AI-Powered Healthcare Data Exchange

Abstract

This article explores the transformative convergence of artificial intelligence and healthcare interoperability standards, examining how the integration of AI technologies with Health Level Seven (HL7) and Fast Healthcare Interoperability Resources (FHIR) is revolutionizing healthcare delivery and data management. The research demonstrates how FHIR's modern architecture has addressed the limitations of earlier standards, providing a robust foundation for AI applications across diverse healthcare domains, including predictive analytics, natural language processing, and real-time clinical decision support. Through comprehensive analysis of current implementations, the study reveals how standardized data formats enable AI systems to process clinical information consistently across different healthcare settings, facilitating the development of generalizable models for disease prediction, risk stratification, and clinical workflow optimization. The integration of transformer-based NLP models with clinical document architectures unlocks previously inaccessible narrative data, while FHIR-enabled real-time systems transform reactive care models into proactive, preventive healthcare delivery paradigms. The findings indicate that the synergy between AI and healthcare interoperability standards not only improves clinical outcomes and operational efficiency but also addresses critical challenges such as healthcare fraud detection and adverse drug event monitoring across multi-center networks, ultimately establishing a foundation for the next generation of intelligent, interconnected healthcare systems.