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Summary
This summary is machine-generated.

Synthetic data generation (SDG) offers privacy-preserving health data reuse but faces adoption barriers. Trustworthy SDG requires addressing data quality, governance, and stakeholder engagement beyond technical fixes.

Keywords:
data quality dimensionshealth datahealth data qualityprivacyprojectsynthetic data generation

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Area of Science:

  • Health Informatics
  • Data Science
  • Medical Research

Background:

  • Synthetic data generation (SDG) is promoted for privacy-preserving health data access and reuse.
  • Despite technical advances, real-world adoption of synthetic health data remains limited.
  • Challenges include data quality, representativeness, infrastructure, trust, and legal uncertainty.

Purpose of the Study:

  • To reflect on the practical operationalization of SDG in European health research.
  • To synthesize insights from 7 European research initiatives within the HealthData4EU cluster.
  • To identify methodological and governance tensions impacting trust and responsible use.

Main Methods:

  • Analysis of experiences from 7 European research initiatives.
  • Synthesis of cross-project insights on SDG operationalization.
  • Examination of implications for trust and responsible use of synthetic health data.

Main Results:

  • Recurring tensions exist between methodological approaches and governance frameworks in SDG.
  • Technical optimization alone is insufficient for achieving trustworthy synthetic health data.
  • Alignment is needed between evaluation, data stewardship, regulatory clarity, and stakeholder engagement.

Conclusions:

  • Trustworthy synthetic health data requires a holistic approach beyond technical solutions.
  • Addressing practical challenges is crucial for integrating SDG into European health research ecosystems.
  • Sustained stakeholder engagement and regulatory clarity are essential for credible SDG adoption.