Abstract
Predicting patient waiting times in public emergency department rooms (EDs) has relied on inaccurate rolling average or median estimators. This inefficiency negatively affects EDs resources and staff management and causes patient dissatisfaction and adverse outcomes. This paper proposes a data science-oriented method to analyze real retrospective data. Using different error metrics, we applied various Machine Learning (ML) and Deep learning (DL) techniques to predict patient waiting times, including RF, Lasso, Huber regressor, SVR, and DNN. We examined data on 88,166 patients’ arrivals at the ED of the Intercommunal Hospital Center of Castres-Mazamet (CHIC). The results show that the DNN algorithm has the best predictive capability among other models. By precise and real-time prediction of patient waiting times, EDs can optimize their activities and improve the quality of services offered to patients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Amor, L.B., Lahyani, I., Jmaiel, M.: Recursive and rolling windows for medical time series forecasting: a comparative study. In: 2016 IEEE Intl Conference on Computational Science and Engineering, CSE 2016, and IEEE Intl Conference on Embedded and Ubiquitous Computing, EUC 2016, and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering, DCABES 2016, Paris, France, 24–26 August 2016, pp. 106–113. IEEE Computer Society (2016). https://doi.org/10.1109/CSE-EUC-DCABES.2016.169
Ataman, M.G., Sarıyer, G.: Predicting waiting and treatment times in emergency departments using ordinal logistic regression models. Am. J. Emerg. Med. 46, 45–50 (2021)
Benevento, E., Aloini, D., Squicciarini, N.: Towards a real-time prediction of waiting times in emergency departments: a comparative analysis of machine learning techniques. Int. J. Forecast. (2021)
Hemaya, S.A., Locker, T.E.: How accurate are predicted waiting times, determined upon a patient’s arrival in the emergency department? Emerg. Med. J. 29(4), 316–318 (2012)
Hijry, H., Olawoyin, R.: Predicting patient waiting time in the queue system using deep learning algorithms in the emergency room. Int. J. Ind. Eng. 3(1) (2021)
Pak, A., Gannon, B., Staib, A., et al.: Forecasting waiting time to treatment for emergency department patients. OSF Preprints 26 (2020)
Patil, P., Thakur, S.D., Kasap, N.: Patient waiting time prediction in hospital queuing system using improved random forest in big data. In: 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) (2019)
Soremekun, O.A., Takayesu, J.K., Bohan, S.J.: Framework for analyzing wait times and other factors that impact patient satisfaction in the emergency department. J. Emerg. Med. 41(6), 686–692 (2011)
Tomar, D., Agarwal, S.: A survey on data mining approaches for healthcare. Int. J. Bio-Sci. Bio-Technol. 5(5), 241–266 (2013)
Ward, P.R., et al.: ‘waiting for’ and ‘waiting in’ public and private hospitals: a qualitative study of patient trust in south Australia. BMC Health Serv. Res. 17(1), 1–11 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ameur, N.B., Lahyani, I., Thabet, R., Megdiche, I., Steinbach, Jc., Lamine, E. (2022). Predicting Patient’s Waiting Times in Emergency Department: A Retrospective Study in the CHIC Hospital Since 2019. In: Fournier-Viger, P., et al. Advances in Model and Data Engineering in the Digitalization Era. MEDI 2022. Communications in Computer and Information Science, vol 1751. Springer, Cham. https://doi.org/10.1007/978-3-031-23119-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-23119-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23118-6
Online ISBN: 978-3-031-23119-3
eBook Packages: Computer ScienceComputer Science (R0)