CS Health Data
Sensitive data federation analysis model in population health
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 realisation of that ambition. Performing GDPR compliant research will be entirely dependent on solving the trusted data federation challenge.
This Case Study will explore how innovative technology can change the current methods in care and improve the crucial influence of healthy citizens and patients in an advanced globally interoperable health data system. The resulting technology will be relevant for future initiatives that need to reuse sensitive data of individuals. The current limited or non-existent level of reuse of critical data on infection, viral spread and post-vaccination as well as long term effects related to COVID-19 will severely hamper preparedness for future SARS-CoV-2 related problems, including the emergence of new variants. Reuse of sensitive RWO is of much wider use than just for COVID-19. Hence this Case Study aims to greatly contribute to the generic abilities of the global society to tackle future health issues.
Co-chairs
Lei LIU, University of Fudan
Barend Mons, CODATA, GO FAIR and LUMC
Lauren Maxwell, Heidelberg University
Francisca Oladipo, Federal University Lokoja
Secretariat Contacts
Simon Hodson, CODATA
Fenghong LIU, CAS
Hana Pergl, CODATA