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Identifying Patient Demand New Patterns in Emergency Departments a Multiple Case Study: A Forecasting Approach

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Industrial Engineering and Operations Management (IJCIEOM 2020)

Abstract

Patient demand arrival prediction is a critical problem to emergencies departments (EDs) that must delivery timely and adequate treatment to meet patient needs. High accuracy on patient demand forecasting allows ED managers to better size and allocate health care professionals. Besides patients can arrive any time expecting for quickly medical assistance, ED managers must provide efficient resource planning in order to fulfill that expectance limited by balance financial budgets. In this paper, the problem of ED patient arrival forecast is proposed as a planning tool allowing ED managers to better prepare short- and long-term staffing policies for the coming demand variations. We apply statistical time series techniques on four EDs historical data to catch patient demand pattern arrival behavior hourly, weekly and yearly all over the time and, thereafter, we forecast them one year ahead. The hourly forecasted patient demand pointed out the grown of pediatrician service while physician service decreases over the time. In addition, forecasted results shows that health care professionals which work on night shifts will find more variation in patient demand than professionals which work on morning shifts.

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Correspondence to Daniel Bouzon Nagem Assad .

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Assad, D.B.N., Cara, J., Ortega-Mier, M. (2020). Identifying Patient Demand New Patterns in Emergency Departments a Multiple Case Study: A Forecasting Approach. In: Thomé, A.M.T., Barbastefano, R.G., Scavarda, L.F., dos Reis, J.C.G., Amorim, M.P.C. (eds) Industrial Engineering and Operations Management. IJCIEOM 2020. Springer Proceedings in Mathematics & Statistics, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-030-56920-4_14

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