An Artificial Intelligence–Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study
The proposed prediction models effectively forecast ED waiting count at both hourly and daily intervals. The results demonstrate the value of integrating diverse data sources and applying advanced modeling techniques to support proactive resource allocation decisions. The implementation of these forecasting tools within hospital management systems has the potential to improve patient flow and reduce overcrowding in emergency care settings. The code is available in our GitHub repository.
Authors
- Khalid Y. Aram (ORCID: https://orcid.org/0000-0002-9406-8326)
- Abdulaziz Ahmed (ORCID: https://orcid.org/0000-0001-6081-8507)
- Bunyamin Ozaydin (ORCID: https://orcid.org/0000-0002-6775-0450)
- James Booth (ORCID: https://orcid.org/0000-0003-4290-6014)
- Orhun Vural (ORCID: https://orcid.org/0009-0004-7918-5307)
- Brittany F. Lindsey
Institutions
- University of Alabama at Birmingham Hospital (US)
- University of Alabama at Birmingham (US)
- Emporia State University (US)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-08-04
- DOI
- https://doi.org/10.2196/73960
- Citations
- 2
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 4.51