Optimizing Interoperability In Healthcare: AI-Driven HL7 And FHIR Implementations For Seamless Data Exchange

Abstract

Interoperability remains a significant issue in healthcare, wherein isolated data systems disrupt smooth communication and synchronized care. This study discovers the ways in which Artificial Intelligence (AI) can be employed for enhancing interoperability through enhanced use of Health Level 7 (HL7) and Fast Healthcare Interoperability Resources (FHIR) standards. Through the use of AI-based techniques such as NLP, machine learning, and rule-based reasoning, healthcare systems can auto-map data, improve semantic accuracy, and enable real-time data exchange across diverse platforms. The paper analyzes case studies of AI-enhanced HL7/FHIR deployments in electronic health records (EHRs), patient portals, and clinical decision support systems. It highlights the way AI enables adaptive data conversion and facilitates compliance with regulations such as HIPAA. The outlined AI-enhanced interoperability framework enables secure, scalable, and seamless data sharing that is crucial for better patient outcomes, reducing administrative burden, and the advancement of personalized medicine.