Background

Multimorbidity is becoming commoner as the population becomes older and people with long-term health conditions live longer. Currently, people with multimorbidity are treated separately for each condition and prescribed different drugs, each to treat one condition. Taking multiple medicines, or polypharmacy, increases the likelihood of serious side effects.

The NHS introduced Structured Medication Reviews by GPs and pharmacists to reduce the number of people taking potentially harmful combinations of drugs. However, there is no easy way of predicting which patients are most likely to benefit from a medication review and prioritising them. The review team is then faced with gathering and making sense of information from records held in different places, and piecing the information together to see how the patient’s conditions and treatments changed over time.

Aims & Objectives

DynAIRx aims to develop new, easy to use, artificial intelligence (AI) tools that support GPs and pharmacists to find patients living with multimorbidity (two or more long-term health conditions) who might be offered a better combination of medicines.
We will focus on three groups of people at high risk of rapidly worsening health from multimorbidity:
1. Older people with frailty.
2. People with mental and physical health problems.
3. Other people with four or more long-term health conditions taking ten or more drugs.

Methods

DynAIRx will develop tools to combine information from electronic health and social care records, clinical guidelines and risk-prediction models to ensure that clinicians and patients have the best information to prioritise and support Structured Medication Reviews.
We will develop AIs that combine information from multiple records and guidelines and calculate risks of hospital admissions and other adverse outcomes for our three multimorbidity groups. To ensure this information is easily understandable we will develop visual summaries of patients’ journeys, showing how health conditions, treatments and risks of future adverse outcomes are changing over time. These visual summaries will be tested in general practices across northern England and improved based on feedback from clinicians and patients.

Partners & Collaborators

Investigators
Buchan I, Walker L, Clegg A, Mair F, Kullu C, Sperrin M, Relton S, van Staa T, Gabbay M, Ruddle R, Bollegala D, Maskell S, Shantsila E, Leeming G, Woodall A, Griffiths A

Timescales

01/09/2022 – 31/08/2025

Funding agency

NIHR Artificial Intelligence for Multiple Long-Term Conditions

Further information

For further information contact Professor Andrew Clegg