Federated health data networks (FHDNs) have emerged as an attractive solution to ‘freeing’ health data enabling this data to stay at the institutions they are collected at whilst allowing partnering institutions send queries and algorithms to run on the data. Although FHDNs may be a solution to the data silos and privacy concerns in healthcare and additionally enable federated learning and thus greater clinical and population studies, the establishment and operation of such networks can present numerous challenges.
In order to identify these challenges, needs and opportunities of FHDNs, DNV’s Healthcare research programme hosted a workshop bringing together global FHDN pioneers at the forefront of establishing and operating FHDNs. We have now published a whitepaper that summarises the main discussion of the workshop.
This whitepaper summarises the main discussion of the workshop.
The workshop identified eight main challenges when establishing, operating, and expanding a FHDN, ranked in order of importance and visualised in the figure above.
Trust emerged as the most important enabler in all phases – establishment, operation and expansion – of a FHDN. DNV, as an independent third-party assurance and risk management company, continuously explores opportunities for continual value creation through the assurance and enabling of trust in emerging technologies.
By investigating the identified challenges and approaches in a FHDN, we aim to better understand how our independent role can enable trust between partners by supporting them, among others with data harmonisation, data governance and management, orchestration of the FHDN and federated learning, as well as other emerging roles.
If you are interested in learning more or collaborating with us, please get in touch.
DNV is an independent foundation with over 150 years of history in assurance and risk management. We use our knowledge to advance safety and performance, set industry benchmarks, and inspire and invent solutions to tackle global transformations. DNV’s Healthcare research programme works with partners through large-scale public-private research and innovation projects (see BigMed, the Nordic Alliance for Clinical Genomics, REALMENT and AI-Mind to name a few) to assess trustworthy AI adoption, enable data sharing opportunities through federated data networks, meet patient autonomy needs with dynamic consent, explore quality driven benchmarking approaches in clinical genomics and assure the safe bringing-to-market of new AI medical device software solutions.