Evaluating an AI Decision Support System for the Emergency Department: Retrospective Study
The AI model shows significant potential to reduce the time to admission decisions, alleviate ED overcrowding, and improve patient care. The model offers the advantage of always providing weighted advice on admission, even when the ED is under pressure. Future prospective studies are needed to assess the impact in the real world and further enhance the performance of the model in diverse hospital settings.
Authors
- Renata Medeiros de Carvalho (ORCID: https://orcid.org/0000-0001-6129-9278)
- Marleen Vreeburg
- Boudewijn F. van Dongen (ORCID: https://orcid.org/0000-0002-3978-6464)
- Annemarie de Jong
- Stef Jansen (ORCID: https://orcid.org/0000-0001-5729-5952)
- Terry van Dijk (ORCID: https://orcid.org/0000-0002-0927-6121)
- Robert van der Plas
- Yvette Van Der Haas
- Chang Ming Li (ORCID: https://orcid.org/0000-0002-4041-2574)
- Marieke S. Sanders (ORCID: https://orcid.org/0009-0002-1054-2324)
- Bart Overbeek
- Wiesje Roskamp
- Dorien Melman
- Arjan van de Merwe
Institutions
- St. Antonius Ziekenhuis (NL)
- Eindhoven University of Technology (NL)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-12-05
- DOI
- https://doi.org/10.2196/80448
- Citations
- 1
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.25