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Authors: José Manuel Domenech Cabrera 1 and Javier Lorenzo-Navarro 2

Affiliations: 1 Insular Maternal-Infant University Hospital Complex of Gran Canaria, Avenida Marítima del Sur, s/n., Las Palmas de Gran Canaria, Spain ; 2 Inst. of Intelligent Systems and Num. Applic. in Engineering, Univ. of Las Palmas, Las Palmas de Gran Canaria, Spain

Keyword(s): Hospital Emergency Department (ED) Predictions, Emergency Department Overcrowding, Time Series Forecasting, Neural Networks.

Abstract: The provision of insufficient resources during periods of high demand can lead to overcrowding in emergency departments. This issue has been extensively addressed through time series forecasting and regression problems. Despite the fact the increasing number of studies, accurate forecasting of demand remains a challenge. Thus, the purpose of this study was to develop a tool to predict the future evolution of emergency department occupancy in order to anticipate overcrowding episodes, avoid their negative effects on health and improve efficiency. This article presents a novel approach under the premise that the ability of the system to drain patients is the most determining factor in overcrowding episodes as opposed to previous approaches focused on patient demand. The forecasts model were based on the hourly number of patients occupying the general Emergency Department of Insular University Hospital of Gran Canaria Island, mainly given data of the flow of patients through the emergen cy department as well as performance indicators from other areas of the hospital extracted from the information system. (More)

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Paper citation in several formats:
Domenech Cabrera, J. and Lorenzo-Navarro, J. (2022). Forecasting Emergency Department Crowding using Data Science Techniques. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF; ISBN 978-989-758-552-4; ISSN 2184-4305, SciTePress, pages 504-513. DOI: 10.5220/0010840700003123

@conference{healthinf22,
author={José Manuel {Domenech Cabrera} and Javier Lorenzo{-}Navarro},
title={Forecasting Emergency Department Crowding using Data Science Techniques},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF},
year={2022},
pages={504-513},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010840700003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF
TI - Forecasting Emergency Department Crowding using Data Science Techniques
SN - 978-989-758-552-4
IS - 2184-4305
AU - Domenech Cabrera, J.
AU - Lorenzo-Navarro, J.
PY - 2022
SP - 504
EP - 513
DO - 10.5220/0010840700003123
PB - SciTePress

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