Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis

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.publisher: Elsevier articletitle: Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis journaltitle: International Emergency Nursing articlelink: http://dx.doi.org/10.1016/j.ienj.2013.08.001 content_type: article copyright: Copyright © 2013 Elsevier Ltd. All rights reserved.status: publishe