BackgroundDiagnosis and treatment of PsA and axSpA is often delayed due to missing clear diagnostic criteria and limitations in resources for referral to rheumatologist including high numbers of incorrect referrals. Primary care is usually provided by either general practitioner, dermatologists, or orthopedics. Clinical discriminators with a high specificity for rheumatic conditions include morning stiffness (MST; peripheral or axial, >30min). Artificial intelligence (AI) and natural language processing (NLP) methods offer algorithms for learning systems to recognize disease associated terms and classify clinical phenotypes using large data sets that may support early identification of patients with suspected diagnosis of PsA or axSpA.ObjectivesAI and NLP methods are used to identify patients with typical attributes for inflammation by using morning stiffness as one potential discriminating pattern, which can be detected automatically and might help to prioritize referral for rheumatologist appointments.MethodsWithin a multicentre observational study, patients with visits at the rheumatologist with a suspected diagnosis of PsA or axSpA from the referral primary care provider were recruited. All data on clinical examinations and findings were collected and evaluated by rheumatologists in focus on criteria for diagnosis of PsA/axSpA (gold standard for evaluation). Unstructured text from the patient history was used to extract diagnosis-relevant characteristics. The information extraction algorithms used NLP models to detect expert curated “morning stiffness” (MST) keywords and puts them into a contextualized framework that allows to capture possible negations.ResultsA total of 116 patients were recruited (73 female, 63%) with a median age of 42 (IQR: 34-54). 51 patients were referred as axSpA (44%) and 60 as PsA (52%) by primary care providers. After preselection for PsA and axSpA patients, we observed a 23% rate of referrals without rheumatic diagnosis. Only 7.1% of patients were admitted without signs of MST, 29% with axial MST, 35% with peripheral MST and 28% with both MST types. Average morning stiffness duration was recorded as 35 minutes; patients with a finally confirmed rheumatic diagnosis had a higher average MST duration reported (36 minutes) compared to patients without a confirmed diagnosis. Our AI assisted extraction of MST identified MST in 82.7% of patient history texts. In 75% NLP methods correctly identified the negation of MST symptoms (6 of 8), and 94% of MST was detected when both axial and peripheral joints were affected (30 of 32). Manual inspection of 20 patient history reports where MST was not detected by our automated algorithm revealed that 17 reports did not contain information about MST and three mention unspecific early morning discomfort, without mention of MST.ConclusionThe high rate of correct detection of MST from patient history text using NLP methods allowed us to assess the potential for NLP models to support automated analysis of patient reports to facilitate intelligent patient referral.AcknowledgementsWe thank the Fraunhofer Excellence Cluster for Immune-Mediated Diseases CIMD for the financial support.Disclosure of InterestsNone declared