Human performance evaluation of a pediatric artificial intelligence sepsis model
This study demonstrates the feasibility of evaluating multiple dimensions of human performance using a mixed methods approach for an AI model implemented in clinical practice. Future studies should aim to reduce the measurement burden of human performance metrics associated with AI implementation in acute care settings and assess the correlation between human performance measures and clinical outcomes.
Authors
- Naveen Muthu (ORCID: https://orcid.org/0000-0002-8259-6965)
- Thuy Bui (ORCID: https://orcid.org/0000-0001-6782-8310)
- Rebekah Carter
- Mark Mai (ORCID: https://orcid.org/0000-0003-0346-5818)
- Nikolay Braykov
- Evan Orenstein (ORCID: https://orcid.org/0000-0003-3756-8575)
- Swaminathan Kandaswamy (ORCID: https://orcid.org/0000-0003-2109-5769)
- Reena Blanco
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/ocaf106
- Citations
- 1
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- National Institutes of Health
- Agency for Healthcare Research and Quality
- U.S. National Library of Medicine
- National Center for Advancing Translational Sciences