Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The increasing digitization of the healthcare industry and the need for interoperability have revealed the necessity of scalable data integration systems. HL7 and FHIR standards have been central throughout the process of facilitating semantically enriched and structured data interchange across different health information systems. However, the emergence of Big Data in healthcare introduces new concerns in terms of volume, velocity, and variety and demands the implementation of more sophisticated data pipelines.This paper provides a summary of the architecture and implementation of Big Data pipelines specifically adapted for HL7/FHIR interoperability, including the contributions of AI, modular design, federated systems, and open-source tools to ensure semantic consistency and operational scalability. Oncology case studies demonstrate the advantages of these pipelines in improving the quality, security, and usability of data in clinical and research environments. Compared to clinical and research case studies, real-world oncology hospital systems further demonstrate the benefits of these pipelines. The paper also presents examples of integrating FHIR with data lakes, CDA conversion processes, and AI-driven interoperability frameworks of the future, providing general insight into the current state of current implementations and their further evolution.