This project will both develop and validate population clusters that consider health and social care determinants and subsequent need for people with MLTC using data-driven Artificial Intelligence (AI) methods, which will be compared with expert-driven approaches to validate efficacy of the machine learning methodology. This will be followed by evaluation of cluster trajectories and efficacy in relation to improved health outcomes and reduction in costs/resources.
Background
An estimated 14 million people in England are living with multiple long-term health conditions (MLTC). Efforts to improve care mainly focus on biological markers or medical features of disease such as blood pressure or cholesterol, without adequately addressing other non-medical factors that contribute to good health. This may include social, economic and environmental factors such as mobility, housing conditions, finances or social isolation.
A shift towards integrated care that considers the ‘whole person’ and their environment is essential in addressing the complex individual needs of people living with MLTC. One approach to delivering more personalised care is to 'cluster' or group people based on similarities in their medical and non-medical needs. This approach has been adopted in other countries but not in the UK due to uncertainty about how to develop clusters, and a lack of evidence linking this approach to improved health or reduced costs.
Aims
Methods
Our findings
We will make our findings available and accessible to people with MLTC, and those who work and make decisions in health and social care. We will co-host information events with a range of audiences to share the results and discuss the wider implications for improving the health and social care of people experiencing MLTC.
Ethics Number 67953
Our study has investigated how the condition of the local environment and neighbourhood area, impacts on the health and wellbeing of those living with multiple long-term conditions.
You can read our plain english research summary and watch our lay summary video by clicking the buttons below.
Please see the staff working on this project below.