Predicting Patient’s Waiting Times in Emergency Department: A Retrospective Study in the CHIC Hospital Since 2019 - Archive ouverte HAL Access content directly
Conference Papers Year : 2023

Predicting Patient’s Waiting Times in Emergency Department: A Retrospective Study in the CHIC Hospital Since 2019

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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.
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Dates and versions

hal-03935078 , version 1 (11-01-2023)

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Cite

Nadhem Ben Ameur, Imene Lahyani, Rafika Thabet, Imen Megdiche, Jean-Christophe Steinbach, et al.. Predicting Patient’s Waiting Times in Emergency Department: A Retrospective Study in the CHIC Hospital Since 2019. MEDI 2022 - International Conference on Model and Data Engineering, Nov 2022, Le Caire (Egypte), Egypt. pp.44-57, ⟨10.1007/978-3-031-23119-3_4⟩. ⟨hal-03935078⟩
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