Improving musculoskeletal care with AI enhanced triage through data driven screening of referral letters.

ABSTRACT: Musculoskeletal complaints account for 30% of GP consultations, with many referred to rheumatology clinics via letters. This study developed a Machine Learning (ML) pipeline to prioritize referrals by identifying rheumatoid arthritis (RA), osteoarthritis, fibromyalgia, and patients requiring long-term care. Using 8044 referral letters from 5728 patients across 12 clinics, we trained and validated ML models in two large centers and tested their generalizability in the remaining ten. The models were robust, with RA achieving an AUC-ROC of 0.78 (CI: 0.74-0.83), osteoarthritis 0.71 (CI: 0.67-0.74), fibromyalgia 0.81 (CI: 0.77-0.85), and chronic follow-up 0.63 (CI: 0.61-0.66). The RA-classifier outperformed manual referral systems, as it prioritised RA over non-RA cases (P < 0.001), while the m

SUBMITTER: Maarseveen TD 

PROVIDER: S-EPMC11825706 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature