Bridging the Gap: Integrating Heterogeneous Clinical Data into HL7 FHIR

Abstract

The fragmentation of patient data across different and disconnected systems, ranging from electronic health records to Artificial Intelligence (AI)-based diagnostic tools, poses a major challenge to the delivery of an efficient and accurate healthcare system. This paper proposes a modular and interoperable architecture designed to integrate heterogeneous clinical data from different sources, including structured clinical records, sociohealth information, patient-generated data, and outputs from AI-based diagnostic systems such as imaging analysis. The proposed architecture facilitates seamless data harmonization and supports clinical decision-making by structuring integrated information through the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. This enables standardized data exchange and full interoperability with existing Health Information Systems, including Electronic Health Records and Telemedicine Platforms. An Implementation Guide is proposed as a reference framework for validating the FHIR resources produced by the architecture. In addition, a key feature of the architecture is its embedded Clinical Decision Support System, which dynamically identifies and presents only the clinically relevant information required for diagnostic reasoning and risk assessment.

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