Healthlink message quality

Validate HL7 v2 messages with confidence

Ensure your Healthlink HL7 v2 messages meet validation standards before submission. Intelligent auto-correction helps resolve errors, saving time and reducing rejections.

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Built for healthcare developers

Why use our validator?

Instant Validation

Validate HL7 v2 XML files against Gazelle EVS standards in seconds, with detailed errors, warnings, and compliance status.

AI-Assisted Auto-Correction

Fix common errors automatically and validate iteratively to help your message pass all checks.

Uses AI technology; see the transparency notice below.

Validation History

Track validations, download corrected files, and export detailed PDF reports at any time.

Secure and Private

Azure AD authentication and encrypted Azure SQL storage keep your files protected.

PDF Reports

Export professional reports with detailed error analysis for documentation and compliance records.

Analytics Dashboard

Review pass and fail rates, error trends, and message type breakdowns at a glance.

Supported HL7 v2 message types

Comprehensive validation and auto-correction for Healthlink message profiles.

All validators are configured from Gazelle EVS Healthlink profiles.

Patient Admin
  • ADT^A01Patient Admission (HL-1)
  • ADT^A03Patient Discharge (HL-5)
  • ADT^A04Patient Registration
  • ADT^A08Patient Update
Laboratory
  • ORU^R01Lab Results (HL-12)
  • ORU^R03Unsolicited Observation
  • OML^O21Lab Order (HL-13)
  • ORL^O22Order Response (HL-11)
Clinical
  • REF^I12Discharge Summary (HL-3)
  • RRI^R12Radiology Results (HL-9)
  • VXU^V04Vaccination (HL-16)
  • SIU^S12Appointment (HL-8)
System
  • ACK^GENERICGeneral Acknowledgement (HL-2)
Need another message type?
Register additional validators in your Gazelle EVS account.

Auto-correction capabilities

For all supported message types, the auto-corrector can fix:

  • UTF-8 BOM removal
  • XML declaration errors
  • Invalid HL7 table codes
  • Missing required fields
  • Message type validation
  • Segment ordering
  • Field length violations
  • Date format standardization

How it works

Simple, fast, and effective.

  1. 1

    Sign In

    Authenticate with your Microsoft account

  2. 2

    Upload

    Upload your HL7 v2 XML file

  3. 3

    Validate

    Get instant results with detailed reports

  4. 4

    Download

    Download corrected files or PDF reports

Ready to get started?

Sign in with your Microsoft account to start validating HL7 v2 messages.

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AI Transparency Notice (EU AI Act Compliance)

AI-Assisted Features

This application uses AI-powered auto-correction technology to assist with HL7 v2 message validation. Rule-based algorithms and pattern matching identify and suggest corrections for common validation errors.

How Auto-Correction Works

  • Detection: Gazelle EVS validation errors are analyzed.
  • Analysis: Rules identify correctable HL7 v2.4 issues.
  • Correction: Suggested fixes are applied to message content.
  • Validation: Messages are re-validated until complete or no more corrections are possible.

Human Oversight and Control

You maintain full control: corrections are performed at your request. Review reports, download corrected files, and verify every change before submission.

Important: Auto-correction may not resolve every issue. Always review corrected files and validation reports before submitting to production systems. This tool assists, but does not replace, human HL7 expertise.

Data Privacy and Security

  • Stored securely in Microsoft Azure SQL Database with encryption
  • Accessible only to you via Azure AD authentication
  • Not used for training AI models or shared with third parties
  • Retained in your validation history for your reference

Contact and Support

  • Technical Support: Contact your system administrator
  • Data Protection: See your organization's data protection officer
  • Feedback: Use the in-app feedback mechanisms after signing in

EU AI Act Classification: This system is classified as "Limited Risk" AI under Regulation (EU) 2024/1689. We are committed to transparency and ensuring users are informed when interacting with AI-assisted features.