SpokeHub20260909

This draft FHIR R4 Implementation Guide defines a spoke-and-hub canonical contract for selected HL7 V2 laboratory and diagnostic-imaging interfaces. It includes logical models, exchange profiles, terminology, mapping guidance, examples, and an executable Python reference converter.

Executable scope

Flow Accepted messages Demonstrated versions Hub output
Lab order OMLO21, legacy ORMO01 V2.5.1 order fixture SpokeHubOrderBundle
Lab result ORU^R01 V2.5.1 and V2.7 fixtures SpokeHubResultBundle
Imaging order OMLO21, legacy ORMO01 mapper path; site fixture required SpokeHubOrderBundle
Imaging report ORU^R01 V2.5.1 RIS fixture SpokeHubResultBundle

The registry accepts governed spokes using V2.3.1, 2.4, 2.5, 2.5.1, 2.6, and 2.7. An allowed MSH-12 value is not a claim of full version coverage: each sender/event needs an interface profile and regression corpus. ADT, ACK/ORL, MDM, SIU, QBP/RSP, DFT, outbound V2, MLLP, acknowledgements, and durable delivery are not implemented in this release.

Canonical design

  1. FHIR Logical Models form the semantic canonical information model: meaning, V2 mappings, version harmonization, identifiers, and Z envelopes.
  2. FHIR R4 Resource Profiles form the exchangeable semantic canonical data model: validation, representation, message composition, provenance, and audit.
  3. CodeSystems, ValueSets, ConceptMaps, and NamingSystems own terminology and authority governance. Received codes remain marked source; only an actual map addition is marked canonical.

See the Canonical Model Decision, Architecture, and Version Strategy.

Package contents

Area Artifacts
Logical models V2 datatype, envelope, Z, lab order/result, imaging order/report
Profiles Patient, Specimen, ServiceRequest, Observation, DiagnosticReport, MessageHeader, source DocumentReference, Provenance, message Bundles
Terminology spoke and Z registries, provenance codes, value sets, NamingSystems, ConceptMaps
Conformance SH-R rules, convert-v2 OperationDefinition, converter CapabilityStatement
Mapping informative FML skeletons plus executable Python two-stage mapper
Runtime CLI, HTTP/FHIR APIs, optional fixed FHIR delivery, configuration, tests, containers

Start with Runtime API, Customization, and the Operations Guide. Replace the example canonical and publisher before publishing a jurisdictional derivative.