How AI Analytical Models Can Use FHIR (Fast Healthcare Interoperability Resources) Data

In the ever-evolving landscape of healthcare, the integration of artificial intelligence (AI) has emerged as a transformative force. Analytical Models (AMs) is a subset of AI used in this space. AMs are used to provide novel predictive functionality that can help advance the quality and outcomes of healthcare. The goal advanced by the organization behind FHIR, FHIR Fast Healthcare Interoperability Resources (FHIR), is to enable electronic health exchange of electronically processed data between health record systems. In order to build a robust framework through which healthcare providers can exchange such information, FHIR Instant Messaging (IM) was developed as an API for health record systems. The framework used to develop IM is known as the Resource Description Framework Specification Versions. This study delves into the symbiotic relationship between AI analytical models and the Fast Healthcare Interoperability Resources (FHIR) data standard, aiming to unlock new dimensions of interoperability and data-driven decision-making within the healthcare sector. With the healthcare systems continuing to grapple with the challenges of siloed data and inefficiencies in information exchange, it becomes important to have a comprehensive exploration of how AI can bridge these gaps. Leveraging the FHIR standard as a robust foundation, it becomes significant to elucidate the potential of AI in harnessing patient data. They are also facilitating seamless data exchange among healthcare stakeholders while also empowering clinicians with actionable insights.