Objective: To evaluate an automatic forecasting algorithm in order to predict the number of monthly emergency department (ED) visits one year ahead.
Methods: We collected retrospective data of the number of monthly visiting patients for a 6-year period (2005-2011) from 4 Belgian Hospitals. We used an automated exponential smoothing approach to predict monthly visits during the year 2011 based on the first 5 years of the dataset. Several in- and post-sample forecasting accuracy measures were calculated.
Results: The automatic forecasting algorithm was able to predict monthly visits with a mean absolute percentage error ranging from 2.64% to 4.8%, indicating an accurate prediction. The mean absolute scaled error ranged from 0.53 to 0.68 indicating that, on average, the forecast was better compared with in-sample one-step forecast from the naïve method.
Conclusion: The applied automated exponential smoothing approach provided useful predictions of the number of monthly visits a year in advance.
Keywords: Emergency nursing; Emergency service; Forecasting; Hospital; Management; Organisation and administration; Time-Series analysis.
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