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Predicting hospital emergency department visits accurately: A systematic review

Author

Listed:
  • Eduardo Silva
  • Margarida F. Pereira
  • Joana T. Vieira
  • João Ferreira‐Coimbra
  • Mariana Henriques
  • Nuno F. Rodrigues

Abstract

Objectives The emergency department (ED) is a very important healthcare entrance point, known for its challenging organisation and management due to demand unpredictability. An accurate forecast system of ED visits is crucial to the implementation of better management strategies that optimise resources utilization, reduce costs and improve public confidence. The aim of this review is to investigate the different factors that affect the ED visits forecasting outcomes, in particular the predictive variables and type of models applied. Methods A systematic search was conducted in PubMed, Web of Science and Scopus. The review methodology followed the PRISMA statement guidelines. Results Seven studies were selected, all exploring predictive models to forecast ED daily visits for general care. MAPE and RMAE were used to measure models' accuracy. All models displayed good accuracy, with errors below 10%. Conclusions Model selection and accuracy was found to be particularly sensitive to the ED dimension. While ARIMA‐based and other linear models have good performance for short‐time forecast, some machine learning methods proved to be more stable when forecasting multiple horizons. The inclusion of exogenous variables was found to be advantageous only in bigger EDs.

Suggested Citation

  • Eduardo Silva & Margarida F. Pereira & Joana T. Vieira & João Ferreira‐Coimbra & Mariana Henriques & Nuno F. Rodrigues, 2023. "Predicting hospital emergency department visits accurately: A systematic review," International Journal of Health Planning and Management, Wiley Blackwell, vol. 38(4), pages 904-917, July.
    • Handle: RePEc:bla:ijhplm:v:38:y:2023:i:4:p:904-917
    DOI: 10.1002/hpm.3629
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    References listed on IDEAS

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    1. Harrou, Fouzi & Dairi, Abdelkader & Kadri, Farid & Sun, Ying, 2020. "Forecasting emergency department overcrowding: A deep learning framework," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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