Dossier-Graph: A Knowledge Graph for German HTA reports
06.08.2025
Project summary
Dossier-Graph is an open-source and free knowledge graph system designed for research and education, built on public data from transparent health technology assessments (IQWiG 2023).
Its mission: transform a vast archive of dossier evaluations into a live, searchable database using AI.
Key Features
Flexible, Traceable Linking:
Connects dossiers, addenda, tables, sections, and more complex conceptual relationships.
Ensures both human-readable and machine-consumable navigation paths.
AI-Driven Search & Analysis:
Automates extraction, semantic search, and interactive analysis.
Users ask questions in natural language and receive comprehensive, cross-document answers instantly.
Why it matters
Clinical development of new medicines differs from many business models because it relies critically on voluntary, even altruistic (Jansen 2009) research participation and hence on public trust. “Information gain per unit of accepted research burden” is a useful concept introduced to think about how regulatory oversight learns from past clinical R&D efforts – a prerequisite of earning public trust. Information is gained not only from conducting clinical research but throughout the entire regulatory process, including post-marketing health technology assessment. Therefore, research responsibilities to maximize available information extend well beyond publishing study results.
In this spirit, the Dossier-Graph knowledge graph is designed to transform post-marketing health technology assessment data into actionable insights, honoring the contributions of the many patients involved in the underlying research.
By integrating state-of-the-art information technology throughout the drug development and regulatory lifecycle, this project advances the actionable use of critical research information.
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References
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