Title: AIM Research Support Facility
Funder: Department of Health and Social Care

Abstract: This proposal brings together the expertise and experience from two national institutes, The Alan Turing Institute and Health Data Research UK (HDRUK), to establish a research support facility (RSF) for the NIHR AI for Multiple Long Term Conditions (Multimorbidity; MLTC-M) programme. It benefits from a combination of expertise in data science methods and health data research; achieved through a collaboration between two of Turing s strategic research programmes, Tools Practices & Systems and Health & Medical Sciences, led respectively by Dr Kirstie Whitaker and Professor Chris Holmes. The RSF will use its convening power and expertise to maximise the scientific impact and potential for patient benefit. This will be focussed around five themes: Robust, secure, reproducible infrastructure. Cloud services are often not certified for sensitive health data and many trusted research environments do not support version control tools for data, software and computational environments. With expertise from the Secure eResearch Platform (SeRP) team we will enable AIM researchers to deliver analyses that can be independently verified and extended. Accessible research-ready data. The RSF team have specialised data engineering expertise to link data from multiple sources, improve data quality and deliver validated, interoperable and reusable data sets to enhance the impact of the MLTC-M research community. Scientific community building and training. We will bring consortia researchers together and spark the interest and excitement of the wider AI community. We will host regular seminars, two international conferences and two collaborative hackathons to inspire data scientists to explore exciting and challenging problems within AI for MLTC-M. Public and patient involvement and engagement (PPI/E). We will build a network connecting PPI/E teams across the AIM Research Collaborations, and stimulate a wider discussion on the opportunities of AI in health research and how an engaged public/patient population can improve the science, AI algorithms, and UK well-being. Sustainability and legacy. We will open source code and protocols, even for sensitive data that can not be made public, following &quot;The Turing Way&quot;, a handbook written to make reproducible research &quot;too easy not to do&quot; (https://the-turing-way.netlify.com). We will ensure the investment provides exceptional value for money and longevity in its impact. To achieve this the RSF will run under three overlapping phases Co-design (months 1-3): Working with funded researchers to build a shared vision of the RSF including ways of working and key deliverables. Delivery (months 4-36): Providing community, data, infrastructure and training support for robust, reproducible science with safe and ethical AI led by a vibrant cross-consortia PPI/E team. Legacy (months 4-40): Working from the start to ensure the long term impact of the funding and provide a sustainable foundation for a growing community of MLTC-M researchers. Each theme will be led by a designated Theme Leader, a national expert in their area of data science, together with an early career researcher co-lead. Themes will organise the structure of work and activities in partnership with the funded Research Collaborations to deliver research that is &quot;greater than the sum of its parts&quot; and embeds open, inclusive, ethical values throughout.

Lay abstract: Many people suffer from multiple health conditions, particularly as they get older or are from less affluent areas. Most of the evidence base for medical care is about treating single conditions and we know surprisingly little about identifying and treating multiple long-term conditions (multimorbidity) or MLTCs. Recently, this has become a strategic priority for the UK s National Health Service and research funders. Research funders have come together to invest at least &pound;23M into research to better understand and treat MLTCs. At least four research consortia will be supported by a dedicated Research Support Facility (RSF). Our bid is to create such an RSF. It will be created to ensure that the funded research projects will work together efficiently to answer questions relevant to patients and the general public using cutting edge analytical tools. These tools and methods are known as Artificial Intelligence. We will support the use of Artificial Intelligence (AI) for Multiple Long-Term Conditions (AIM) through the RSF to minimise administrative costs of research and support data access and analysis. This in turn will speed up research and its uptake and impact. This mode of working is novel, but has been developed and refined through The Alan Turing Institute, the UK s National Institute for Data Science and AI and published as &ldquo;The Turing Way&rdquo;. The AIM programme represents an opportunity to pilot specific aspects of support in collaboration with partners already active in the field, including the Health Data Research UK (HDRUK) Innovation Gateway which helps to find data and the UK Secure eResearch Platform (SeRP) to help with analysis. SeRP is what is known as a trusted research environment (TRE). TREs provide researchers with access to de-identified data to protect the privacy of research participants and the general public. The RSF will use its convening power and expertise to maximise the scientific impact and potential for patient benefit through focus on five themes. Each theme is led by a national expert in key areas of data science and their contributions are structured to offer both constructive challenge and expert resources to the funded Research Collaborations to deliver research that is &quot;greater than the sum of its parts&quot; and embeds open, inclusive, ethical values throughout. The five themes are: Reproducible, secure and interoperable infrastructure Accessible, research-ready data Scientific community building and training Public and patient involvement and engagement (PPI/E) Sustainability and legacy

Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE