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EHDSLens

A Python toolkit for analyzing the European Health Data Space (EHDS) systematic literature review

PyPI version Python 3.9+ License: MIT CI

Overview

EHDSLens provides programmatic access to the dataset and analytical tools from the systematic literature review:

"European Health Data Space (EHDS) Regulation (EU) 2025/327: A Systematic Review of Implementation Challenges and Opportunities"

The toolkit enables researchers, policymakers, and practitioners to:

  • 📊 Explore 52 peer-reviewed studies and grey literature
  • 🔍 Search & Filter by thematic axis, quality rating, year, and more
  • 📈 Visualize publication trends, quality distributions, and thematic coverage
  • 📝 Export bibliographies (BibTeX, RIS, APA, Vancouver) and reports
  • 🎯 Analyze GRADE-CERQual confidence assessments

Quick Example

from ehdslens import EHDSAnalyzer

# Initialize and load the 52-study database
analyzer = EHDSAnalyzer()
analyzer.load_default_data()

# Get statistics
stats = analyzer.get_statistics()
print(f"Total studies: {stats['total']}")

# Search for federated learning studies
fl_studies = analyzer.search_studies("federated learning")
print(f"Found {len(fl_studies)} studies about federated learning")

# Get GRADE-CERQual findings
findings = analyzer.get_grade_cerqual_summary()
for f in findings:
    print(f"{f['confidence'].upper()}: {f['finding']}")

Installation

pip install ehdslens

Or with visualization support:

pip install ehdslens[viz]

Features

Five Thematic Axes

The systematic review organizes findings across five key domains:

Axis Focus Area
Governance, Rights & Ethics Data governance frameworks, patient rights, ethical considerations
Secondary Use & PETs Privacy-enhancing technologies, anonymization, data access
National Implementation Member state transposition, regulatory challenges
Citizen Engagement Public trust, participation, health literacy
Federated Learning & AI Distributed analytics, AI governance, interoperability

Quality Assessment

All studies are assessed using the Mixed Methods Appraisal Tool (MMAT):

  • 🟢 High Quality: 4-5 criteria met
  • 🟡 Moderate Quality: 3 criteria met
  • 🟠 Low Quality: 1-2 criteria met
  • ⚪ N/A: Policy documents (different criteria)

GRADE-CERQual

Confidence in findings assessed using GRADE-CERQual methodology:

  • Methodological limitations
  • Coherence
  • Adequacy of data
  • Relevance

Command Line Interface

# Show statistics
ehdslens stats

# Analyze a thematic axis
ehdslens analyze governance

# Search studies
ehdslens search "privacy"

# Export bibliography
ehdslens export --format bibtex -o references.bib

# Show GRADE-CERQual findings
ehdslens grade

Documentation

Citation

If you use EHDSLens in your research, please cite:

@article{liberti2025ehds,
  title={European Health Data Space (EHDS) Regulation (EU) 2025/327:
         A Systematic Review of Implementation Challenges and Opportunities},
  author={Liberti, Fabio},
  journal={[Journal Name]},
  year={2025},
  doi={[DOI]}
}

License

MIT License - see LICENSE for details.