Artificial intelligence-driven forecasting and shift optimization for pediatric emergency department crowding
The AI-driven forecasting and shift optimization system demonstrated the efficacy of integrating AI and MLOps in predicting PED overcrowding and optimizing physician shifts. This approach outperformed traditional methods, highlighting its potential for managing overcrowding in emergency departments. Future research should focus on multicenter validation and real-world implementation to fully leverage the benefits of this innovative system.
Authors
- Özlem Tekşam (ORCID: https://orcid.org/0000-0003-1856-0500)
- Izzet Turkalp Akbasli (ORCID: https://orcid.org/0000-0003-3055-7355)
- Ahmet Ziya Bırbılen (ORCID: https://orcid.org/0000-0001-7131-3993)
Institutions
- Hacettepe University (TR)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1093/jamiaopen/ooae138
- Citations
- 11
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 24.79