Understanding the care.data conundrum: new information flows for economic growth

The analysis of data from electronic health records aspires to facilitate healthcare efficiencies and biomedical innovation. There are also ethical, legal and social implications from the handling of sensitive patient information. The paper explores the concerns, expectations and implications of the National Health Service (NHS) England care.data programme: a national data sharing initiative of linked electronic health records for healthcare and other research purposes. Using Nissenbaum’s contextual integrity of privacy framework through a critical science and technology studies (STS) lens, it examines the way technologies and policies are developed to promote sustainability, governance and economic growth as the de facto social values, while reducing privacy to an individualistic preference. The state, acting as a new, central data broker reappropriates public ownership rights and establishes those information flows and transmission principles that facilitate the assetisation of NHS datasets for the knowledge economy. Various actors and processes from other contexts attempt to erode the public healthcare sector and privilege new information recipients. However, such data sharing initiatives in healthcare will be resisted if we continue to focus only on the monetary and scientific values of these datasets and keep ignoring their equally important social and ethical values

The knowledge-space dynamic in the UK bioeconomy

The loss of manufacturing employment to lower cost economies has meant that countries like the UK have sought to promote innovation in areas such as biotechnology. The emergence of the 'bioeconomy', however, has been highly uneven, with concentrations of activity in certain countries and particular regions in those countries. In the UK, for example, there are four major concentrations of the bioeconomy. Each of these concentrations exhibit distinct patterns of knowledge and spatial inputs into the innovation process, meaning that it is important to consider the knowledge-space dynamic in and of each region

How long does biomedical research take? Studying the time taken between biomedical and health research and its translation into products, policy and practice

This article has been made available through the Brunel Open Access Publishing Fund.Background: The time taken, or ‘time lags’, between biomedical/health research and its translation into health improvements is receiving growing attention. Reducing time lags should increase rates of return to such research. However, ways to measure time lags are under-developed, with little attention on where time lags arise within overall timelines. The process marker model has been proposed as a better way forward than the current focus on an increasingly complex series of translation ‘gaps’. Starting from that model, we aimed to develop better methods to measure and understand time lags and develop ways to identify policy options and produce recommendations for future studies. Methods: Following reviews of the literature on time lags and of relevant policy documents, we developed a new approach to conduct case studies of time lags. We built on the process marker model, including developing a matrix with a series of overlapping tracks to allow us to present and measure elements within any overall time lag. We identified a reduced number of key markers or calibration points and tested our new approach in seven case studies of research leading to interventions in cardiovascular disease and mental health. Finally, we analysed the data to address our study’s key aims. Results: The literature review illustrated the lack of agreement on starting points for measuring time lags. We mapped points from policy documents onto our matrix and thus highlighted key areas of concern, for example around delays before new therapies become widely available. Our seven completed case studies demonstrate we have made considerable progress in developing methods to measure and understand time lags. The matrix of overlapping tracks of activity in the research and implementation processes facilitated analysis of time lags along each track, and at the cross-over points where the next track started. We identified some factors that speed up translation through the actions of companies, researchers, funders, policymakers, and regulators. Recommendations for further work are built on progress made, limitations identified and revised terminology. Conclusions: Our advances identify complexities, provide a firm basis for further methodological work along and between tracks, and begin to indicate potential ways of reducing lags