Protocol for Radiographer x AI led discharge

Abstract

Abstract Introduction Emergency Department (ED) overcrowding, often exacerbated by prolonged patient length of stay (LOS), is a global challenge. Patients presenting with suspected fractures—many of whom are triaged as low-acuity, contribute significantly to the ED burden. Radiographer-led discharge (RLD), supported by artificial intelligence (AI), presents a potential strategy to streamline care, reduce LOS, and maintain diagnostic safety. Methods This multi-centre, retrospective study evaluates whether diagnostic radiographers, assisted by the radiological AI decision support tool RBfracture 2 . 6 , can safely discharge patients with no acute skeletal or joint injury. Fifteen radiographers from three countries will independently assess 340 retrospective radiographic examinations (300 consecutive and 40 enriched with rare findings). Referral notes and AI predictions are available. Reference standards are established by consensus among three MSK radiologists/reporting radiographers. Primary outcomes include ED workload reduction (true negatives) and false negative rate. Secondary outcomes will assess AI standalone performance and inter-country comparison. Results The primary object is to evaluate whether diagnostic radiographers from three different countries, assisted by the AI tool Rbfracture 2.6, can safely reduce the emergency department (ED) workload by discharging patients without acute skeletal or joint injuries, specifically those referred with suspected bone fracture or joint dislocation. A secondary objective is to validate the performance of RBfracture 2.6 in detecting fractures, joint dislocations, elbow effusions, knee effusions, and knee lipohemarthrosis.

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