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.
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Abstract
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-pp.ucl.ac.uk/id/eprint/10175187 |
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