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Predicting emergency department disposition using machine learning and large language models to support proactive capacity management: a multicenter retrospective study

Crossref DOI link: https://doi.org/10.1186/s12873-026-01487-x

Published Online: 2026-02-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Zeinali, Farzad

Taaffe, Kevin

Gaafary, Chris

Jackson, William

Ramsay, Michael

Hobbs, Jessica

Pirrallo, Ronald
Funding

Funding for this research was provided by:

Prisma Health
License Information

Text and Data Mining valid from 2026-02-12

Version of Record valid from 2026-03-18
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Article History

Received: 29 October 2025

Accepted: 21 January 2026

First Online: 12 February 2026

Declarations

:

: This study was approved by the Clemson University Institutional Review Board (IRB2024-0339) and conducted in accordance with the Declaration of Helsinki. The requirement for informed consent was waived due to the retrospective nature of the study and use of de-identified data.

: Not applicable. This manuscript does not contain individual data.

: The authors declare no competing interests.

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