Lithuanian Lung Cancer 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-lung/ and changes regularly. See the Directory of published versions

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Oficialus URL: https://hl7.lt/fhir/lung/ImplementationGuide/lt.hl7.fhir.lung				Versija: 0.0.1	Paryškinti teksto pakeitimus?
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Lithuanian Lung Cancer Early Diagnosis Implementation Guide

Introduction and Purpose

This Implementation Guide specifies how to consistently represent and exchange structured clinical data related to the Lithuanian Lung Cancer Early Diagnosis Programme using the HL7® FHIR® standard.

The guide supports the national programme for early detection of malignant bronchial and lung tumours, with a primary focus on low-dose chest computed tomography (LDCT) and the structured capture of clinically relevant findings, recommendations, and pre-examination risk information.

Its purpose is to enable:

• consistent and high-quality data capture across healthcare providers,
• semantic interoperability between screening, radiology, referral, and follow-up systems,
• structured reporting for programme coordination, quality assurance, monitoring, and secondary use,
• and reliable longitudinal follow-up of participants across repeated screening and surveillance cycles.

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 lung cancer early diagnosis workflow, centred on low-dose chest computed tomography (LDCT) and the structured collection of data required before, during, and after the imaging examination.

It covers the following clinical domains:

• Pre-examination questionnaire data, including functional status, smoking history, anthropometric data, recent respiratory infection history, and prior chest CT history,
• LDCT imaging acquisition metadata, including radiation dose information and examination context,
• Structured recording of pulmonary nodules and other suspicious focal findings,
• Classification of findings and programme recommendations,
• Structured capture of significant incidental findings in the chest and upper abdominal region,
• and support for repeated imaging and surveillance over time.

The guide models lung cancer early diagnosis as an imaging-driven, risk-informed, longitudinal workflow, where the main diagnostic decision point is the interpretation of LDCT findings and the corresponding recommendation for repeat screening, short-term surveillance, or specialist referral.

Key Modelling Principles

The modelling approach is based on the following core principles:

1. Separation of questionnaire data, imaging acquisition, and radiological interpretation
   Pre-examination risk and eligibility information, technical imaging details, and radiological conclusions are represented as distinct but linked parts of the workflow.

2. Explicit representation of focal lung findings
   Pulmonary nodules and other suspicious lesions are modelled in a structured way, including type, location, morphology, size, and volume, so that they can be compared across repeated examinations.

3. Support for clinically significant incidental findings
   The guide does not focus only on suspected lung malignancy. It also supports structured capture of important incidental findings in the lungs, mediastinum, cardiovascular structures, breasts, liver, kidneys, bones, adrenal glands, and other visible anatomical regions when these findings require further action.

4. Separation of findings from recommendation logic
   Observations describe what is present in the images, while the conclusion and recommendation represent the next clinical step within the programme, such as routine recall, earlier surveillance imaging, or referral for specialist consultation.

5. Longitudinal comparability
   The data model supports repeated LDCT examinations over time, including comparison with previous studies and tracking of lesion evolution, which is essential in early lung cancer detection programmes.

6. Terminology-based interoperability
   The guide is designed to use internationally recognised terminologies and classifiers, especially SNOMED CT, and where relevant also LOINC, ICD-10-AM, and national classifiers, to ensure semantic consistency across systems.

Content of the Guide

This guide provides:

• FHIR profiles and extensions for the lung cancer early diagnosis workflow,
• structured representation of the pre-LDCT questionnaire,
• structured modelling of LDCT examination metadata and findings,
• support for lung finding categories and recommendation pathways,
• terminology bindings using SNOMED CT and other relevant code systems,
• structured example instances illustrating realistic programme scenarios,
• mappings from the national clinical dataset to interoperable FHIR artefacts,
• and identification of gaps and future development needs.

At the current stage, the guide focuses on the core data structures for LDCT-based early diagnosis, especially the questionnaire and radiology reporting dataset. Further refinement, terminology expansion, workflow alignment, 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 lung cancer early diagnosis data across systems and providers.
2. Data Quality: Improve the consistency, completeness, and reusability of questionnaire, imaging, and radiological data.
3. Clinical Utility: Support structured reporting, surveillance planning, referral decisions, and quality assurance.
4. Programme Monitoring: Enable population-level analysis of participation, findings, recommendations, and diagnostic pathways.
5. Longitudinal Care: Support follow-up across repeated LDCT examinations and subsequent clinical action.

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

Contributors

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