FHIR
FHIR Plugin – Overview & Available Actions
Summary (SEO Meta Description):
The FHIR Plugin in WeHub enables seamless data transformation and normalization between healthcare data standards such as HL7 v2, CCDA, and FHIR (STU3/R4).
It ensures interoperability across clinical systems by converting legacy and modern healthcare data into FHIR-compliant resources.
Overview
The FHIR Plugin is one of the most critical healthcare integrations in WeHub.
It provides three specialized actions to convert clinical data into the FHIR standard, ensuring interoperability across different healthcare systems and workflows.
This plugin is widely used where clinical messages, legacy documents, or older FHIR versions need to be standardized into modern FHIR resources.
Typical Use Cases:
- Legacy System Migration: Convert HL7 v2 messages or CCDA documents into FHIR bundles for modern EHR systems.
- FHIR Version Normalization: Standardize FHIR STU3 resources into R4, ensuring compatibility with updated APIs and workflows.
- Clinical Data Exchange: Enable healthcare providers to share data across different systems in FHIR-compliant formats.
- Analytics & Research: Normalize clinical datasets into FHIR for easier aggregation and analysis.
Available Actions
| Action | Description |
|---|---|
| HL7 v2 → FHIR | Converts HL7 v2 messages (e.g., ADT, ORM, ORU) into structured FHIR resources. |
| FHIR STU3 → R4 | Normalizes existing STU3-based resources into the widely adopted FHIR R4 version. |
| CCDA → FHIR | Transforms C-CDA clinical documents (CCD, Consultation Notes, Discharge Summaries, etc.) into FHIR resources. |
💡 Why It Matters
FHIR is the de-facto standard for healthcare interoperability.
By using this plugin, organizations can:
- Future-proof their workflows with FHIR-based standards.
- Ensure compliance with healthcare interoperability regulations.
- Simplify integrations across EHRs, labs, pharmacies, and third-party apps.
Keywords: WeHub FHIR Plugin, HL7 v2 to FHIR, CCDA to FHIR, FHIR STU3 to R4, healthcare interoperability, clinical data transformation