Computer scientists at Loughborough University have used GP and hospital data from more than 9,600 patients with learning disabilities and multiple health conditions to develop the AI model.
An artificial intelligence model, developed by computer scientists at Loughborough University as part of the DECODE project, aims to tackle healthcare challenges faced by people with learning disabilities and multiple health conditions.
This group has a life expectancy of 20 years lower than the UK average, often due to poorer physical and mental health and a higher likelihood of having multiple chronic illnesses. These factors increase the risk of preventable complications, reduced quality of life, and prolonged hospital stays.
“This research demonstrates how AI could help tackle these vast inequalities by spotting patterns and predicting resource needs, which could all improve patient outcomes,” said Jon Sparkes, chief executive of learning disability charity Mencap.
Early and accurate predictions
The Loughborough University researchers used GP and hospital data from more than 9,600 patients with learning disabilities and multiple health conditions to develop an AI model capable of predicting hospital stay lengths within the first 24 hours of admission.
“The model generates predictions by assessing factors such as a patient’s age, medication history, lifestyle, and existing health conditions,” said Georgina Cosma, an expert in AI for healthcare at Loughborough University and DECODE co-investigator.
The model was 76% effective in distinguishing between patients likely to have prolonged hospital stays and those who would be discharged sooner.
It was also used to analyse the hospital data to identify key reasons for hospitalisations and health patterns among people with learning disabilities and multiple health conditions.
It found that cancer is the leading cause of hospital admissions for men and women with learning disabilities and multiple health conditions and on average people with learning disabilities and multiple health conditions stay in hospital for three days. Stays that exceed 129 days are often linked to mental illness.
“With early and accurate predictions, hospitals can plan better and provide more personalised care, ensuring fair treatment for all patients,” said Cosma.
Risk prediction algorithms
The insights from this study will be used to support the NHS in developing risk prediction algorithms to assist clinicians in decision-making.
“While hospital care is an important part of healthcare provision, we are exploring ways to minimise the need for hospitalisation by exploring where health interventions could be delivered earlier and people with learning disabilities could be engaged in their care better,” said Satheesh Gangadharan, consultant psychiatrist with the Leicestershire Partnership NHS Trust and the DECODE co-principal investigator.
The data used to train the AI model came from GPs and hospitals in Wales. As part of their next steps, the researchers are applying the model to datasets from hospitals in England to assess whether similar patterns emerge across different populations.
“We’re also seeking additional funding for a clinical trial to test how this personalised prediction tool can reduce emergency admissions and improve quality of life for patients with learning disabilities and multiple long-term conditions,” said Thomas Jun, an expert in sociotechnical system design at Loughborough University and DECODE co-principal investigator.
