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About AIM and RSF
Developing data standards, best practice and community around AI for multiple long term conditions research
AIM Community
The AIM (AI for Multiple Long-term Conditions) Community comprises multiple research consortia, each dedicated to a distinct focus and mission. These consortia stand at the forefront of innovation in addressing the intricate challenges associated with Multiple Long-Term Conditions (MLTC).
About the eight consortia in the AI for Multiple Long-term Conditions (AIM) Programme
AIM-CISC: This project uses Artificial Intelligence to analyse and understand available information to attempt to establish what patterns of multimorbidity are most common, which most affect people's lives, and help improve the quality and safety of care. AIM-CISC project is based at the University of Edinburgh, with collaborators from The Roslin Institute, NHS Lothian, and University College London.
AI-MULTIPLY: The project aims to characterise the dynamic relationships of MLTC and polypharmacy and inform healthcare pathways. The project is based at Newcastle University and Queen Mary, University of London.
Cluster-AIM: This project seeks to develop and validate population clusters to integrate health and social care using mixed methods. This project is based at the University of Southampton, University of Oxford, University of Kent, University of Nottingham and the University of Leicester.
DECODE: This project focuses on mapping the challenges and requirements for Data-driven, machinE learning-aided stratification and management of multiple long-term COnditions in adults with intellectual DisabilitiEs (ID). The project is based at Loughborough University and has collaborators at the Leicestershire NHS Trust, Loughborough University, the University of Leicester, and De Montfort University.
DynAIRx: This project aims to develop new, easy-to-use AI and data viz tools that help GPs and pharmacists treat patients with MLTC. This project is based at the University of Liverpool, University of Leeds, University of Manchester and the University of Glasgow.
MELD-B: This project is based at the University of Southampton, with collaborators at the University of Glasgow, Swansea University, Southampton City Council, the University of Aberdeen, and King's College London. The MELD-B team uses an Artificial Intelligence-enhanced analysis of birth cohort data and electronic health records to identify life-course time points and targets for the prevention of early-onset, burdensome Multiple Long-Term Conditions
OPTIMAL: This project is based at the University of Birmingham and the University of Manchester, with collaborators in University Hospitals Birmingham NHS Trust, NHS Greater Glasgow & Clyde, and the University of St Andrews. The project is about OPTIMising therapies, discovering therapeutic targets and AI-assisted clinical management for patients living with complex multiple long-term conditions.
CoMPuTE: This project uses Artificial Intelligence to predict who is more likely to develop multiple long-term conditions. Although it wasn’t part of the original AIM grant, it is funded by the National Institute for Health and Care Research (NIHR) under its Programme Grants for Applied Research (NIHR204406). The project is based at the University of Oxford, University of Leeds, and University College London.
The Role of the Research Support Facility (RSF)
At the heart of this dynamic AIM Community, the Research Support Facility (RSF) plays an indispensable role in ensuring that these seven research consortia achieve their goals and deliver meaningful results. RSF is supporting the AIM through five, interconnected themes.
Theme 1: Reproducible, secure and interoperable infrastructure
This theme focuses on providing expertise and support across dataset-specific, methodological and infrastructural/technical domains, aiming to increase the efficiency and reproducibility of research in secure settings.
This theme plays a crucial role in assembling data that is ‘research-ready’. As experts in data curation and quality control, the RSF Data Wranglers ensure that data and workflows are standardised, making it easier for other researchers to reuse data and research artefacts in extended analyses within the AIM Programme.
This theme aims to build connections between early career researchers (ECRs) across the AIM programme so knowledge and expertise can be shared across AIM and into the wider network.
Enhancing existing patient and public involvement networks across the AIM programme, this theme supports and empowers people with lived experience of MLTC to co-create research with the AIM consortia.
This theme works with the AIM community to understand research outputs and help to amplify their value. Working with researchers, policymakers and other valued stakeholders, this theme aims to maximise clinical and academic outputs for long-term impact in MLTC research.
AIM Handbook
In this handbook, you can find detailed information on each consortium. It includes plain English summaries of projects within the AIM programme, complemented by illustrations from the research support facility to aid understanding. These summaries are a collaborative effort by the research consortia and are co-authored within this handbook:
AI for Multiple Long Term Conditions: Research Support Facility (AIM RSF), AIM-CISC, AI-MULTIPLY, Cluster-AIM, DECODE, DynAIRx, MELD-B, & OPTIMAL. (2024). AIM for Multiple Long-Term Conditions Handbook. Zenodo. https://doi.org/10.5281/zenodo.10867842