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Describing Data Processing in FHIR: AI-Assisted Interoperability for Cancer Stage Extraction.

David Ouagne1, Vincent Zossou1, Bastien Rance1,2

  • 1AP-HP, Paris, France.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Generative AI assists in documenting FHIR workflows for TNM cancer staging extraction. This approach streamlines the creation of interoperable clinical data models, reducing complexity and development time.

Keywords:
generative artificial intelligenceinteroperabilitylarge language modelsreproducibility

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Area of Science:

  • Clinical informatics
  • Artificial intelligence in healthcare
  • Health data standards

Background:

  • Creating interoperable clinical data models using Fast Healthcare Interoperability Resources (FHIR) is crucial but time-consuming.
  • This study addresses the challenge of documenting and structuring FHIR-based data transformation workflows.

Purpose of the Study:

  • To explore the application of Generative AI in automating the documentation and structuring of FHIR data transformation workflows.
  • Specifically focusing on the extraction of TNM cancer staging information.

Main Methods:

  • Utilized FHIR Release 4 and the PlanDefinition resource to model transformation processes.
  • Employed Business Process Model and Notation (BPMN) to define workflows.
  • Leveraged the large language model Claude Code (Sonnet 4.5) to generate FHIR artifacts from BPMN inputs.
  • Validated generated artifacts through syntax checks, Implementation Guide compilation, and expert review.

Main Results:

  • AI-assisted generation successfully produced a validated PlanDefinition with seven structured activities.
  • The generated artifacts accurately represented the TNM extraction workflow.
  • All FHIR artifacts were interoperable and passed conformance tests after minor revisions.

Conclusions:

  • Generative AI effectively supports FHIR workflow modeling, enhancing efficiency and reducing complexity.
  • Expert validation remains critical for ensuring semantic accuracy and reproducibility of AI-generated FHIR artifacts.