P109 Early diagnosis of inflammatory arthritis (IA) using machine learning analysis of GP referral letters and blood tests to improve pre-hospital referral triage

Abstract

Abstract Background/Aims The diagnosis of inflammatory arthritis (IA) from referral letters is challenging. We aimed to develop a model integrating GP referral letter data and pre-referral blood test results (BTR) to identify clusters of patient phenotypes (characteristics) that predict at referral that patients will have any of three types of IA (rheumatoid arthritis, seronegative inflammatory arthritis and psoriatic arthritis) requiring early DMARD treatment. Methods The anonymised text of original GP referral letters of patients on our departmental DMARD monitoring database with diagnoses of IA was compared with referral letters of patients with other inflammatory rheumatological conditions (OICs) entered into the same database over the same period. We developed and used novel natural language processing (NLP) methods based on bidirectional encoder representations from transformers (BERT) to identify patients with IA and OICs for triage purposes. Results We have analysed 867 OIC patients and 267 IJD letters using our NLP methods. Data augmentation was used to address imbalance and small data challenges in this real-world application. Our method using only GP referral letters achieved overall accuracy (using confirmed diagnosis as the outcome) of 80% in identifying IJD and OCIs, reaching 86% sensitivity and 84% precision (reproducibility) at detecting OICs, compared with 71% sensitivity and 68% precision in detecting IA patients. Conclusion Our NLP methods alone identified OIC with >80% sensitivity and precision. In data science terms our NLP based method thus has high sensitivity for identifying a wide variety of OICs likely to need DMARD treatment. Our methodology may have great practical value if GP referral letters can be analysed at source. For high specificity and precision very large amounts of data are required to train the NLP model; we had many more OIC letters than IA letters available in analysable formats from other databases in our department. Analysable text of referral letters from primary care is currently difficult to obtain and performing NLP on photographs of referral letters in electronic patient records (EPRs) is a major challenge. These significant bottlenecks in NHS data systems must be removed to allow direct access and analysis of GP referral letters in the compatible format. BTR will be easier to analyse in the EPRs. Combined analysis of pre-referral NLP and BTR may increase the efficiency of automated triage of GP referrals with suspected early IA. Machine learning analysis of GP referrals potentially allows triage of the right patient into the right clinic first time, assisting demand management and capacity in stretched rheumatology clinics and improving patient experience by ensuring the right clinic appointment. Further work is underway to improve the precision of the system with a view to embedding this into our advanced Advice and Guidance triage process. Disclosure A. Bradlow: None. B. Wang: None. W. Li: None. E. Bazuaye: None. A.T.Y. Chan: Member of speakers’ bureau; UCB, Novartis, Sanofi, AbbVie, Celgene and Janssen.