Xt-EHR T7.2 Sub-team for Imaging Reports Model Xt-EHR T7.2

Readme

Back
  • Choose Orientation
Back to Library
  • Choose Orientation

Xt-EHR T7.2 Sub-team for Imaging Reports Model Analysis

Deploy Status Analysis For Review

Original Project Request

Prompt: "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."

Specific Requirements:

  1. Extract all data elements from Xt-EHR Imaging Report and Study models
  2. Analyze real-world usage patterns from PARROT dataset
  3. Map real-world elements to Xt-EHR model elements
  4. Identify candidates for "beyond basic" classification
  5. Generate recommendations for basic vs. beyond basic categorization

Live Analysis Dashboard

The dashboard provides: - Interactive data element usage statistics - Real-world vs. model element mappings - Detailed analysis results and recommendations - Downloadable reports and visualizations

Overview

This project provides evidence-based analysis of the Xt-EHR Imaging Report information model by comparing theoretical model elements against real-world usage patterns from the PARROT dataset. Our goal is to identify which data elements are essential for core clinical workflows versus those serving specialized administrative or technical functions.

🤖 AI Analysis Attribution

In accordance with EU AI Act transparency requirements (Article 52):

AI-Assisted Analysis: The data analysis, pattern identification, and report compilation in this project were performed with the assistance of Claude Sonnet 4.5 (Anthropic), a large language model and General-Purpose AI system. All findings have been validated against source data from the PARROT v1.0 dataset and Xt-EHR model specifications, and are subject to expert review by the Xt-EHR T7.2 Sub-team.

Scope of AI Involvement: - Analysis of 2,738 real-world imaging reports from PARROT v1.0 dataset - Pattern recognition and element usage frequency calculations - Comparative mapping between real-world data and Xt-EHR model specifications - Classification recommendations (Basic, Intermediate, Beyond Basic categories) - Documentation synthesis and report generation

Human Oversight: All AI-generated analysis and recommendations undergo expert validation and are reviewed within the context of healthcare interoperability standards and clinical practice.

📖 Compliance Resources: - EU AI Act Compliance Statement - Full regulatory compliance documentation - AI Attribution Quick Reference - Attribution templates and FAQ

Analysis Methodology

🔄 Process Flow

graph TB
    subgraph "Data Sources"
        A[Xt-EHR FHIR IG v0.2.1<br/>EHDS Information Models]
        B[PARROT v1.0 Dataset<br/>2,738 Real Reports]
    end

    subgraph "Model Extraction"
        C[Extract Data Elements<br/>Header & Body Structure]
        D[Classify Element Types<br/>Required vs Optional]
    end

    subgraph "Real-World Analysis"
        E[Parse Report Content<br/>14 Languages, 21 Countries]
        F[Identify Usage Patterns<br/>Modality, Anatomy, Findings]
    end

    subgraph "Comparative Analysis"
        G[Map Real-World to Model<br/>Element Usage Frequency]
        H[Gap Analysis<br/>Used vs Unused Elements]
    end

    subgraph "Evidence-Based Classification"
        I[BASIC Elements<br/>11 Core Elements - 90%+ Value]
        J[INTERMEDIATE Elements<br/>6 Enhanced Workflow Elements]
        K[BEYOND BASIC Elements<br/>31+ Admin/Technical Elements]
    end

    A --> C
    A --> D
    B --> E
    B --> F
    C --> G
    D --> G
    E --> G
    F --> G
    G --> H
    H --> I
    H --> J
    H --> K

    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style I fill:#c8e6c9
    style J fill:#fff3e0
    style K fill:#ffebee

📊 Data Source References

Xt-EHR FHIR Implementation Guide

PARROT Dataset

🔗 Model Traceability

Our analysis directly references specific elements from the Xt-EHR models:

Model Section FHIR Path Analysis Coverage
Header Elements EHDSImagingReport.header.* Document metadata, authorship, recipients
Order Information EHDSImagingReport.body.orderInformation.* Service requests, clinical context
Examination Report EHDSImagingReport.body.examinationReport.* Modality, anatomy, procedures, findings
Supporting Info EHDSImagingReport.body.supportingInformation.* Clinical context, medications, devices
Study Metadata EHDSImagingStudy.* DICOM metadata, series information

Data Sources

🏛️ Xt-EHR Information Model

📊 PARROT Dataset v1.0

Project Structure

Key Findings

Based on comprehensive analysis of 2,738 real-world imaging reports against the Xt-EHR v0.2.1 model specification:

📈 Usage Statistics

🎯 Evidence-Based Classification

Category Element Count Clinical Coverage Implementation Complexity
BASIC 11 elements 90%+ clinical value Low - immediate interoperability
INTERMEDIATE 6 elements Enhanced workflows Medium - use case driven
BEYOND BASIC 31+ elements Administrative/technical High - specialized requirements

🔍 Detailed Mappings

Available in analysis documents with complete traceability to source models and real-world evidence.

Web Interface

A Flask web application provides interactive access to all analysis results:

cd flask_app
python app.py

Features: - Document library with search and categorization - Mobile-first responsive design
- PDF export with selectable orientations - Real-time analysis dashboard

See flask_app/README.md for detailed setup and deployment instructions.

Implementation Strategy

Target: Core 11 elements for immediate clinical value - Complexity: Low implementation burden - Coverage: 90%+ of real-world clinical needs
- ROI: Very high - maximum value with minimal effort

🔧 Phase 2: Enhanced Profile (Use Case Driven)

Target: Additional 6 intermediate elements - Complexity: Medium - specific workflow integration - Coverage: Enhanced clinical context and workflows - ROI: Medium-High - targeted value for specific use cases

🏢 Phase 3: Comprehensive Profile (Enterprise/Regulatory)

Target: Full model implementation including beyond basic elements - Complexity: High - complete administrative and technical infrastructure - Coverage: Full workflow support and regulatory compliance - ROI: Low-Medium - justified only for specialized institutional needs

Regulatory Compliance

🇪🇺 EU AI Act Compliance

This project operates in accordance with the European Union Artificial Intelligence Act (Regulation EU 2024/1689), which establishes harmonized rules for trustworthy AI in Europe.

Classification: Limited Risk (Transparency Requirements) - AI-assisted analysis for healthcare data model evaluation - Transparency obligations fulfilled through clear AI attribution - Subject to human oversight and expert validation

Key Resources: - 📋 EU AI Act - European Commission - 🇮🇪 Irish Implementation - Enterprise Ireland - 🏛️ European Approach to AI - 📖 Project Compliance Statement - Detailed compliance documentation

Timeline Context: - AI Act entered into force: 2 August 2024 - Transparency requirements (Article 52): In effect - GPAI obligations: 2 August 2025 - Full application: 2 August 2026

This project aligns with the European Health Data Space (EHDS) initiative and supports trustworthy AI principles in healthcare interoperability.

Acknowledgments

This project builds upon the work of several important initiatives:

🏛️ Xt-EHR Project

📊 PARROT Project

🤖 AI Analysis Tools

🔗 Model Provenance

Our analysis maintains full traceability to source materials: - Xt-EHR Elements: Direct references to FSH model definitions - Real-World Evidence: Quantitative analysis of PARROT dataset usage patterns - AI-Assisted Classification: Evidence-based justification validated by domain experts - Regulatory Compliance: Aligned with EU AI Act and EHDS frameworks

We gratefully acknowledge the contributions of all projects and tools in enabling this comparative analysis and advancing standardized, trustworthy health data exchange.

License

This project is part of the Xt-EHR T7.2 Sub-team analysis work.

Development Environment

Prerequisites

Quick Start

# Clone and setup
git clone <repository-url>
cd "FHIR Imaging Report"

# For web app setup, see flask_app/README.md

Team

Xt-EHR T7.2 Sub-team for Imaging Reports Model

Quick Actions
Back to Document Library
  • Choose Orientation