Interoperability and Integration Frameworks for AI in Healthcare Enterprises: Leveraging FHIR and Cloud APIs
Sarcouncil Journal of Engineering and Computer Sciences – SARC Publisher
Summary
This paper addresses the complex challenges of integrating and ensuring interoperability of healthcare data, which is exacerbated by the rapid adoption of Artificial Intelligence (AI). It proposes a new data integration model leveraging HL7 FHIR standards combined with cloud APIs to achieve optimal patient care while maintaining regulatory compliance.
Details
The contemporary healthcare landscape faces significant challenges in data integration and system interoperability due to the rapid adoption of AI technologies. Healthcare enterprises operate within complex ecosystems, including EHRs, LIS, imaging platforms, and various specialized applications, each with unique data formats and protocols. This paper highlights HL7 FHIR as a transformative standard that addresses these integration issues through its modern, web-based architecture and standardized resource definitions, enabling an accessible, developer-aligned structure. Furthermore, cloud-based APIs revolutionize healthcare by offering scalable and secure platforms for data exchange. By integrating specialized health services APIs from major cloud providers, compliance with FHIR is enhanced alongside advanced security features and machine learning capabilities. The convergence of FHIR standards and cloud API technologies creates unprecedented opportunities to implement refined AI solutions within organizational boundaries while ensuring regulatory adherence and optimal patient care outcomes.
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