Forecasting Emergency Department Crowding using Data Science Techniques

José Manuel Domenech Cabrera, Javier Lorenzo-Navarro

2022

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 emergency department as well as performance indicators from other areas of the hospital extracted from the information system.

Paper Citation

in Harvard Style

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) - Volume 5: HEALTHINF; ISBN 978-989-758-552-4, SciTePress, pages 504-513. DOI: 10.5220/0010840700003123

in Bibtex Style

@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) - Volume 5: HEALTHINF},
year={2022},
pages={504-513},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010840700003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF
TI - Forecasting Emergency Department Crowding using Data Science Techniques
SN - 978-989-758-552-4
AU - Domenech Cabrera J.
AU - Lorenzo-Navarro J.
PY - 2022
SP - 504
EP - 513
DO - 10.5220/0010840700003123
PB - SciTePress