Considerations for using AI to model data exchange (interoperability) in FHIR
This article outlines where AI can meaningfully support interoperability work with HL7 FHIR—not as an autopilot, but as a co‑pilot that accelerates consolidation, documentation, and quality checks. It covers AI-assisted capture and presentation of internal business processes, the definition of process metadata, the creation of profile drafts, validation and simulation, bundling, and building an Implementation Guide (IG). Key takeaway: AI improves speed and consistency when traceability, governance, and human review are treated as non‑negotiables.
Part 1 — Positioning: AI as a co‑pilot for interoperability
Interoperability rarely fails because of “FHIR itself”. It fails when domain process knowledge does not translate into clear, testable exchange rules. This is where AI can help: as a co‑pilot that structures, consolidates, and proposes drafts—while domain experts and interoperability architects remain accountable for the decisions.
The goal is not “automation at all costs”, but shorter feedback loops, better consistency, and higher testability from process understanding to an operational Implementation Guide.
Part 1 — From internal processes to interoperability scenarios
1. Capturing and presenting business processes
Process knowledge is often scattered across workshops, tickets, emails, PDFs, and spreadsheets. AI can consolidate these sources and introduce a shared language across business and technical stakeholders:
- Extract actors, triggers, inputs/outputs, and decision points,
- Detect contradictions, gaps, and duplicates,
- Present hand‑offs as a clear narrative (who sends what, when, to whom, and why).
2. Process metadata: the glue for stable exchange definitions
Robust exchange specifications often need metadata such as scope, ownership, systems involved, data stewardship, legal basis, security level, versioning, frequency, and quality expectations. AI can propose metadata and run completeness checks—final approval remains with the responsible roles.
- Completeness checks (e.g., missing owner/trigger/receiver),
- Standardized templates for process and exchange descriptions,
- Consistency checks across processes (terms, roles, system names).
3. Bridging into FHIR thinking (without going deep technical)
Before profiling, AI can help map domain concepts to likely FHIR building blocks: actors, events, documents or transactions, and data points. The result is an interoperability backlog of scenarios that are clear, prioritized, and testable.
Part 2 — From drafts to quality: profiles, validation, and simulation
4. Transforming captured requirements into profile drafts
AI can assist with profile drafts by proposing candidate resources, drafting constraints (e.g., cardinalities, required elements, “Must Support”), and producing descriptions and examples. However, profiling decisions are often shaped by governance and scope boundaries—AI provides suggestions, while humans own the architecture.
5. Validation and simulation: the biggest leverage
The highest return often comes from quality work. AI can generate scenario-based test cases, create synthetic example data, and identify inconsistencies early—before implementation costs accumulate.
- Test case generation (happy paths and edge cases),
- Explain validation errors in plain language,
- Impact analysis: “What breaks downstream if field X is missing?”
Part 2 — Bundles and the Implementation Guide: consistency & communication
6. Assembling profiles into bundles
When multiple profiles interact, references and dependencies become critical. AI can propose bundle structures from use cases, check references, and highlight cross-profile inconsistencies (e.g., identifier patterns or coding strategies).
7. Building and maintaining an Implementation Guide (IG)
An IG is not only technical documentation—it is communication. AI can help with editorial work: harmonizing language, structuring chapters, generating examples, FAQs, glossaries, and change summaries, and supporting multilingual documentation.
Three non‑negotiables
- Traceability: every rule must map back to a need or use case.
- Testability: everything in the IG must be verifiable via test cases.
- Operability: versioning, deprecation, ownership, and change communication.
Closing thought: AI shortens the distance between process knowledge and implementable interoperability—when governance, evidence, and human review are built in by design.