Interoperability and Integration Frameworks for AI in Healthcare Enterprises: Leveraging HL7 FHIR and Cloud APIs
Year: 2025
Type: article
Abstract: Contemporary healthcare landscape data faces unprecedented challenges in integration and system interoperability as organizations have rapidly adopted artificial intelligence technologies to increase clinical decisions, operational efficiency, and patient outcomes. Healthcare enterprises work within complex ecosystems, including electronic health records, laboratory information systems, imaging platforms, clinical decision support equipment, and many special applications, ownership data formats, and communication protocols. HL7 FHIR has emerged as a transformational standard that addresses integration challenges through modern, web-based architecture and standardized resource definitions, enabling a more accessible and developer-aligned integration structure. Cloud-based API has revolutionized healthcare interpreting by providing a scalable, safe platform for data exchange and application integration, offering special health services API with major cloud providers, which add to FHIR compliance with advanced security facilities and machine learning services. The convergence of FHIR standards and cloud API technologies creates unprecedented opportunities to implement refined AI solutions for healthcare enterprises that work within organizational boundaries while maintaining regulatory compliance and ensuring optimal patient care results.
Author: Venkateswara Reddi Cheruku
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7080449350
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