Forecasting hourly emergency department arrival using time series analysis

Abstract

Background/aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.

Forecasting: theory and practice

“…One of the aspects of the non-clinical forecasting that has received the most attention in both research and application is the policy and management. Demand forecasting is regularly used in Emergency Departments (Arora et al, 2020;Choudhury and Urena, 2020;Khaldi et al, 2019;Rostami-Tabar and Ziel, 2020), ambulance services (Al-Azzani et al, 2020;Setzler et al, 2009;Vile et al, 2012;Zhou and Matteson, 2016) and hospitals with several different specialities (McCoy et al, 2018;Ordu et al, 2019;Zhou et al, 2018) to inform operational, tactical and strategic planning. The common methods used for this purpose include classical ARIMA and exponential smoothing methods, regression, singular spectrum analysis, Prophet, Double-Seasonal Holt-Winter, TBATS and Neural Networks.…”

Metaheuristic Approaches Integrated with ANN in Forecasting Daily Emergency Department Visits

“…Most of the studies focus on the daily basis of ED visits. Some studies tackle the hourly [6], weekly [14,23], and monthly trend of ED visits [15,23]. e datagathering period varied from one study to another.…”

“…ED patient visit forecasting, also called ED patient volume forecasting or ED patient admission forecasting, is the problem of forecasting the future patient arrival of an ED. For that purpose, the historical data demonstrated as a time series are gathered in a regular time frame of hourly [6,7], daily [8][9][10][11][12][13], weekly [14], monthly [15] and yearly [16] basis. Based on the literature, the vast majority of studies focus daily [5,17].…”

Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation

“…The correlation between the internet search index and patient arrivals was verified by Pearson correlation coefficient, Johansen cointegration, and Granger causality analysis. We then applied 8 forecasting models to predict ED patient arrivals, including ELM, generalized linear model (GLM), autoregressive integrated moving average model (ARIMA), ARIMA with explanatory variables (ARIMAX), support vector machine (SVM), artificial neural network (ANN), random forest (RF), and long short-term memory (LSTM) [24][25][26][27][28][29][30][31][32][33]. After that, their performances were evaluated in terms of accuracy and robustness analysis.…”