Department of Statistics and Operations Research, University of North Carolina at Chapel Hill (UNC), NC, USA.
Department of Emergency Medicine, Clinical Informatics Fellowship Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Am J Emerg Med. 2021 Oct;48:177-182. doi: 10.1016/j.ajem.2021.04.075. Epub 2021 Apr 29.
To develop a novel predictive model for emergency department (ED) hourly occupancy using readily available data at time of prediction with a time series analysis methodology.
We performed a retrospective analysis of all ED visits from a large academic center during calendar year 2012 to predict ED hourly occupancy. Due to the time-of-day and day-of-week effects, a seasonal autoregressive integrated moving average with external regressor (SARIMAX) model was selected. For each hour of a day, a SARIMAX model was built to predict ED occupancy up to 4-h ahead. We compared the resulting model forecast accuracy and prediction intervals with previously studied time series forecasting methods.
The study population included 65,132 ED visits at a large academic medical center during the year 2012. All adult ED visits during the first 265 days were used as a training dataset, while the remaining ED visits comprised the testing dataset. A SARIMAX model performed best with external regressors of current ED occupancy, average department-wide ESI, and ED boarding total at predicting up to 4-h-ahead ED occupancy (Mean Square Error (MSE) of 16.20, and 64.47 for 1-hr- and 4-h- ahead occupancy, respectively). Our 24-SARIMAX model outperformed other popular time series forecasting techniques, including a 60% improvement in MSE over the commonly used rolling average method, while maintaining similar prediction intervals.
Accounting for current ED occupancy, average department-wide ESI, and boarding total, a 24-SARIMAX model was able to provide up to 4 h ahead predictions of ED occupancy with improved performance characteristics compared to other forecasting methods, including the rolling average. The prediction intervals generated by this method used data readily available in most EDs and suggest a promising new technique to forecast ED occupancy in real time.
利用预测时可获得的现有数据，采用时间序列分析方法，开发一种新的急诊科（ED）每小时占用率预测模型。
我们对 2012 年全年某大型学术中心的所有 ED 就诊进行回顾性分析，以预测 ED 每小时占用率。由于时间和工作日的影响，选择了季节性自回归综合移动平均带外回归（SARIMAX）模型。对于一天中的每一个小时，都建立了一个 SARIMAX 模型来预测未来 4 小时的 ED 占用率。我们将得到的模型预测精度和预测区间与之前研究的时间序列预测方法进行了比较。
本研究人群包括 2012 年某大型学术医疗中心的 65132 例 ED 就诊。所有成人 ED 在头 265 天的就诊被用作训练数据集，而其余 ED 就诊则构成了测试数据集。带有当前 ED 占用率、平均部门范围内 ESI 和 ED 住院总人数等外部回归器的 SARIMAX 模型在预测未来 4 小时的 ED 占用率方面表现最佳（1 小时和 4 小时提前占用的均方误差（MSE）分别为 16.20 和 64.47）。我们的 24-SARIMAX 模型优于其他流行的时间序列预测技术，包括与常用的滚动平均方法相比，MSE 提高了 60%，同时保持了相似的预测区间。
考虑到当前 ED 占用率、平均部门范围内 ESI 和住院总人数，24-SARIMAX 模型能够提供未来 4 小时的 ED 占用率预测，与其他预测方法（包括滚动平均法）相比，具有更好的性能特征。该方法生成的预测区间使用了大多数 ED 中易于获得的数据，为实时预测 ED 占用率提供了一种很有前途的新技术。
