User Guide Overview

EHDSLens is organized into several modules, each serving a specific purpose in analyzing the EHDS systematic literature review.

Architecture

ehdslens/
├── core.py          # EHDSAnalyzer - main interface
├── data.py          # Study, StudyDatabase, enums
├── analysis.py      # ThematicAnalyzer, QualityAssessor, GRADECERQual
├── visualization.py # EHDSVisualizer - charts and diagrams
├── export.py        # ReportGenerator - reports and bibliographies
└── cli.py           # Command line interface

Core Components

EHDSAnalyzer

The main entry point providing a unified interface to all functionality:

from ehdslens import EHDSAnalyzer

analyzer = EHDSAnalyzer()
analyzer.load_default_data()

StudyDatabase

Container for the 52 studies with filtering and search capabilities:

from ehdslens import StudyDatabase

db = analyzer.db
print(f"Contains {len(db)} studies")

Study Dataclass

Individual study representation with rich metadata:

study = db.get_study(1)
print(study.authors)
print(study.title)
print(study.primary_axis)
print(study.quality_rating)

Thematic Axes

The five thematic axes from the systematic review:

Axis	Description
`GOVERNANCE_RIGHTS_ETHICS`	Data governance, patient rights, ethical frameworks
`SECONDARY_USE_PETS`	Privacy-enhancing technologies, data access policies
`NATIONAL_IMPLEMENTATION`	Member state transposition, regulatory harmonization
`CITIZEN_ENGAGEMENT`	Public trust, participation, health literacy
`FEDERATED_LEARNING_AI`	Distributed analytics, AI governance

Quality Ratings

MMAT-based quality assessment:

Rating	Criteria Met
`HIGH`	4-5 of 5
`MODERATE`	3 of 5
`LOW`	1-2 of 5
`NOT_APPLICABLE`	Policy documents

Workflow Example

from ehdslens import EHDSAnalyzer
from ehdslens.data import ThematicAxis, QualityRating

# 1. Load data
analyzer = EHDSAnalyzer()
analyzer.load_default_data()

# 2. Explore statistics
stats = analyzer.get_statistics()

# 3. Filter relevant studies
studies = analyzer.filter_studies(
    axis=ThematicAxis.FEDERATED_LEARNING_AI,
    min_quality=QualityRating.MODERATE
)

# 4. Analyze themes
analysis = analyzer.analyze_axis(ThematicAxis.FEDERATED_LEARNING_AI)

# 5. Get evidence confidence
findings = analyzer.get_grade_cerqual_summary()

# 6. Export results
from ehdslens.export import ReportGenerator
reporter = ReportGenerator(analyzer.db)
reporter.save_markdown_report("report.md")