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Anticipating special events in Emergency Department forecasting

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  • Rostami-Tabar, Bahman
  • Ziel, Florian

Abstract

Accurate daily forecast of Emergency Department (ED) attendance helps roster planners in allocating available resources more effectively and potentially influences staffing. Since special events affect human behaviours, they may increase or decrease the demand for ED services. Therefore, it is crucial to model their impact and use them to forecast future attendance to improve roster planning and avoid reactive strategies. In this paper, we propose, for the first time, a forecasting model to generate both point and probabilistic daily forecast of ED attendance. We model the impact of special events on ED attendance by considering real-life ED data. We benchmark the accuracy of our model against three time-series techniques and a regression model that does not consider special events. We show that the proposed model outperforms its benchmarks across all horizons for both point and probabilistic forecasts. Results also show that our model is more robust with an increasing forecasting horizon. Moreover, we provide evidence on how different types of special events may increase or decrease ED attendance. Our model can easily be adapted for use not only by EDs but also by other health services. It could also be generalised to include more types of special events.

Suggested Citation

  • Rostami-Tabar, Bahman & Ziel, Florian, 2022. "Anticipating special events in Emergency Department forecasting," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1197-1213.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:3:p:1197-1213
    DOI: 10.1016/j.ijforecast.2020.01.001
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    References listed on IDEAS

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    1. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 9(8), pages 1-22, August.
    2. Ziel, Florian & Liu, Bidong, 2016. "Lasso estimation for GEFCom2014 probabilistic electric load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1029-1037.
    3. Ziel, Florian, 2019. "Quantile regression for the qualifying match of GEFCom2017 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1400-1408.
    4. Boylan, John E. & Goodwin, Paul & Mohammadipour, Maryam & Syntetos, Aris A., 2015. "Reproducibility in forecasting research," International Journal of Forecasting, Elsevier, vol. 31(1), pages 79-90.
    5. Ziel, Florian, 2016. "Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR–ARCH type processes," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 773-793.
    6. Kuang Xu & Carri W. Chan, 2016. "Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 314-331, July.
    7. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    8. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    9. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    10. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    11. Izady, Navid & Worthington, Dave, 2012. "Setting staffing requirements for time dependent queueing networks: The case of accident and emergency departments," European Journal of Operational Research, Elsevier, vol. 219(3), pages 531-540.
    12. Cheang, B. & Li, H. & Lim, A. & Rodrigues, B., 2003. "Nurse rostering problems--a bibliographic survey," European Journal of Operational Research, Elsevier, vol. 151(3), pages 447-460, December.
    13. Haben, Stephen & Giasemidis, Georgios & Ziel, Florian & Arora, Siddharth, 2019. "Short term load forecasting and the effect of temperature at the low voltage level," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1469-1484.
    14. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    15. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    16. Franses, Philip Hans, 2016. "A note on the Mean Absolute Scaled Error," International Journal of Forecasting, Elsevier, vol. 32(1), pages 20-22.
    17. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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    Cited by:

    1. Rostami-Tabar, Bahman & Ali, Mohammad M. & Hong, Tao & Hyndman, Rob J. & Porter, Michael D. & Syntetos, Aris, 2022. "Forecasting for social good," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1245-1257.
    2. Abreu, Paulo & Santos, Daniel & Barbosa-Povoa, Ana, 2023. "Data-driven forecasting for operational planning of emergency medical services," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).

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