Design and Evaluation of a Scalable FHIR-Based Data Engineering Architecture for Integrating Heterogeneous Electronic Health Records

  • Authors

    • Venkata Manvitha Ala Independent Researcher, United States of America (USA). Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-V9I3P104

    Published 08-14-2026

  • Healthcare Data Engineering, Fast Healthcare Interoperability Resources (FHIR), Electronic Health Records (EHR) Integration, Semantic Interoperability, Scalable Data Architecture, Clinical Data Harmonization

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

    How to Cite

    [1]
    V. M. Ala, “Design and Evaluation of a Scalable FHIR-Based Data Engineering Architecture for Integrating Heterogeneous Electronic Health Records”, IJDEIC, vol. 9, no. 3, pp. 64–102, Aug. 2026, doi: 10.67228/30715717/IJDEIC-V9I3P104.

  • Abstract

    The rapid growth of electronic health record (EHR) systems has created significant challenges in integrating heterogeneous healthcare data generated from diverse clinical environments. Although Fast Healthcare Interoperability Resources (FHIR) provides a standardized approach for healthcare data exchange, existing solutions often focus on resource transformation while providing limited evaluation of information preservation, scalability, observability, and reliability. This study proposes SCALE-FHIR, a scalable FHIR-based data engineering architecture for integrating heterogeneous EHR sources through modular ingestion, a Canonical Clinical Event Model (CCEM), semantic harmonization, FHIR resource generation, validation, lineage tracking, and fault-handling mechanisms. The architecture supports multiple healthcare data representations, including CSV, C-CDA, and relational EHR structures, and transforms them into standardized FHIR R4 resources. The proposed framework is evaluated using Synthea synthetic records and MIMIC-IV clinical data through experiments measuring FHIR conformance, mapping completeness, field fidelity, information loss, throughput, latency, scalability, fault detection, and recovery performance. The evaluation demonstrates that SCALE-FHIR enables reliable transformation of heterogeneous healthcare data while maintaining clinical information integrity and supporting scalable processing. The contributions of this work include an end-to-end healthcare data engineering architecture, a fidelity-aware interoperability evaluation framework, integrated observability and fault recovery mechanisms, and a reproducible benchmark methodology for assessing FHIR-based integration systems. SCALE-FHIR provides a foundation for scalable healthcare analytics, research platforms, and interoperable digital health ecosystems.

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