The NHS holds vast amounts of patient data that could drive new treatments and improve public health, but a lack of public trust is blocking that potential. This project tackles a fundamental gap in current thinking. Bioethicists and policymakers have focused on fixing public distrust through better regulation or transparency, but they have not clearly defined what the public actually gets in return for sharing their health data. The term "public benefit" is used constantly in data governance debates, yet no one has pinned down what it means—whether it should be clinical improvements, economic gains, or scientific knowledge, and who should decide the priorities. If this research succeeds, it will give health data regulators and NHS leaders a concrete framework for defining and measuring public benefit. That could reshape how data-sharing programmes are designed, how consent forms are written, and how the public is told what their data achieves. The project compares England and Germany—the two countries that invented the dominant publicly funded healthcare models—so its findings could influence governance frameworks across Europe. The immediate output is conceptual clarity, not a new app or policy, but that clarity is a prerequisite for rebuilding the trust that makes data-driven medicine possible.
View original technical description
The use of health data held and controlled by public healthcare institutions (here forth health data) is vital for developing new healthcare technologies, improving health outcomes across populations, and supporting data industries and innovation. Lack of public trust, however, jeopardises this potential, leading to real costs and missed opportunities. In bioethics, a number of suggestions and recommendations have emerged as to how this seeming dissonance between public expectations and research and innovation ‘reality’ should be resolved in order to engender and maintain public trust. It has been argued, among others, that addressing it requires a focus on trustworthiness, reliability, or more robust regulation. This project moves beyond discussions about how to fix the problem of trust, to research into the underlying drivers of (dis)trust. It begins from the premise that public trust is warranted to public institutions tasked with benefiting the public. Despite attempts to define public benefit with respect to use of health data, the meaning, and application of the term remains unclear. Therefore, this project seeks to investigate, what constitutes public benefit in the context of health data use in the current digitalised era, and its relationship with public trust. It examines what benefits ought to be generated (e.g. economic benefits, epistemic benefits), for whom (e.g. public healthcare systems, patients, industrial sector), how these benefits should be assessed and prioritised (e.g. how should epistemic benefits be weighed against clinical benefits), and their relationship to public trust. To develop a conceptually robust and empirically grounded account of public benefit in the context of health data use and articulate its connection to public trust, this project combines philosophical methods with empirical analysis of comparative case-studies from England and Germany – the countries that invented the two most common publicly-funded healthcare models. The project has the following objectives: 1) Identify and describe the benefit(s) of health data 2) Analyse existing conceptions of public benefit in the context of health data research and innovation 3) Examine the nature and role of publicly funded healthcare systems in prioritising and promoting benefits 4) Construct and critically analyse an account of public benefit in the context of health data research and innovation 5) Examine and evaluate the concept of public trust towards public healthcare institutions that (also) promote non-health related benefits 6) Analyse existing health data governance frameworks and policies and articulate how they could be adapted to promote public benefit and secure public trust
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know