The Social Data Foundation model: Facilitating health and social care transformation through datatrust services

Personal data store ecosystems in health and social care

This paper considers how the development of personal data store ecosystems in health and social care may offer one person-centered approach to improving the ways in which individual generated and gathered data-e.g., from wearables and other personal monitoring and tracking devices-can be used for wellbeing, individual care, and research. Personal data stores aim to provide safe and secure digital spaces that enable people to self-manage, use, and share personal data with others in a way that aligns with their individual needs and preferences. A key motivation for personal data stores is to give an individual more access and meaningful control over their personal data, and greater visibility over how it is used by others. This commentary discusses meanings and motivations behind the personal data store concept-examples are provided to illustrate the opportunities such ecosystems can offer in health and social care, and associated research and implementation challenges are also examined.

Machine learning models, trusted research environments and UK health data: ensuring a safe and beneficial future for AI development in healthcare

Digitalisation of health and the use of health data in artificial intelligence, and machine learning (ML), including for applications that will then in turn be used in healthcare are major themes permeating current UK and other countries' healthcare systems and policies. Obtaining rich and representative data is key for robust ML development, and UK health data sets are particularly attractive sources for this. However, ensuring that such research and development is in the public interest, produces public benefit and preserves privacy are key challenges. Trusted research environments (TREs) are positioned as a way of balancing the diverging interests in healthcare data research with privacy and public benefit. Using TRE data to train ML models presents various challenges to the balance previously struck between these societal interests, which have hitherto not been discussed in the literature. These challenges include the possibility of personal data being disclosed in ML models, the dynamic nature of ML models and how public benefit may be (re)conceived in this context. For ML research to be facilitated using UK health data, TREs and others involved in the UK health data policy ecosystem need to be aware of these issues and work to address them in order to continue to ensure a 'safe' health and care data environment that truly serves the public.

Privacy-by-Design Environments for Large-Scale Health Research and Federated Learning from Data

This article offers a brief overview of 'privacy-by-design (or data-protection-by-design) research environments', namely Trusted Research Environments (TREs, most commonly used in the United Kingdom) and Personal Health Trains (PHTs, most commonly used in mainland Europe). These secure environments are designed to enable the safe analysis of multiple, linked (and often big) data sources, including sensitive personal data and data owned by, and distributed across, different institutions. They take data protection and privacy requirements into account from the very start (conception phase, during system design) rather than as an afterthought or 'patch' implemented at a later stage on top of an existing environment. TREs and PHTs are becoming increasingly important for conducting large-scale privacy-preserving health research and for enabling federated learning and discoveries from big healthcare datasets. The paper also presents select examples of successful TRE and PHT implementations and of large-scale studies that used them.