Abstract
Federated learning (FL) represents a paradigm shift in artificial intelligence (AI) by enabling collaborative model training across decentralized entities—such as hospitals, insurance providers, and clinics—without requiring the exchange of raw patient data. This approach is particularly transformative in healthcare, where data privacy regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and impose strict limitations on data sharing. In the context of urgent care denials—such as rejected pre-authorizations, triage prioritization errors, or resource allocation decisions—FL offers a way to improve AI-driven decision-making while preserving patient confidentiality.
This elaborates on a federated AI framework designed to address critical challenges in urgent care denial management, including systemic biases, data imbalances, and compliance risks. By decentralizing data processing and integrating privacy-preserving technologies, the system ensures that sensitive patient information remains localized while enabling institutions to collaboratively refine denial prediction models.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Sharma, Kush, "Federated AI Learning in Healthcare: Comprehensive Framework for Managing Urgent Care Denials", Technical Disclosure Commons, (May 04, 2025)
https://www.tdcommons.org/dpubs_series/8081