Juang Wang-Chuan, Huang Sin-Jhih, Huang Fong-Dee, Cheng Pei-Wen, Wann Shue-Ren
Department of Emergency, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
Department of Information Management, Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan.
BMJ Open. 2017 Dec 1;7(11):e018628. doi: 10.1136/bmjopen-2017-018628.
Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits.
We retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses.
A series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visit=7111.161+(a+0.37462 a-1).
The ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes.
急诊科过度拥挤是全球范围内日益重要的问题。医院管理者越来越关注急诊科拥挤情况，以便为患者提供更高质量的医疗服务。良好管理策略的关键要素之一是需求预测。我们的研究旨在构建一个合适的模型并预测每月的急诊科就诊人数。
我们回顾性收集了2009年1月至2016年12月的每月急诊科就诊人数，以进行时间序列自回归积分滑动平均（ARIMA）分析。模型的初步开发基于2009年至2016年过去的急诊科就诊人数。进一步采用最佳拟合模型预测下一年（2016年）急诊科就诊人数的月度数据。最后，我们用平均绝对百分比误差（MAPE）评估所识别模型的预测准确性。所有统计分析均使用SAS/ETS V.9.4软件包和Office Excel 2016。
一系列统计检验表明，包括ARIMA(0, 0, 1)、ARIMA(1, 0, 0)、ARIMA(1, 0, 1)、ARIMA(2, 0, 1)、ARIMA(3, 0, 1)和ARIMA(5, 0, 1)在内的六个模型为候选模型。选择给出最小赤池信息准则和施瓦茨贝叶斯准则并符合残差独立性假设的模型作为合适模型。最后，确定并得到一个合适的ARIMA(0, 0, 1)结构，其MAPE为8.91%，即就诊人数 = 7111.161 + (a + 0.37462 a-1)。
ARIMA(0, 0, 1)模型可被认为适合预测未来的急诊科就诊人数，其预测结果可用于辅助决策过程。
