The Challenges of Healthcare Data Interoperability in the AI Era and FHIR Solutions

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In healthcare settings, systems often operate using proprietary formats and databases, necessitating significant effort for data consolidation. This lack of standardization has led to serious issues such as medical errors and delays. The COVID-19 pandemic highlighted how this data fragmentation hinders rapid and coordinated responses. To address these challenges, FHIR (Fast Healthcare Interoperability Resources), developed by HL7, functions as a standard. FHIR uses an architecture that represents clinical data as 'resources' in formats like JSON or XML, simplifying information exchange between disparate systems. Key advantages include inherent compatibility with LLMs and its semantic document structure which keeps relevant information contextually close. Furthermore, FHIR achieves true interoperability not alone, but by coexisting complementarily with other standards such as SNOMED CT for detailed clinical terminology and CIE-11 for international diagnostic coding. IHE (Integrating the Healthcare Enterprise) provides specific profiles to integrate these resources into real clinical workflows, enhancing practical usability in healthcare settings. The article emphasizes that while advanced technologies like AI and quantum computing offer potential, standardizing a 'common language' like FHIR remains crucial for building the foundation of data exchange.