Developing and Applying the BE-FAIR Equity Framework to a Population Health Predictive Model: A Retrospective Observational Cohort Study
The BE-FAIR framework offers a practical approach to address bias in healthcare predictive models, guiding model development, and implementation. By identifying and mitigating biases, BE-FAIR enhances the fairness and equity of healthcare delivery, particularly for minoritized groups, paving the way for more inclusive and effective population health strategies.
Authors
- Jeffrey S. Hoch (ORCID: https://orcid.org/0000-0002-4880-4281)
- Sili Fan (ORCID: https://orcid.org/0000-0002-3497-1695)
- Mayu Sasaki
- Daniel J. Tancredi (ORCID: https://orcid.org/0000-0002-3884-7907)
- Hendry Ton (ORCID: https://orcid.org/0000-0001-7595-4333)
- Reshma Gupta (ORCID: https://orcid.org/0000-0002-2108-3007)
- Sandra L. Taylor (ORCID: https://orcid.org/0000-0001-7811-812X)
- Jason Y. Adams (ORCID: https://orcid.org/0000-0001-5623-8702)
- Yi Zhang (ORCID: https://orcid.org/0000-0002-4423-1658)
- Matthew Crase
Institutions
- Center For Policy Research (US)
- Center for Health and Gender Equity (US)
- University of California Davis Medical Center (US)
- UC Davis Health (US)
- UC Davis Children's Hospital (US)
- University of California, Davis (US)
Publication Details
- Journal
- Journal of General Internal Medicine
- Published
- 2025-03-14
- DOI
- https://doi.org/10.1007/s11606-025-09462-1
- Citations
- 2
- Primary Topic
- Healthcare Policy and Management
- Type
- article
- Field-Weighted Citation Impact
- 6.26