Early clinical evaluation of a vendor developed pediatric artificial intelligence sepsis model in the emergency department
When tailoring a vendor-developed pediatric ED sepsis model for real-world usage, predictive performance differed substantially. Post-implementation we found improvements in care process measures, suggesting such models may benefit sepsis care when adapted for specific clinical workflows.
Authors
- Naveen Muthu (ORCID: https://orcid.org/0000-0002-8259-6965)
- John Cheng (ORCID: https://orcid.org/0000-0003-4605-5675)
- Thuy Bui (ORCID: https://orcid.org/0000-0001-6782-8310)
- Jonathan Beus (ORCID: https://orcid.org/0000-0003-0911-0823)
- Rebekah Carter
- Andrea McCarter
- Mark Mai (ORCID: https://orcid.org/0000-0003-0346-5818)
- Nikolay Braykov
- S Brown
- Srikant Iyer
- Edwin Ray
- Tal Senior
- Hannah Thummel
- Malachy J. Gleeson
- Evan Orenstein (ORCID: https://orcid.org/0000-0003-3756-8575)
- Swaminathan Kandaswamy (ORCID: https://orcid.org/0000-0003-2109-5769)
- Kiran Hebbar
- Reena Blanco
- James D. Fortenberry (ORCID: https://orcid.org/0000-0001-9041-2903)
Institutions
- Emory University (US)
- Children's Healthcare of Atlanta (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-07-19
- DOI
- https://doi.org/10.1093/jamia/ocaf105
- Citations
- 2
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.71
Funders
- National Institutes of Health
- Agency for Healthcare Research and Quality
- U.S. National Library of Medicine
- National Center for Advancing Translational Sciences