Project title: RMD-Health: Machine learning-enabled decision support system to improve early detection and referral of rheumatic and musculoskeletal diseases.
Lay summary: This project aims to develop and validate an ML-based risk stratification decision support system, RMD-Health, for accurate and fast referral for patients with suspected RMD to a point where it can be submitted for regulatory approval compliant with AI as a Medical Device Regulations.
Objectives:
1) Evaluate the risk stratification model in large-scale retrospective data to detect model bias.
2) Optimise and de-bias the risk stratification model to maximise accuracy and generalisability, and ensure fairness among patient subgroups through retrospective evaluation.
3) Develop a full software prototype of a real-time decision support system that incorporates user requirements.
4) Pilot and test the prototype in RBFT and OUH rheumatology referral pathways for usability, health economics, clinical safety and efficacy for regulatory approval and clinical adoption through prospective clinical validation.
Public benefit statement: The RMD system will benefit:
1) patients by enabling accurate and fast referrals, thus reducing the time waiting for the right treatment
2) GPs by reducing unnecessary consultations and workload
3) secondary care clinicians by reducing unnecessary appointments and workload by automatic and more accurate risk stratification. Our experiment shows it can reduce 8 hours per week (2 clinics) for referral assessment
4) the NHS by reducing costs created by long referral times and unnecessary appointments.
This research will contribute directly to NHS long-term plan of transforming elective care including the delivery plan for tackling the COVID-19 backlogs.
Organisation: University of Reading
Date of signed agreement: 11/06/2026
Health Research Classification System (HRCS) category: Inflammatory and Immune System
Current project status: Live – Data in use
Multiple-SDE project: No
Lead SDE: TVS SDE
Participating SDEs/organisations: TVS SDE
Further reading: