Enhancing healthcare interoperability and intelligence: AI integration with FHIR

13^th Global Summit on Artificial Intelligence and Neural Networks

March 14, 2025 | Webinar

Goutham Bilakanti

Cigna Healthcare, USA

Scientific Tracks Abstracts: J Comput Sci Syst Biol

Abstract :

The integration of Artificial Intelligence (AI) with Fast Healthcare Interoperability Resources (FHIR) is revolutionizing healthcare by enhancing data interoperability, predictive analytics, and clinical decision-making. FHIR provides a standardized framework for healthcare data exchange, while AI leverages this structured data to drive automation, personalized treatment, and operational efficiency. This paper explores the synergy between AI and FHIR, discussing key applications such as predictive analytics, natural language processing (NLP) for unstructured data, and AI-powered decision support. The challenges of data privacy, security, and integration complexities are also addressed. The future of AI driven FHIR solutions promises improved patient outcomes, streamlined workflows, and a more intelligent healthcare ecosystem. Summary FHIR, developed by HL7, is a widely adopted standard for healthcare data exchange, enabling seamless interoperability among electronic health records (EHRs) and other healthcare systems. The integration of AI with FHIR allows for more advanced data utilization, driving automation, insights, and precision medicine. Key Applications â?¢ Predictive Analytics â?? AI models use FHIR data to predict disease risks, readmission probabilities, and treatment outcomes. â?¢ Natural Language Processing (NLP) â?? AI extracts meaningful insights from unstructured clinical notes, converting them into FHIR-compatible data. â?¢ Clinical Decision Support â?? AI enhances real-time decision-making by analyzing patient data within FHIR based systems. â?¢ Automated Data Processing â?? AI-powered automation reduces administrative burdens and improves data accuracy. Challenges â?¢ Data Privacy & Security â?? Ensuring compliance with HIPAA and GDPR regulations when using AI on patient data. â?¢ Integration Complexity â?? Harmonizing AI algorithms with existing FHIR-based healthcare infrastructures. â?¢ Bias & Explainability â?? Addressing AI model biases and ensuring transparency in decision-making. Future Outlook The convergence of AI and FHIR is poised to transform healthcare by making data more actionable and patient care more precise. As AI models become more sophisticated, their integration with FHIR will lead to smarter, more efficient healthcare systems, ultimately enhancing patient outcomes and reducing costs.

Biography :

Goutham Bilakanti is a Senior Lead Software Engineer at Cigna Healthcare, renowned for his groundbreaking contributions to healthcare and financial systems. With over twenty peer-reviewed publications in top industry journals, he has established himself as an influential researcher and thought leader. His work spans critical areas such as cognitive artificial intelligence (AI), smart card technology applications in banking, and block-chain implementations, all of which have shaped security protocols adopted by financial institutions globally. A key focus of my research has been AI applications in card payment systems and healthcare supply chain optimization. In addition to his academic contributions, Goutham Bilakanti led innovative projects integrating behavioral health analytics and digital twin technology. Through my combination of technical expertise, strategic vision, and practical implementation, he continues to drive progress in both the financial and healthcare industries, shaping the future of digital technology with a focus on security, efficiency, and accessibility.