IEEE J Biomed Health Inform. 2017 Mar;21(2):515-526. doi: 10.1109/JBHI.2015.2511820. Epub 2015 Dec 23.
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.
对于那些根据预期可用床位容量和紧急需求提前做出关于择期入院可接受率决策的医院，准确预测住院床位容量对于床位预留目的尤为有用。如前所述，通过检查次日出院患者数量的预测，可以得出每天结束时剩余的未占用床位，进而获得次日的床位容量。每日出院人数波动的特征，如趋势、季节周期、特殊日效应和自相关性，使决策优化变得复杂，而时间序列模型能够很好地捕捉这些特征。本研究比较了三种模型：一种结合季节性回归和自回归积分滑动平均模型（ARIMA）的模型、一种乘积季节性自回归积分滑动平均模型（MSARIMA）以及一种基于MSARIMA和加权马尔可夫链模型的组合模型，用于生成每日出院人数的预测。这些模型应用于一家完整医院的三年出院数据。利用诸如对称值方向、归一化均方误差和平均绝对百分比误差等多种性能指标来捕捉模型选择中的预测不足和预测过度情况。研究结果表明，使用所提出的模型可以预测每日出院人数。还讨论了一些重要的实际意义，例如在出院计划、入院安排和床位预留中使用准确的预测。
