Privacy-Preserving Solutions for Real-World Data (RWD) Access and Collaboration: Challenges & Opportunities

Background: The demand for geographically diverse, high-quality, fit-for-purpose, analysis-ready RWD sources has been increasing to support healthcare decision making. However, data privacy has been a long-standing challenge for RWD sharing and evidence generation across sites, regions, and countries. While different solutions can mitigate the issue (e.g., synthetic data, anonymized or limited data sets, federated data sharing), heterogenous regulations, guidelines, and study needs drive the need for understanding the right toolset for each application.

Objectives: • Provide an overview of privacy-preserving approaches and discuss synthetic-data use cases in real-world evidence across sectors
• Discuss methodological challenges and elaborate upon future development needs

Description: A panel of experts representing academia, healthcare IT, life sciences, and regulatory perspectives will engage with researchers interested in privacy-preserving approaches and emerging methods such as synthetic data. This panel will provide cross-sector experiences and discuss future development needs.

A. Moderator: E. Wang will provide an overview of different privacy-preserving technologies and introduce the panelists. (5 min)

B. Data and analytics platform: J. Rassen will discuss privacy issues and use of data across geographies, including opportunities and challenges. He will provide the perspective of the data platform and data users. (10 min)

C. Synthetic data generation platform: L. Mosquera will provide an overview of a healthcare synthetic-data application and how it compares with other privacy-preserving technologies. She will discuss how the technology is applied in different settings with data-sharing opportunities. (10 min)

D. Life science industry: M. Burcu will discuss his organization’s experience with privacy-preserving technologies and summarize a recently completed synthetic data validation study in vaccine effectiveness. He will discuss methodological challenges and development needs for adoption and acceptability. (10 min)

E. Academia: R. Foraker will discuss her organization’s experience in using privacy-preserving data sharing approaches for research and training. She will discuss lessons learned from the National COVID Cohort Collaborative (N3C) project that involved privacy-preserving data sharing from multiple sites in the US during the pandemic. (10 min)

F. Regulatory - EMA: D. Morales will discuss EMA’s initiatives and perspective related to high-fidelity synthetic data to support regulatory decisions. (10 min)

G. Government organization: A panelist on behalf of P. Myles will discuss the ongoing work of the MHRA on high-fidelity synthetic data applications that could promote innovation in the fields of AI as a Medical Device (AIaMD) development and clinical trials methodology (10 min)

H. Panel discussion: Lessons learned and next steps in development (20 min)

I. Closing remarks (5 min)

Acknowledgement for non-presenting authors: Dr. Puja Myles, Director, Clinical Practice Research Datalink (CPRD), Medicines & Healthcare products Regulatory Agency (MHRA), UK; and Dr. Jon D. Morrow, Clinical Associate Professor of Obstetrics & Gynecology, NYU School of Medicine, USA. Dr. Morrow was an employee of MDClone, Inc., when this abstract was submitted.