Designing Privacy-First Health Monitoring Architectures Using Federated AI and FHIR-Compliant Azure Health Data Services In .NET

The rapid advancement of digital health technologies necessitates architectures that ensure data privacy, regulatory compliance, and intelligent analytics. This paper presents a privacy-first health monitoring system that integrates Federated Artificial Intelligence (AI), Fast Healthcare Interoperability Resources (FHIR), and Azure Health Data Services within a .NET framework. The proposed architecture enables real-time health data collection and decentralized machine learning while preserving patient privacy and meeting HIPAA and FHIR compliance standards. By leveraging federated learning, the system ensures that sensitive health data remains on local edge devices while enabling collaborative AI model training. Azure Health Data Services provide scalable, secure cloud infrastructure for storing and managing standardized clinical data, while the .NET platform ensures seamless integration and robust API development. The implementation demonstrates secure data exchange, enhanced interoperability, and privacy-aware analytics. This approach fosters a compliant, intelligent, and extensible foundation for future digital healthcare solutions, addressing both technological and ethical challenges in modern health informatics.