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.


CONCLUSIONS
The applied automated exponential smoothing approach provided useful predictions of the number of monthly visits a year in advance.
Research group Economics and Public Policy, Faculty of Business Economics, Hasselt University, Belgium. Electronic address: Jochen.bergs@uhasselt.be.
Jochen
J
Bergs
Emergency Department, Heilig Hart Ziekenhuis Roeselare, Belgium.
Philipe
P
Heerinckx
Emergency Department, University Hospitals Leuven, Belgium.
Sandra
S
Verelst
D000465:Algorithms; D001530:Belgium; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005544:Forecasting; D006801:Humans; D065186:Interrupted Time Series Analysis; D011237:Predictive Value of Tests; D012189:Retrospective Studies
Emergency nursing; Emergency service; Forecasting; Hospital; Management; Organisation and administration; Time-Series analysis