the best use of the available resources. Once the simulation model has been run and verified, and optimization procedure will be put in place to minimize the number of patients waiting in queue to be treated while maximizing flow of patients. The model is built using ARENA simulation software and OptQuest heuristic optimization to propose various combinations for the number of nurses needed for healthcare delivery. The proposed method significantly improves system efficiency by reducing the number of patients waiting in queue for health treatment and care, and also increases the total number of patients treated. |
