Clinician-in-the-loop screening saturation: predicting annotation yield for efficient EHR review
The proposed scalable, model-agnostic framework turns AI scores into actionable staffing decisions in clinical workflows. This screening-saturation model integrates with clinician-in-the-loop (CITL) tools and readily adapts across medical domains requiring lean chart review.
Authors
- Fatemeh Mohammad Alizadeh Chafjiri (ORCID: https://orcid.org/0000-0003-2467-5777)
- Jennifer V. Gettings (ORCID: https://orcid.org/0000-0002-4576-3293)
- Tobias Loddenkemper (ORCID: https://orcid.org/0000-0003-2074-0674)
- Assaf Landschaft (ORCID: https://orcid.org/0009-0004-1698-1496)
- Molly Ann Puckett
- Leena Abdelmoity
Institutions
- Boston Children's Hospital (US)
- Harvard University (US)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-10-31
- DOI
- https://doi.org/10.1186/s12911-025-03241-y
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00