OUR

RESEARCH

Many people live with two or more ‘long-term health conditions’ which include lots of different illnesses, such as cancer, heart, and mental health problems. People living with multiple long-term conditions may progress to poor health and have a shorter life expectancy.

Treating multiple health conditions is a balancing act. It often requires taking multiple medications, known as polypharmacy (when more than five drugs are used). The relationship between MLTCs and polypharmacy is complex, and sometimes these medications (and their side effects) can interact in ways that can cause further health issues.

About Us

Research Aims

The AI MULTIPLY project aims to improve treatment for individuals with MLTCs by exploring the connections between these conditions and polypharmacy. It will also investigate the personal and social factors that contribute to polypharmacy. By gaining insights into these relationships, the project will support the development of strategies to address the issue with the goal of reducing healthcare inequalities.

Our group has experience in applying new developments in computer technology, termed artificial intelligence (AI) and machine learning, to healthcare data. We will develop these methods to look for new patterns linking MLTCs and prescribed medicines. The information and patterns generated will feed into the design of a larger collaborative project.

Outcomes

Health Outcomes: The project is expected to improve patient care by offering personalised treatment plans.

Cost savings: Streamlined treatment can reduce hospital visits and healthcare costs.

Health Equity: Addressing inequalities by improving access to care and treatment options for disadvantaged communities.

In the long term, we hope our research will lead to the development of strategies for prevention and improved management of multiple long-term conditions.

Research WORK PACKAGES

Data – WP 1, 2
Health – WP 3, 4
Collaboration – WP 5
Inequalities
PPIE

Data

Work Package 1: Collecting Data

“Data Access, Data Wrangling, Data Engineering” AI MULTIPLY researchers will make use of multiple, large patient datasets, both national and…

Data

Work Package 2: Analysing Data

We will look for relationships between MLTC, polypharmacy and personal/ social factors.

Health

Work Package 3: Clinical Interpretation of Results

Work package 3 will use data from Electronic Health Records to explore health outcomes associated with AI-derived patterns and trajectories of MLTC….

Health

Work Package 4: Investigating and supporting collaboration across a diverse interdisciplinary research team

Studies how all the different people in the consortium work together in practice and how these ways of working inform the design of the artificial intelligence technology.

Collaboration

Work Package 5: Health and Social Care Outcomes

This work package is focused on making sure these findings can be used in practice to help improve the treatment of people with different long-term conditions.

Cross-Cutting Inequalities

Inequalities

Our cross-cutting intersectional inequalities theme acknowledges that structural, contextual and individual factors
such as ethnicity, gender, differing abilities and socioeconomics, are interactive and multiplicative in determining
health outcomes

Cross-Cutting PPIE

Patient and Public Involvement and Engagement

Our patient advisory group has developed an innovative PPI structure, to ensure the study aims and outcomes reflect patient and public priorities.

Governance

The AI MULTIPLY consortium is directed by Nick Reynolds, Professor of Dermatology at Newcastle University and Michael Barnes, Professor of Bioinformatics and Director of the Centre for Translational Bioinformatics at Queen Mary University of London.

Publication Policy

Governance Structure