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

Int Emerg Nurs. 2014 Apr;22(2):112-5. doi: 10.1016/j.ienj.2013.08.001. Epub 2013 Aug 24.

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.

Keywords: Emergency nursing; Emergency service; Forecasting; Hospital; Management; Organisation and administration; Time-Series analysis.

Publication types

  • Multicenter Study

MeSH terms

  • Algorithms*
  • Belgium
  • Emergency Service, Hospital / statistics & numerical data*
  • Forecasting
  • Humans
  • Interrupted Time Series Analysis
  • Predictive Value of Tests
  • Retrospective Studies