Jochen Bergs1, Philipe Heerinckx2, Sandra Verelst3. 1. Research group Economics and Public Policy, Faculty of Business Economics, Hasselt University, Belgium. Electronic address: Jochen.bergs@uhasselt.be. 2. Emergency Department, Heilig Hart Ziekenhuis Roeselare, Belgium. 3. Emergency Department, University Hospitals Leuven, Belgium.
Abstract
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.
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.
Authors: Andreas Asheim; Lars P Bache-Wiig Bjørnsen; Lars E Næss-Pleym; Oddvar Uleberg; Jostein Dale; Sara M Nilsen Journal: BMC Emerg Med Date: 2019-08-05