AI set to improve early detection of rheumatic and musculoskeletal diseases with £1.2million fund

A team led by Henley Business School, University of Reading, today announces it has been awarded a grant of £1.2 million for the development of RMD-Health – a machine learning system designed to significantly improve the early detection and referral of rheumatic and musculoskeletal diseases (RMD).  Piloting in the Royal Berkshire NHS Foundation Trust (RBFT) and Oxford University Hospitals NHS Foundation Trust (OUH), the three-year project, funded in part by NIHR, will fully develop the product ready for regulatory approval and commercialisation.

With up to one-third of the UK population affected by RMD, these diseases, including inflammatory arthritis (IA), are a leading cause of disability and one of the biggest contributors to sick days and unemployment. The need for accurate identification is critical yet IA can be difficult to detect and can present with non-specific symptoms. 

Professor Weizi (Vicky) Li, project lead and Professor of Informatics and Digital Health at Henley, says: “With an estimated annual cost of £1.8 billion in sick leave and work-related disability for rheumatoid arthritis alone, the current RMD referral system faces huge challenges.

“Our machine-learning system presents a new approach to RMD referrals. Unlike existing solutions, which often rely on the advice and guidance from already stretched rheumatology specialists, we’re introducing a machine learning-based decision support system enabling doctors to refer patients more accurately and promptly, ultimately leading to quicker and more effective treatment.”

With only 40 per cent accuracy in suspected early IA referrals by GPs in 2019-2021, there is a significant burden on secondary care clinicians who must sift through large volumes of referrals and attend unnecessary appointments. Delays in assessing referrals contribute to delayed patient access to the right clinics and treatments and often result in repeated GP consultations. 

Dr Antoni Chan, project co-lead and Consultant Rheumatologist and Physician at RBFT, adds: “This exciting and innovative project represents a major step forward in the early detection and referral of RMD, promising improved patient outcomes, reduced healthcare costs and increased efficiency across our healthcare system. 

“Developed using available patient referral data, the tool has so far demonstrated significantly higher accuracy during experiments at RBFT than existing clinical criteria and clinicians’ assessments. With this grant, we fully expect to be on track for regulatory approval at the end of three years.”

The development of a full software prototype features interdisciplinary collaboration among AI experts, secondary care specialists, GPs, industry stakeholders, patients and the public to establish future adoption of RMD-Health into the NHS.

The healthcare project is led by  Henley Business School, part of the University of Reading, in partnership with Royal Berkshire NHS Foundation Trust, RBFT Health Data Institute (HDI), Oxford University Hospitals NHS Foundation Trust, Health Innovation Oxford and Thames Valley, Buckinghamshire, Oxfordshire and Berkshire West Integrated Care Board and patient leaders.

Project leads: Professor Weizi (Vicky) Li (University of Reading, UoR) and Dr Antoni Chan (Royal Berkshire NHS Foundation Trust). Other investigators include: Dr Anthony Bradlow (RBFT), Eghosa Bazuaye  (RBFT), Professor Raashid Luqmani (Oxford University Hospitals NHS Foundation Trust), Dr Bing Wang (UoR), Julie Hart (UoR), Dr Daniel Alton (Buckinghamshire, Oxfordshire and Berkshire West Integrated Care Board), David Ruddy (Patient leader), Dr Mamta Bajre (Health Innovation Oxford and Thames Valley).  

The team acknowledges funding support from the National Institute for Health and Care Research (NIHR), the Engineering & Physical Sciences Research Council (EPSRC), the Health Innovation Partnership (UoR&RBFT), and Henley Business School.