Healthcare Data & FHIR for AI
1 of 6 modules available · 0 completed
Modules
- 1. Reading FHIR: Resources, JSON & ReferencesAvailable
- 2. Querying FHIR APIsNot yet released
- 3. Terminologies, Profiles & Implementation GuidesNot yet released
- 4. From FHIR to AI-Ready ContextNot yet released
- 5. SMART on FHIR, Access & PermissionsNot yet released
- 6. Capstone: Build an AI Data Pipeline over FHIRNot yet released
Also useful with this course
- Visual Guide · FHIR · API · MCP · RAG · LLM
- Visual Guide · How an AI Healthcare Application Fits Together
- Visual Guide · The Healthcare Data Landscape
How this content was reviewed
AI-assisted authoring was followed by structural checks, cross-model AI review and synthetic-user testing. No human clinical, regulatory, scientific or legal expert validation is currently claimed.
- AI-assisted authoring — completed
- Deterministic structural QA — completed
- Cross-model AI review — completed
- Synthetic-user testing — completed
- Human domain-expert review — not yet completed
Status checked 19 September 2026. Primary regulatory and legal summaries checked 25 August–10 September 2026.
Interoperability you can actually query
Healthcare Data & FHIR for AI
A six-module track in development on the data layer any healthcare AI system sits on. Module 1 — reading FHIR resources, references and Bundles — is available now. The remaining five modules (querying FHIR APIs, terminologies and implementation guides, AI-ready context, SMART on FHIR access, and a capstone pipeline) are planned and not yet released. Hands-on throughout, over synthetic resources only.
1 of 6 planned modules are released so far; the rest are in development.
Reading FHIR: Resources, JSON & References
Read a FHIR resource as a modelled clinical object rather than arbitrary JSON: ids versus business identifiers, the terminologies that supply coded clinical meaning, references between resources, searchset Bundles, and the version question you ask before any integration.
Querying FHIR APIs
The RESTful interaction style: search parameters, chained searches, _include, paginated Bundles, status and error handling, and reading a server's CapabilityStatement before you write a single query.
Terminologies, Profiles & Implementation Guides
Codes versus display text, SNOMED CT, LOINC and UCUM, how profiles and extensions constrain resources, validation, and why local and national implementation guides decide what your integration actually has to support.
From FHIR to AI-Ready Context
Turning retrieved resources into a bounded model input: temporal windows, provenance, missingness, deduplication, flattening versus preserving structure, and the minimum-necessary principle applied to context construction.
SMART on FHIR, Access & Permissions
The OAuth2 mental model behind healthcare app access: discovery, scopes, launch context, user-facing versus backend services access, and least privilege — conceptually, with no fake authentication.
Capstone: Build an AI Data Pipeline over FHIR
From a synthetic clinical workflow: retrieve and assemble the right resources, preserve provenance, validate the payload, and define failure handling and handoff to the consuming AI system.
Also useful with this course
- Visual Guide · FHIR · API · MCP · RAG · LLM
- Visual Guide · How an AI Healthcare Application Fits Together
- Visual Guide · The Healthcare Data Landscape
How this content was reviewed
AI-assisted authoring was followed by structural checks, cross-model AI review and synthetic-user testing. No human clinical, regulatory, scientific or legal expert validation is currently claimed.
- AI-assisted authoring — completed
- Deterministic structural QA — completed
- Cross-model AI review — completed
- Synthetic-user testing — completed
- Human domain-expert review — not yet completed
Status checked 19 September 2026. Primary regulatory and legal summaries checked 25 August–10 September 2026.