title: Time-Series Approaches for Forecasting the Number of Hospital Daily Discharged Inpatients.
abstract: For hospitals where decisions regarding acceptable rates of elective admissions are made in advance based on expected available bed capacity and emergency requests, accurate predictions of inpatient bed capacity are especially useful for capacity reservation purposes. As given, the remaining unoccupied beds at the end of each day, bed capacity of the next day can be obtained by examining the forecasts of the number of discharged patients during the next day. The features of fluctuations in daily discharges like trend, seasonal cycles, special-day effects, and autocorrelation complicate decision optimizing, while time-series models can capture these features well. This research compares three models: a model combining seasonal regression and ARIMA, a multiplicative seasonal ARIMA (MSARIMA) model, and a combinatorial model based on MSARIMA and weighted Markov Chain models in generating forecasts of daily discharges. The models are applied to three years of discharge data of an entire hospital. Several performance measures like the direction of the symmetry value, normalized mean squared error, and mean absolute percentage error are utilized to capture the under- and overprediction in model selection. The findings indicate that daily discharges can be forecast by using the proposed models. A number of important practical implications are discussed, such as the use of accurate forecasts in discharge planning, admission scheduling, and capacity reservation.
authors.lastname: Ting Zhu
authors.lastname: Li Luo
authors.lastname: Xinli Zhang
authors.lastname: Yingkang Shi
authors.lastname: Wenwu Shen
mesh_terms: D001509:Bed Occupancy / Q000706:statistics & numerical data*; D005544:Forecasting / Q000379:methods*; D006742:Hospital Bed Capacity; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D010351:Patient Discharge / Q000706:statistics & numerical data*; D012044:Regression Analysis; D013997:Time Factors