Healthcare Data & FHIR for AI
1 of 6 modules available · 0 completed · ≈ 50 min available now · ≈ 6 h planned
Modules
- 1. Reading FHIR: Resources, JSON & References50 min
- 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
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, 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.