Forecasting hourly emergency department arrival using time series analysis
收藏DataONE2020-03-11 更新2025-06-21 收录
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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), HoltâWinters, 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 ...
创建时间：
2025-06-17
