Aims. To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Methods. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation. Results. For THA, there were 5,558 patient radiology reports included, of which 4,137 were used for model training and testing, and 1,421 for external validation. Following training, model performance demonstrated average (mean across three folds) accuracy, F1 score, and area under the receiver operating curve (AUROC) values of 0.850 (95% confidence interval (CI) 0.833 to 0.867), 0.813 (95% CI 0.785 to 0.841), and 0.847 (95% CI 0.822 to 0.872), respectively. For TKA, 7,457 patient radiology reports were included, with 3,478 used for model training and testing, and 3,152 for external validation. Performance metrics included accuracy, F1 score, and AUROC values of 0.757 (95% CI 0.702 to 0.811), 0.543 (95% CI 0.479 to 0.607), and 0.717 (95% CI 0.657 to 0.778) respectively. There was a notable deterioration in performance on external validation in both cohorts. Conclusion. The use of routinely available preoperative radiology reports provides promising potential to help screen suitable candidates for THA, but not for TKA. The external validation results demonstrate the importance of further model testing and training when confronted with new clinical cohorts. Cite this article: Bone Joint J 2024;106-B(7):688–695

Aim. Restarting elective services presents a challenge to restore and improve many of the planned patient care pathways which have been suspended during the response to the COVID-19 pandemic. A significant backlog of planned elective work has built up representing a considerable volume of patient need. We aimed to investigate the health status, quality of life, and the impact of delay for patients whose referrals and treatment for symptomatic joint arthritis had been delayed as a result of the response to COVID-19. Methods. We interviewed 111 patients referred to our elective outpatient service and whose first appointments had been cancelled as a result of the response to the COVID-19 pandemic. Results. Patients reported significant impacts on their health status and quality of life. Overall, 79 (71.2%) patients reported a further deterioration in their condition while waiting, with seven (6.3%) evaluating their health status as ‘worse than death’. Conclusions. Waiting lists are clearly not benign and how to prioritize patients, their level of need, and access to assessment and treatment must be more sophisticated than simply relying on the length of time a patient has been waiting. This paper supports the contention that patients awaiting elective joint arthroplasty report significant impacts on their quality of life and health status. This should be given appropriate weight when patients are prioritized for surgery as part of the recovery of services following the COVID-19 pandemic. Elective surgery should not be seen as optional surgery—patients do not see it in this way

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To review the evidence and reach consensus on recommendations for follow-up after total hip and knee arthroplasty.

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It is common practice for patients to have postoperative blood tests after total joint replacement (TJR). However, there have been significant improvements in perioperative care with arthroplasty surgery, and a drive to reduce the length of stay (LOS) and move towards day-case TJR. We should reconsider whether this intervention is necessary for all patients.

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To evaluate the effectiveness of an institutionally developed algorithm for evaluation and diagnosis of prosthetic joint injection and to determine the impact of this protocol on overall hospital re-admissions.p