CoMPuTE

Background:

Why is this research important?

Over a quarter of adults in England have more than one health condition. By 2035 this is expected to increase by 10-17%. Having more than one condition is called ‘multiple long-term conditions’ (MLTC), previously known as Multimorbidity. The more conditions someone has, the more disabling the effects.

MLTC is difficult for patients and carers: taking more medicines (with possible problems caused by conflicting or simply too many medications); the cost and wasted time of attending too many healthcare appointments, and the day-to-day challenges of living with multiple conditions.

This study hopes to predict who will suffer from MLTC and how MLTC will progress over a person’s lifetime. Previous research has focused on looking at the causes of MLTC, however, much is still unknown about why certain conditions appear together, and how they relate to normal ageing, prevention, and appropriate care. Also, although the NHS currently invests significant amounts of money in trying to prevent specific health conditions (e.g. heart disease, cancer), many people do not engage. This is a missed opportunity to prevent future ill health. 

Methods

Aim

This project looks at whether using artificial intelligence (AI) can help us predict those more likely to develop MLTC – to get help sooner to those who need it and prevent people from developing MLTC in the first place.

How  will we do this?

Regular computer models are already used for research on electronic health records. We want to use AI techniques to process this information faster and more accurately. The data will be ‘anonymised’ so it cannot be traced to individuals. Because many people have concerns about how their data are used, members of the public have been involved in this work from the beginning and will be involved throughout. A public member leads one section of work. Other public members work on an equal level with academic researchers.

• This study hopes to see whether it is possible to predict who will suffer from MLTC and how MLTC will progress over a person’s lifetime.
• It will investigate inequalities and the health and financial burden of MLTC.
• It brings in the public perspective on ethical and social questions about the use of AI in healthcare. Members of often-excluded communities will be actively involved in discussion groups, the development of study materials and the writing of papers. This is important to ensure that plans to help people with MLTC address everyone’s health and care needs.

The amount of public participation and leadership in this project makes it unique. One-third of the project is entirely public-led.

The project is organised into three different themes based on the three main aims:

   I.        To harness the power of longitudinal Electronic Health Records to develop AI-enhanced models and tools and improve the management (prevention and treatment) of mid-life and early old age MLTC and Complex-MLTC. (Theme 1)

    II.        To characterise the epidemiology, inequalities and costs of Complex-MLTC by clusters of disease trajectories identified in mid-life and early old age. (Theme 2)

    III.        To ensure decision-making tools and other outputs are fit-for-purpose and account for actual lived care needs and, therefore, serve the needs and expectations of target audiences. (Theme 3)

What we will do with the findings

This programme will use innovative AI techniques, causal inference, qualitative methods, and public leadership to improve our understanding of Complex-MLTC and their management. We will use multiple dissemination channels, including publications in high-impact journals alongside news media and web presentations, with an equal emphasis on impactful direct-to-public dissemination. This will include:

• Channels as determined by a stakeholder workshop to ensure comprehensive, impactful dissemination and uptake of the results
• International conference presentations
• Guidelines for the management of MLTC, implemented through Academic Health Science Networks and feeding into subsequent NICE guidance.
• Web presentation of the primary results including video clips and research blogs.
• Prototype dashboard for managing MLTC.

Adoption of NICE guidelines for prescribing in general and management of MLTC, in particular, will provide national guidance.

Who We Are

Lead applicant Professor Rafael Perera is Professor of Medical Statistics, Lead for the Thames Valley Applied Research Collaborative and Deputy Lead for the Oxford British Research Council multimorbidity themes and has studied long-term conditions. He has overall responsibility for managing the teams, ensuring cross-speciality communication and learning, and ensuring that the work progresses to time. He leads a team of data scientists, social scientists, statisticians, epidemiologists, psychiatrists and philosophers, who together have a broad spectrum of experience which they bring to addressing the problem of analysing the causes and development of multimorbidity.

Theme leads:

Theme 1: AI and NHS Capability Theme, Professor Clare Bankhead

Theme 2: Epidemiology, Inequalities and Health Economics Theme, Associate Professor Derrick Bennett

Theme 3: Ethics, Patients and the Public Theme, Anica Alvarez Nishio 

Contact details: nicola.pidduck@phc.ox.ac.uk / mary.logan@phc.ox.ac.uk

Glossary

Artificial Intelligence: the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Complex-Multiple Long-Term Conditions: Four or more concurrent conditions