AWS describes HealthLake as a managed FHIR persistence layer and says a data-transformation agent can convert legacy clinical documents into queryable FHIR resources. A converted resource still needs source-document identity, field mapping, profile and version, terminology, uncertainty, validation, and receiving-workflow evidence before it can be trusted for use.
Firely presents server, API, validation, and conformance tooling for FHIR implementations. An endpoint can accept and return resources while still missing required profiles, terminology, search behavior, authorization, provenance, or production workflow semantics.
Google Cloud's official overview places FHIR, HL7v2, and DICOM data in modality-specific stores with distinct APIs. One managed service can centralize operations without making the formats, meanings, consent rules, or clinical workflows interchangeable.
HL7's permanent R4 publication defines resources, formats, APIs, terminology, and conformance infrastructure. A production exchange still needs the applicable profiles, implementation guide, versions, partner agreement, security, and tested behavior.
The current published specification gives architects a version to assess, but adoption still depends on implementation-guide dependencies, partner support, artifact maturity, and conformance evidence.
HL7's current PDex guide maps payer clinical, claims, encounter, and prior-authorization data into FHIR exchange. Trust depends on preserving source mappings, versions, provenance, and reconciliation.
HL7's current published guide separates export kickoff, status polling, completion manifest, files, and errors. A working endpoint is only the start; buyers need the whole job and its authorization boundary.
HL7 lists SMART App Launch 2.2 as the current published authorization guide. A FHIR endpoint still needs scoped access, token controls, and operating evidence.