Elizabeth Ford, Andy Boyd, Juliana K.F. Bowles, Alys Havard, Robert W. Aldridge, Vasa Curcin, Michelle Greiver, Katie Harron, Vittal Katikireddi, Sarah E. Rodgers and Matthew Sperrin

Learning Health Systems (2019) 3(3): e10191


The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well‐being.

However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency.

We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team‐based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.