UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Prediction of Waiting Times in A&E

Arias-Gómez, Luis F; Lovegrove, Thomas; Kunz, Holger; (2023) Prediction of Waiting Times in A&E. In: Mantas, John and Gallos, Parisis and Zoulias, Emmanouil and Hasman, Arie and Househ, Mowafa S and Charalampidou, Martha and Magdalinou, Andriana, (eds.) Healthcare Transformation with Informatics and Artificial Intelligence. (pp. pp. 36-39). IOS Press: Athens, Greece. Green open access

[thumbnail of Kunz_SHTI-305-SHTI230417.pdf]

Download (130kB) | Preview


Predicting waiting times in A&E is a critical tool for controlling the flow of patients in the department. The most used method (rolling average) does not account for the complex context of the A&E. Using retrospective data of patients visiting an A&E service from 2017 to 2019 (pre-pandemic). An AI-enabled method is used to predict waiting times in this study. A random forest and XGBoost regression methods were trained and tested to predict the time to discharge before the patient arrived at the hospital. When applying the final models to the 68,321 observations and using the complete set of features, the random forest algorithm’s performance measurements are RMSE=85.31 and MAE=66.71. The XGBoost model obtained a performance of RMSE=82.66 and MAE=64.31. The approach might be a more dynamic method to predict waiting times.

Type: Proceedings paper
Title: Prediction of Waiting Times in A&E
Event: ICIMTH (International Conference on Informatics, Management, and Technology in Healthcare) 2023
Location: Netherlands
ISBN-13: 978-1-64368-400-0
Open access status: An open access version is available from UCL Discovery
DOI: 10.3233/SHTI230417
Publisher version: https://doi.org/10.3233/SHTI230417
Language: English
Additional information: © 2023 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0)
Keywords: Waiting times, A&E, Random Forest, XGBoost
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10175187
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item