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Artificial intelligence to understand clusters of multiple long-term conditions: An NIHR priority

Published: 06 July 2020

NIHR is launching a new funding call to harness the potential of artificial intelligence to meet the challenge of multiple long-term conditions (multimorbidity). Shakti Patel, Research Programme Manager at the Department of Health and Social Care, explains how NIHR and DHSC have consulted with the AI research community to ensure this call will deliver high quality and ambitious research.

A substantial and growing number of people around the world suffer from two or more long-term conditions, referred to as multiple long-term conditions or multimorbidity (MLTC-M). Having multiple long-term conditions affects quality of life, leads to poorer health outcomes and experiences of care, and accounts for disproportionate healthcare workload and costs. Professor Chris Whitty, Chief Medical Officer for England and joint head of NIHR, has written about the importance of MLTC-M as a research priority in the journal Nature.

Diseases often cluster together due to common risk factors, rather than chance. Understanding and mapping the clusters of conditions that occur more frequently in the population than we might expect is crucial for tackling the problem of MLTC-M. Advances in artificial intelligence, machine learning and statistical methods offer huge potential to identify and understand more about trajectories in the development of clusters of disease. It will enable us to uncover new mechanisms for disease, to develop treatments, and to reconfigure services to better meet patients’ needs. 

The AIM call

The new £23m Artificial Intelligence for Multiple Long-Term Conditions funding call (AIM) will harness the potential of artificial intelligence (AI) to meet the challenge of MLCT-M, and is part of the NHSX AI Lab funding announced by the Prime Minister in 2019. This call will bring together advanced data science and AI researchers, with health and social care communities to work together in Research Collaborations to tackle this problem.

The call will offer two types of funding: awards of up to £5m for Research Collaborations, and Development Awards of up to £120k. 

In recognition of the fact that MLTC-M has been put in the ‘too difficult’ box for too long, the AIM call will also include the development of a £3m Research Support Facility (RSF) for successful applicants, to help overcome barriers to research and build a community of researchers in this important field.

What the research community told us

The application of AI techniques to health and care challenges is a relatively new field, particularly in the area of MLTC-M. The NIHR was keen to engage with the AI and health and care communities prior to launching this new call, to learn from their experiences conducting research in this area and to seek feedback on how the scope could be refined. 

We published a draft research specification in April, to seek feedback on the call remit and the opportunities for the RSF. We also held engagement webinars, which over 120 people attended and gave useful feedback to help us shape the final call specification. 

We heard feedback on the feasibility of the scale and timescales of the Research Collaborations, and the importance of bridging communication between different disciplines, including a suggestion around developing ‘explainable AI’ resources. The community also fed back on the remit and structure of the RSF, highlighting the need to ensure it works with, and builds on, existing infrastructure and initiatives in this space (e.g. HDR UK, the NHSX AI Lab, the joint NIHR-MRC Tackling Multimorbidity at Scale programme) to ensure the best possible return on investment for the MLTC-M research community. Support with technical AI expertise and supporting networking and community building were identified as useful functions for the RSF. 

Next steps

We have refined the scope of the call to reflect community feedback:

  • We have extended the funding limit for the Research Collaborations from £2.5 to £5m and extended the timescales by six months. 
  • We have refined and clarified the specification to address questions from the community. 
  • The RSF will focus on providing technical expertise and capabilities to work with the AIM Research Collaborations, to overcome the challenges in conducting AI and MLTC-M research and build a network of researchers in this field. It will also work with existing parts of the NIHR, such as the NIHR Academy, to develop training and workshops for early and mid-career researchers. The competition for the RSF will launch in July.
  • The commissioning processes for the Research Collaborations and the RSF have been better aligned, so that the RSF will be in place for the start of the Research Collaborations.

We’d like to thank all of those who made time to attend our webinars and provide their views. AI and advanced data science provide a new avenue by which researchers can systematically identify or explore clusters of disease. We hope that by seeking input from the research community ahead of launching this call, we have developed a practical and relevant funding opportunity that will deliver the high quality and ambitious research needed to further our understanding of disease clusters.

The AIM call is now open and the next engagement event will be held on Friday, 17 July. More information can be found on the AI funding page

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