End-to-End CCDA to FHIR Conversion Pipeline Using NLPEnd-to-End CCDA to FHIR Conversion Pipeline Using NLP
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Hasshya Moorthy
• Jun 23
Built an end-to-end healthcare interoperability pipeline that converts CCDA (Clinical Document Architecture) XML files into standardized FHIR resources using a hybrid approach combining deterministic extraction, NLP-assisted ontology mapping, and semantic normalization.
Problem Solved Healthcare systems often store clinical data in heterogeneous formats, making interoperability difficult. Manual conversion of CCDA records into FHIR-compliant resources is time-consuming and error-prone.
Solution Implemented
Developed streaming XML parsing for efficient handling of large clinical datasets
Extracted structured and unstructured medical data from CCDA documents
Integrated NLP pipeline using spaCy/SciSpaCy for:
Clinical entity recognition
Contextual linking
Negation detection
Mapped concepts to healthcare standards:
SNOMED CT
LOINC
RxNorm
Generated validated FHIR R4 resources and JSON bundles
Added human-in-the-loop review workflow and validation dashboard
Key Features CCDA XML → FHIR conversion NLP-assisted semantic mapping Clinical ontology integration Confidence scoring system (S-MCS) Validation and compliance checks Streamlit monitoring dashboard
Tech Stack Python • lxml • spaCy • SciSpaCy • SNOMED CT • LOINC • RxNorm • FHIR R4 • HAPI FHIR • Streamlit • Docker
Links GitHub: https://github.com/hasshya1530/hybrid_ccda_to_fhir_convertor Demo:  https://hybrid-ccda-to-fhir-convertor.streamlit.app
StreamlitMachine Learning EngineeringAI DevelopmentAI Engineer PythonspaCy
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