Introduction

Reusing Real World Observations (RWO) and health data for research, health innovation and policy is key to better health in general, pandemic preparedness and imminent cost savings. However, the generally accepted notion that ‘citizens should be in control of the reuse of their personal data’ remains a paper mantra unless we design and implement a user friendly, trusted and sustainable environment that allows the realization of that ambition. Performing GDPR compliant research will be entirely dependent on solving the trusted data federation challenge.

We seek projects and initiatives working on population health data, clinical data, and genetic data to participate in this Case Study.

Challenges

Better use of data and evidence based personalized medicine could potentially save society hundreds of billions of Euros each year. Successful and reusable components of a system enabling this societal health benefit and cost saving can be reused. Many public organizations and private companies have indicated keen interest in a GDPR compliant access to a real world, citizen and patient-controlled system that can help them to learn from the data. The Case Study will adhere to joint principles of open protocols and standards, around which multiple vendor solutions can be operational to create a distributed, safe and scalable environment. Reuse can be for any purpose for which consent of the citizen is pre-recorded in the system, and can range from improved care outcome studies, to outbreak management, to vaccine follow up to drug rationalization and virtual clinical trials or post marketing surveillance by the pharmaceutical industry.

Engagement  

Controlled and globally distributed access to sensitive data is a very important issue for social and health sciences, particularly when topics with global dimensions. The Case Study will seek to demonstrate how to make better steps to make available those parts of the data which can be made shared.  Commonly agreed FAIR implementation profiles will be created and FAIR data points established in various ‘GOSC’ regions. The feasibility for distributed analytics over datasets held in various regions will then be explored and demonstrated. Community accepted standards will be used throughout and only if needed new choices and challenges in terms of standards and technology will be addressed.

Deliverables

1. A FAIR-based system with optimal scaling potential and no vendor lock in, entirely based on FAIR Digital Objects.
2. Fully distributed and GDPR compliant analytics and learning with full respect for and actual involvement of the citizen.
3. FAIR Data Points in a number of locations, with synthetic (and if possible real-world data) to demonstrate cross-regional-OSC re-use of sensitive data for analytical purposes.