A Case for Synthetic Data in Regulatory Decision-Making in Europe.

Area of Science:

• Health data science
• Regulatory science
• Data privacy

Background:

• European Union faces significant challenges in health data usage, including privacy, fragmentation, validity, and generalizability.
• Strict regulations, high costs, and time constraints hinder patient data sharing in Europe, delaying evidence generation and regulatory approvals.
• Synthetic data, artificially generated, is emerging as a proxy for real-world data (RWD) in healthcare research.

Purpose of the Study:

• To review the potential merits and challenges of synthetic data for decision-making within the European regulatory environment.
• To explore current applications and ongoing initiatives involving synthetic data in healthcare.
• To assess the value of synthetic data for regulatory purposes and identify barriers to its adoption.

Main Methods:

• Literature review of synthetic data applications in healthcare research.
• Analysis of regulatory landscapes and initiatives in the European Union concerning data usage.
• Examination of barriers and opportunities for synthetic data adoption in European healthcare.

Main Results:

• Synthetic data presents innovative solutions to overcome privacy and regulatory obstacles in European health data usage.
• Despite growing interest and initiatives, realistic barriers impede the full adoption of synthetic data by European researchers and regulators.
• The increasing use of RWD and synthetic data necessitates a regulatory framework for evidence generation and decision-making.

Conclusions:

• Synthetic data holds significant promise for advancing healthcare research and regulatory processes in Europe.
• Addressing practical challenges is crucial for the successful integration of synthetic data into European regulatory frameworks.
• Continued exploration of synthetic data is vital given advancements in AI, machine learning, and data generation.