Data use register

Project code: SDE_NENC_PROJ_5

Applicant: Newcastle University

Data type: Grouped (small numbers hidden)

Project description: The goal of this project is to understand how taking lots of medicines (polypharmacy) and having several long-term health conditions affect people in the North East and North Cumbria.

People are living longer, but this means they often have long-term health problems. Because of this, many need to take more medicines. Taking five or more medicines is called polypharmacy. This can cause problems, especially for older people, such as becoming frail, needing to go to hospital more, or medicines reacting badly with each other. These problems are sometimes called inappropriate polypharmacy.
Some studies have looked at polypharmacy in poorer communities or among people from ethnic minorities. These groups may be at higher risk of taking too many medicines that could cause harm, but we still don’t know exactly how bad the problem is.

This project will focus on understanding how common polypharmacy is in the North East and North Cumbria. It will also look at the link between taking lots of medicines and having more than one long-term health condition. By using data about people’s backgrounds in different areas, the project will study how polypharmacy affects people in disadvantaged communities. The aim is to help those most at risk and suggest ways to make things safer for patients.

Date of agreement: Not applicable, no personal data being processed

Period of data access agreement: Not applicable

Project code: SDE_NENC_PROJ_1

Applicant: Newcastle University

Data type: Pseudonymised Record Level - people's names and contact information is replaced with a code which allows their health information to be linked, but they can't be identified.

Project description: Many people live with two or more long-term health problems, such as cancer, heart disease, or mental health conditions. These health problems, called Multiple Long-Term Conditions (MLTCs), can lead to worse health and shorter lives.

Treating MLTCs can be tricky. Often, people need to take lots of medicines—this is called polypharmacy when it’s five or more. But taking lots of medicines can sometimes cause new problems if the drugs don’t work well together.

The AI MULTIPLY project wants to make treatment better for people with MLTCs. It will look at how these conditions and taking many medicines are connected. The project will also study how personal and social factors affect polypharmacy. By learning more about these links, the project hopes to create fairer healthcare for everyone.

Date of agreement: 07 May 2024

Period of data access agreement: One year

Project Identifier: SDE_NENC_PROJ_1

Legal Name of Contracting Organisation: University of Newcastle

Lay Summary:

Many people have multiple long-term conditions (MLTC-M) at the same time. These problems can include things like cancer, heart disease, diabetes, or mental health conditions. When someone has multiple health conditions, it can be harder to stay healthy. What’s more, factors like ethnicity, social status, and biological sex impact this further.

People with many health conditions often need to take lots of different medicines. This is called “polypharmacy”. Sometimes, these medicines can affect each other or cause side effects. This can make someone feel worse.

The aim of our research is to learn how long term health conditions and the medicines used to treat them change over a person’s life.

Our research team includes many different experts like doctors, pharmacists and data scientists. The experts study people and community groups and work closely with patients and the public. This makes sure that our research is useful and fair.

Our goal is to understand:
• how long-term health problems start to happen over time
• how taking many medicines affects people
• how things like culture and poverty can change people’s encounters with healthcare

To do this, we use computer programs called “machine learning” or “Artificial Intelligence (AI)”. These programs look for patterns in very large sets of NHS health records. These include things like test results, findings, and prescription details. We make sure to include data from people of different backgrounds. This means that the research helps a bigger group of people.

Patients and community groups have already helped us shape our research questions and we continue to work with those groups to help us learn what our findings mean and whether they’re important to patients.

Public benefit statement:
Through better understanding the relationships between multiple long-term conditions, polypharmacy, personal and social factors, AI-MULTIPLY aims to optimise treatment for individual patients.

The ultimate aim is to develop new AI techniques that will help doctors to provide more personalise, effective, and equitable healthcare for people with long-term conditions.

Understanding multiple long-term health conditions
better:
• Figure out how different long-term conditions develop and interact over time
• Identify critical moments or “tipping points” where health conditions start to rapidly progress
• Create wats to predict and potentially prevent sudden worsening of health

Help to develop strategies to reduce health
Inequalities:
• Understand how factors like ethnicity, social status and gender impact health outcomes
• Discover why some groups of people experience worse health trajectories
• Help to develop strategies to reduce health disparities

Date of Countersigned Contract: 3/26/2026

Current Project Status: Live - Data in use

Health Research Classification System (HRCS) Category: Generic Health Relevance

Multiple-SDE Project Indicator: No

Is SDE the lead SDE in this project? Not applicable

Name of SDE parties: Not applicable 

Further Information: https://ai-multiply.co.uk/