Project References and Traceability
🎯 Original Project Request
Analyze the Xt-EHR Imaging Report information model to identify which data elements are actually used in real-world imaging reports versus those that could be considered 'beyond basic' by comparing with real-world imaging reports from the PARROT dataset.
📚 Data Source References
🏛️ Xt-EHR Project
| Resource | Link | Description |
|---|---|---|
| Official Site | xt-ehr.eu | Xt-EHR Joint Action homepage |
| Model History | Version History | EHDS Logical Information Models releases |
| Current Version | v0.2.1 (Oct 10, 2025) | First preview version analyzed |
| GitHub Repository | Xt-EHR/xt-ehr-common | Source code and model definitions |
| Issues Tracker | GitHub Issues | Active model development discussions |
🔍 Specific Model References
| Model Component | Source File | Analysis Focus |
|---|---|---|
| Imaging Report | imagingReport.fsh | Complete report structure and elements |
| Imaging Study | imagingStudy.fsh | DICOM metadata and study organization |
📊 PARROT Project
| Resource | Link | Description |
|---|---|---|
| Dataset Repository | PARROT v1.0 | Multi-language radiology reports |
| Dataset Characteristics | 2,738 reports | 14 languages, 21 countries, 10 modalities |
| Research Purpose | Real-world evidence | Clinical usage pattern analysis |
🔗 Model Traceability Matrix
Element Classification Mapping
| Our Classification | Xt-EHR Model Path | Real-World Evidence | Implementation Priority |
|---|---|---|---|
| BASIC (11 elements) | header.subject, body.examinationReport.* |
90%+ clinical coverage | High - Core functionality |
| INTERMEDIATE (6 elements) | body.recommendation.*, body.comparisonStudy.* |
25-75% usage, high value | Medium - Enhanced workflows |
| BEYOND BASIC (31+ elements) | header.authorship.*, header.documentMetadata.* |
<25% usage, admin focus | Low - Specialized needs |
Direct Model References
Header Elements (Administrative)
// Source: EHDSImagingReport.header
* header.authorship 1..* Base "Report authoring details"
* author[x] ^short = "Author by whom the document was authored"
* datetime 1..1 dateTime "Date and time of last modification"
Body Elements (Clinical Content)
// Source: EHDSImagingReport.body.examinationReport
* examinationReport 1..1 Base "Examination report content"
* modality 1..* CodeableConcept "Imaging modality used"
* bodyPart 0..* EHDSBodyStructure "Body parts investigated"
* conclusion 1..1 Base "Clinical conclusion and impression"
📈 Analysis Process Validation
Methodology Verification
- Model Extraction: Direct parsing of FSH files from official repository
- Version Control: Specific to v0.2.1 release (October 10, 2025)
- Real-World Evidence: Quantitative analysis of PARROT dataset
- Cross-Validation: Mapping between model elements and usage patterns
Quality Assurance
- ✅ Source Verification: All references link to official repositories
- ✅ Version Tracking: Specific version numbers and release dates
- ✅ Reproducibility: Complete methodology documentation
- ✅ Traceability: Direct mapping from findings to source elements
🎯 Implementation Guidance
Phase 1: Basic Profile
Target: 11 Core Elements - Focus on essential clinical content - 90%+ real-world value coverage - Low implementation complexity
Phase 2: Enhanced Profile
Target: 6 Intermediate Elements - Workflow enhancement features - Use case driven implementation - Medium complexity integration
Phase 3: Comprehensive Profile
Target: 31+ Beyond Basic Elements - Administrative and technical completeness - Specialized institutional requirements - High complexity implementation
🇪🇺 EU AI Act Compliance References
AI Regulatory Framework
| Resource | Link | Purpose |
|---|---|---|
| EU AI Act (Full Text) | Regulation (EU) 2024/1689 | Primary legislation |
| EU AI Act Overview | European Commission AI Hub | Policy guidance and implementation |
| European Approach to AI | Excellence and Trust Framework | Strategic EU AI approach |
| Irish Implementation | Enterprise Ireland AI Act | National guidance and timeline |
| AI Act Service Desk | Single Information Platform | Interactive compliance tools |
AI Analysis Attribution
Model Used: Claude Sonnet 4.5 (Anthropic)
Classification: General-Purpose AI (GPAI) Model under EU AI Act Article 3(44) and (51)
Transparency Compliance: Article 52 - AI-generated content disclosure
Project Compliance Document: EU-AI-ACT-COMPLIANCE.md
Healthcare and EHDS Context
| Resource | Link | Purpose |
|---|---|---|
| European Health Data Space | EHDS Regulation | Healthcare data framework |
| GDPR | Regulation (EU) 2016/679 | Data protection compliance |
📋 Document Cross-References
| Document | Purpose | Key References |
|---|---|---|
| README.md | Project overview with EU AI Act compliance | Original prompt, methodology, AI attribution |
| EU AI Act Compliance | Regulatory compliance statement | Risk classification, transparency, GPAI obligations |
| Executive Summary | Key findings with AI attribution | Results, recommendations, compliance disclosure |
| Process Flow | Detailed methodology | Visual process diagram, validation steps |
| Element Analysis | Complete element inventory | Direct model references, usage patterns |
| PARROT Analysis | Dataset analysis with AI attribution | Real-world evidence, statistical analysis |
| Beyond Basic Classification | Specialized elements analysis | Low-usage elements, admin focus |
| PARROT Analysis | Real-world usage evidence | Quantitative statistics, coverage analysis |
| Executive Summary | Key findings and recommendations | Evidence-based guidance, implementation strategy |
🔄 Continuous Updates
This analysis maintains synchronization with: - Xt-EHR Model Evolution: Monitor repository for updates - PARROT Dataset Releases: Track new versions for expanded analysis - Implementation Feedback: Real-world deployment insights for classification refinement
This document ensures complete traceability between our analysis, source data models, and real-world evidence for evidence-based Xt-EHR implementation guidance.