Berrios-Montero Rolando A
Author Affiliation: Health System Strengthening, Chemonics International, Inc, Arlington, Virginia.
Health Care Manag (Frederick). 2019 Apr/Jun;38(2):116-123. doi: 10.1097/HCM.0000000000000262.
Hospital leaders encourage morning discharge of patients to boost patient flow. This work presents a detailed process of a building model for forecasting patient discharge before noon applying the Box-Jenkins methodology using weekly historic data. Accurately forecasting is of crucial importance to plan early discharge activities, influenced by the fluctuations in daily discharges process. The objective is to find an appropriate autoregressive integrated moving average (ARIMA) model for forecasting the rate of patients out by noon based on the lowest error in a statistical forecast by applying the mean absolute percentage error. The results obtained demonstrate that a nonseasonal ARIMA model classified as ARIMA(2,1,1) offers a good fit to actual discharge-before-noon data and proposes hospital leaders short-term prediction that could facilitate decision-making process, which is important in an uncertain health care system environment.
医院领导鼓励患者上午出院以促进患者流量。这项工作展示了一个使用每周历史数据应用Box-Jenkins方法预测中午前患者出院情况的构建模型的详细过程。准确预测对于规划早期出院活动至关重要，因为这受到每日出院过程波动的影响。目标是通过应用平均绝对百分比误差，基于统计预测中的最低误差找到一个合适的自回归积分移动平均（ARIMA）模型，用于预测中午前出院的患者比率。所获得的结果表明，分类为ARIMA(2,1,1)的非季节性ARIMA模型与实际上午出院数据拟合良好，并为医院领导提供了短期预测，这有助于决策过程，而在不确定的医疗保健系统环境中这一点很重要。
