Having equal opportunity to use digital in all its forms to maintain health and lead a full life in older age can have obstacles. Broadband access is still variable by geography with, for example, some rural areas with poor connection. Cost can be a barrier on limited budgets when digital is competing with heating. But many older people lack skills and confidence in adopting technologies that they did not grow up with. Digital designers often lack insight into the needs of older people, particularly those with limitations of vision, hearing or touch. Ability to filter key information from the ‘noise’ of the Internet is difficult for all and may become more so as people age.This presentation will describe various initiatives to address digital health equity for older people.This includes a focus on co-design with older people. The higher use of tablet computers by older people is well known and easily explained. Some of our work in robotics showed age-related differences such as preference for voice among older and younger while middle-aged people preferred keyboard interaction. Other work with robot-pets showed older people wanting life-like pets while younger roboticists favoured more mythical appearance.In our GOALD (Generating Older Active Lives Digitally) project we are setting up inter-generational co-design groups to consider how digital can be used for various purposes. For example, how can virtual reality be used in care homes to give residents the chance to ‘visit’ places they can no longer physically access, and perhaps exercise while they do so? How can family carers use linked smart speakers to be assured of their loved ones safety and stay in contact without being intrusive?We have been working in a project called EPIC (eHealth Productivity and Innovation in Cornwall) with small companies to develop the eHealth ecosystem in Cornwall. Although this is across the age range, because of the demography of Cornwall there has been a focus on older age. So the co-design questions above are collaborations with small companies where not only do we want to develop new products and services we want to use that in the ‘levelling up’ of Cornwall.Addressing digital health equity has included the use of digital health champions and volunteers. For example, volunteers in Plymouth SeniorNet helped older people get online with impact on their mental health probably through family and social connections. We have tried to promote digital health professionalism among nursing students and for them to become digital health champions supporting, for example, the use of smart speakers in care homes.The UK still has poor access and use of the medical record directly by patients. Other countries do this better. Studies we completed twenty years ago showed the benefits of using the medical record to tailor and filter information for patients. This is particularly useful for older patients and needs more attention.Finally, although this is more of a national or supra national issue (e.g. through EU supported ‘Superfast Cornwall’ broadband roll out), ensuring equitable internet access can be addressed (still) through the use of kiosks as well as local hubs including ‘roof top’ local area wifi to help with lack of infrastructure as well as economic barriers.

Digital Health Equity for Older People

Medicine stands apart from other areas where machine learning can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of the data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. Think Covid 19! In this talk I will show how machine learning is transforming medicine and how medicine is driving new advances in machine learning, including new methodologies in time-series, causal inference, interpretable and explainable machine learning, as well as the development of new machine learning areas - quantitative epistemology.

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