Bridging the Gap: Integrating Heterogeneous Clinical Data into HL7 FHIR

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 archi- tecture designed to integrate heterogeneous clinical data from different sources, including structured clinical records, socio- health information, patient-generated data, and outputs from AI- based diagnostic systems such as imaging analysis. The proposed architecture facilitates seamless data harmo- nization and supports clinical decision-making by structuring integrated information through the HL7 Fast Healthcare Interop- erability 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.