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HL7 TO FHIR CONVERTER

Convert HL7v2 messages into FHIR resources.

Convert legacy messages into FHIR R4 bundles. Review the output, check profiles and prepare data for your next integration.

Browser-based evaluationFHIR R4 outputSingle-message and batch APIs

FROM INTERFACES TO RESOURCES

Make legacy messages useful in a FHIR workflow.

Integration teams need more than a change in format. Use a representative message to understand its FHIR output before connecting downstream systems.

In practiceLegacy feeds

Bridge an existing interface.

Evaluate how a familiar HL7v2 feed can supply a FHIR-based application.

Interface teamsFHIR adoption

Features

Convert, check and inspect in one workflow.

Move from a source message to output your team can review.

CONVERSION

HL7v2 in. FHIR R4 out.

Upload messages such as ADT, ORU, RAD and TRN for conversion into FHIR bundles.

PROFILES

Check the target structure.

Validate generated output against standard profiles, including US Core and IPS.

INSPECTION

Look inside the result.

Use the built-in viewer to inspect resources and their relationships.

EXPORT

Take the output forward.

Copy or export converted resources for downstream integration work.

SINGLE MESSAGE

Connect through REST.

Submit individual messages from your integration workflow.

BATCH

Evaluate beyond one message.

Use the batch API for larger test sets; the browser handles one message at a time.

UPLOAD. CONVERT. REVIEW.

Follow a message into FHIR.

Start in the browser, then explore API access for repeatable conversion.

Developers & QA

Bring conversion into your pipeline.

REST endpoints support individual messages and batch testing.

  • Convert a single HL7v2 message
  • Process a batch of test messages
  • Retrieve FHIR output for review and integration
Get instant access

HL7 to FHIR Converter: sample workflow

HL7 to FHIRSAMPLE WORKSPACE
SOURCE · HL7V2
MSH|^~\&|HIS|RIH|EHR|RIH|20260310||ADT^A01|00001|P|2.5
PID|1||MRN-483920||Smith^Emma||19850412|F
PV1|1|I|2000^2012^01
OBX|1|NM|8867-4^Heart rate||72|/min
MAP + NORMALIZE
PID.5 → Patient.name
PID.7 → Patient.birthDate
PV1 → Encounter
OBX → Observation
Source fields → mapping rules → target resources
OUTPUT · FHIR R4
{
  "resourceType": "Bundle",
  "type": "collection",
  "entry": [
    { "resource": { "resourceType": "Patient" } },
    { "resource": { "resourceType": "Encounter" } },
    { "resource": { "resourceType": "Observation" } }
  ]
}

Security & trust

A focused environment for evaluation.

Use synthetic or de-identified data only.

Explore the tool with representative test files before planning your production workflow.

Test data only

Keep real patient data out of the trial environment.

HTTPS connections

Data transfers use encrypted connections.

Temporary uploads

The product does not retain uploaded source files.

Production planning

Discuss your deployment and data-handling requirements with the Hgear team.

Who it is for

Built around integration work.

For interface analysts, healthcare developers and QA teams.

Interoperability engineersIntegration developersInterface analystsQA teamsHealthcare product teamsFHIR implementers
  • Explore legacy feed modernization
  • Review output with integration and QA teams
  • Evaluate conversion before downstream ingestion

Common questions

HL7 to FHIR Converter FAQ

Which messages and FHIR version are supported?

The converter supports HL7v2 messages including ADT, ORU, RAD and TRN, with FHIR R4 output.

Can I convert multiple messages?

The browser handles one message at a time. A batch endpoint is available through the REST API.

How can I review the converted data?

Use the built-in viewer to inspect the resulting resources, then copy or export the output.

What data can I use in the trial?

Use synthetic or de-identified messages only. Do not submit real patient data during evaluation.

Start with one representative message.

Explore the converter with test data, then plan the integration around your source systems.

Try free HL7 to FHIR

Connect message conversion with Hgear’s broader transformation and data-quality workflows.

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