Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan

Abstract

ObjectiveEmergency 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.MethodsWe 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.ResultsA 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 Visitt=7111.161+(at+0.37462 at−1).ConclusionThe 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.

Forecasting hourly emergency department arrival using time series analysis

“…The present study focused on applying time series forecasting methods to address emergency department overcrowding issues. Time series forecasting methods have been used in various fields of healthcare, such as forecasting daily outpatient visits (Luo et al, 2017), inpatient admissions (Zhou et al, 2018), maternal mortality (Sarpong, 2013), emergency department visits (Juang et al, 2017), disease management (Sato, 2013;Song et al, 2016), healthcare waste generation (Chauhan and Singh, 2017) and hospital census data (Capan et al, 2016).…”

Cardiology Admissions from Catheterization Laboratory – Time Series Forecasting

“…Additionally, Naïve, seasonal Naïve, mean, exponential smoothing, drift, and Holt's methods were also implemented and compared with ARIMA. The fitted model with minimum Akaike information criterion (AIC) and Schwartz Bayesian criterion (BIC) was selected as the optimal forecasting model [10]. The AIC is an estimator of the relative quality of statistical models for a given set of data.…”

Hourly Forecasting of Emergency Department Arrivals – Time Series Analysis

“…The fitted model with minimum Akaike information criterion (AIC) and Schwartz Bayesian criterion (BIC) was selected as the optimal forecasting model. [14] The accuracy of the forecast was then measured based on the root mean square error and mean error. Figure 1 shows the original data decomposed into observed, trend, seasonal, and random data.…”