Lithuanian Breast Diagnostics Implementation Guide
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Lithuanian Breast Diagnostics Implementation Guide, published by Lithuanian Medical Library. This guide is not an authorized publication; it is the continuous build for version 0.0.1 built by the FHIR (HL7® FHIR® Standard) CI Build. This version is based on the current content of https://github.com/HL7LT/ig-lt-breast/ and changes regularly. See the Directory of published versions

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Official URL: https://hl7.lt/fhir/breast/ImplementationGuide/lt.hl7.fhir.breast Version: 0.0.1
Computable Name: LtBreast

Lithuanian Breast Cancer Screening and Diagnostic Implementation Guide

Introduction and Purpose

This Implementation Guide specifies how to consistently represent and exchange structured clinical data related to the Breast Cancer Screening and Diagnostic Programme using the HL7® FHIR® standard.

The guide supports the national programme for early detection and diagnosis of breast cancer by defining interoperable data structures for imaging, clinical interpretation, diagnostic decisions, invasive procedures, and pathology reporting. Its purpose is to enable:

  • consistent and high-quality data capture across healthcare providers,
  • semantic interoperability between radiology, pathology, and clinical systems,
  • structured reporting for programme monitoring, quality assurance, and secondary use,
  • and reliable longitudinal follow-up of patients across diagnostic episodes.

The guide is developed as part of the national ADP project to support coordinated, data-driven management of preventive and early diagnostic programmes in Lithuania.

Scope

This guide focuses specifically on the breast cancer diagnostic and screening workflow, which is fundamentally different from lifestyle- or questionnaire-based preventive programmes.

It covers the following clinical domains:

  • Imaging acquisition (mammography, tomosynthesis, ultrasound),
  • Radiological interpretation and reporting using BI-RADS,
  • Explicit modelling of assessment and workflow decisions,
  • Follow-up actions (additional imaging, biopsy),
  • Invasive diagnostic procedures (image-guided biopsy),
  • Pathological histological examination and diagnostic reporting.

The guide models breast cancer diagnostics as a sequential, decision-driven, multi-disciplinary workflow that integrates radiology and pathology into a single longitudinal information model.

Key Modelling Principles

The modelling approach is based on the following core principles:

  1. Separation of data acquisition and interpretation
    Imaging procedures generate structured datasets, while interpretation and diagnosis are represented separately as diagnostic reports and assessments.

  2. Explicit separation of clinical observation and workflow logic
    For example, BI-RADS assessment represents what is observed, while follow-up recommendations and referrals represent workflow decisions derived from that assessment.

  3. Domain-specific modelling
    Breast imaging, radiology reporting, biopsy, and pathology require domain-specific profiles that are distinct from lifestyle, laboratory, or general preventive models.

  4. Longitudinal coherence
    All data elements are designed to support linkage across time, allowing follow-up imaging, biopsies, and pathology to be connected to earlier screening and diagnostic events.

Content of the Guide

This guide provides:

  • FHIR profiles and extensions for breast imaging, assessment, and diagnosis,
  • terminology bindings using SNOMED CT, LOINC and ICD-10-AM,
  • structured example instances illustrating real clinical scenarios,
  • mappings from clinical programme datasets to interoperable FHIR artefacts,
  • and identification of gaps and future development needs.

At the current stage, the guide includes the core workflow and a first set of key profiles and examples. Further refinement, terminology expansion, and clinical validation will be performed in subsequent iterations.

Why Use This Guide?

By adopting this guide, implementers and healthcare institutions can:

  1. Interoperability: Ensure consistent and comparable breast screening and diagnostic data across systems.
  2. Data Quality: Improve the consistency, completeness, and clinical usefulness of imaging and diagnostic data.
  3. Clinical Utility: Support structured reporting, quality assurance, population-level monitoring, and future clinical decision support.
  4. Longitudinal Care: Enable linkage of screening, diagnostics, treatment, and follow-up across time and providers.

Navigate the sections below to access the profiles, terminology bindings, and detailed examples needed to implement the standard.

IP Statements

Contributors

Name Role Organization
Igor Bossenko Primary Author HELEX
Kati Laidus Co-Author HELEX
Martynas Bieliauskas Reviewer LMB