ObjectivesIn response to demographic and health system pressures, the development of non-medical advanced clinical practice (ACP) roles is a key component of National Health Service workforce transformation policy in the UK. This review was undertaken to establish a baseline of evidence on ACP roles and their outcomes, impacts and implementation challenges across the UK.DesignA scoping review was undertaken following JBI methodological guidance.Methods13 online databases (Medline, CINAHL, ASSIA, Embase, HMIC, AMED, Amber, OT seeker, PsycINFO, PEDro, SportDiscus, Osteopathic Research and PenNutrition) and grey literature sources were searched from 2005 to 2020. Data extraction, charting and summary was guided by the PEPPA-Plus framework. The review was undertaken by a multi-professional team that included an expert lay representative.Results191 papers met the inclusion criteria (any type of UK evidence, any sector/setting and any profession meeting the Health Education England definition of ACP). Most papers were small-scale descriptive studies, service evaluations or audits. The papers reported mainly on clinical aspects of the ACP role. Most papers related to nursing, pharmacy, physiotherapy and radiography roles and these were referred to by a plethora of different titles. ACP roles were reported to be achieving beneficial impacts across a range of clinical and health system outcomes. They were highly acceptable to patients and staff. No significant adverse events were reported. There was a lack of cost-effectiveness evidence. Implementation challenges included a lack of role clarity and an ambivalent role identity, lack of mentorship, lack of continuing professional development and an unclear career pathway.ConclusionThis review suggests a need for educational and role standardisation and a supported career pathway for advanced clinical practitioners (ACPs) in the UK. Future research should: (i) adopt more robust study designs, (ii) investigate the full scope of the ACP role and (iii) include a wider range of professions and sectors.

IntroductionA global health workforce crisis, coupled with ageing populations, wars and the rise of non-communicable diseases is prompting all countries to consider the optimal skill mix within their health workforce. The development of advanced clinical practice (ACP) roles for existing non-medical cadres is one potential strategy that is being pursued. In the UK, National Health Service (NHS) workforce transformation programmes are actively promoting the development of ACP roles across a wide range of non-medical professions. These efforts are currently hampered by a high level of variation in ACP role development, deployment, nomenclature, definition, governance and educational preparation across the professions and across different settings. This scoping review aims to support a more consistent approach to workforce development in the UK, by identifying and mapping the current evidence base underpinning multiprofessional advanced level practice in the UK from a workforce, clinical, service and patient perspective.Methods and analysisThis scoping review is registered with the Open Science Framework (https://osf.io/tzpe5). The review will follow Joanna Briggs Institute guidance and involves a multidisciplinary and multiprofessional team, including a public representative. A wide range of electronic databases and grey literature sources will be searched from 2005 to the present. The review will include primary data from any relevant research, audit or evaluation studies. All review steps will involve two or more reviewers. Data extraction, charting and summary will be guided by a template derived from an established framework used internationally to evaluate ACP (the Participatory Evidence-Informed Patient-Centred Process-Plus framework).DisseminationThe review will produce important new information on existing activity, outcomes, implementation challenges and key areas for future research around ACP in the UK, which, in the context of global workforce transformations, will be of international, as well as local, significance. The findings will be disseminated through professional and NHS bodies, employer organisations, conferences and research papers.

Aims Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. Methods The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O’Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy. Results Of the 455 studies identified, only 12 were suitable for inclusion. Nine reported implant identification and three described predicting risk of implant failure. Of the 12, three studies compared AI performance with orthopaedic surgeons. AI-based implant identification achieved AUC 0.992 to 1, and most algorithms reported an accuracy > 90%, using 550 to 320,000 training radiographs. AI prediction of dislocation risk post-THA, determined after five-year follow-up, was satisfactory (AUC 76.67; 8,500 training radiographs). Diagnosis of hip implant loosening was good (accuracy 88.3%; 420 training radiographs) and measurement of postoperative acetabular angles was comparable to humans (mean absolute difference 1.35° to 1.39°). However, 11 of the 12 studies had several methodological limitations introducing a high risk of bias. None of the studies were externally validated. Conclusion These studies show that AI is promising. While it already has the ability to analyze images with significant precision, there is currently insufficient high-level evidence to support its widespread clinical use. Further research to design robust studies that follow standard reporting guidelines should be encouraged to develop AI models that could be easily translated into real-world conditions. Cite this article: Bone Joint J 2022;104-B(8):929–937.

Introduction Total hip and knee arthroplasty are common orthopaedic procedures that require post-operative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI) technology has the potential to automate image analysis. This systematic review reports on how AI-based technologies are currently being used and their accuracy in image analysis following THA and TKA. Methods EMBASE, Medline and PubMed libraries were systematically searched for articles published until 15/09/2021 using terms related to “x-ray analysis”, “total hip/knee arthroplasty”, and “AI”. The review was performed according to the PRISMA guidelines (PROSPERO#CRD42021276876). Study quality was assessed using a modified MINORS tool. AI performance was reported using the area under the curve (AUC) and accuracy. Results Of the 455 studies identified, 12 were included: nine reported implant identification, three described prediction of implant failure and three compared AI performance with orthopaedic surgeons. AI-based implant identification was precise (AUC 0.992–1) and most algorithms reported accuracy >90%. Two of these studies reported AI performance to be similar or superior to human experts. AI prediction of dislocation risk following THA was acceptable (AUC 76.67), diagnosis of hip implant loosening was good (accuracy 88.3%) and measurement of acetabular angles on post-operative x-rays was comparable to humans (Cohen's kappa 0.76–1.00). Conclusion AI technology can be trained to identify implant models on post-operative x-rays with a performance that is comparable to that of human experts. However, the technology requires further development to enable analysis of other post-operative radiographic features following arthroplasty surgery that could improve patient care. Take-home message Artificial intelligence image analysis through deep learning can classify hip and knee implants, and measure malposition and detect features of loosening of hip implants.

Over 8000 total hip arthroplasties (THA) in the UK were revised in 2019, half for aseptic loosening. It is believed that Artificial Intelligence (AI) could identify or predict failing THA and result in early recognition of poorly performing implants and reduce patient suffering.The aim of this study is to investigate whether Artificial Intelligence based machine learning (ML) / Deep Learning (DL) techniques can train an algorithm to identify and/or predict failing uncemented THA.Consent was sought from patients followed up in a single design, uncemented THA implant surveillance study (2010–2021). Oxford hip scores and radiographs were collected at yearly intervals. Radiographs were analysed by 3 observers for presence of markers of implant loosening/failure: periprosthetic lucency, cortical hypertrophy, and pedestal formation.DL using the RGB ResNet 18 model, with images entered chronologically, was trained according to revision status and radiographic features. Data augmentation and cross validation were used to increase the available training data, reduce bias, and improve verification of results.184 patients consented to inclusion. 6 (3.2%) patients were revised for aseptic loosening. 2097 radiographs were analysed: 21 (11.4%) patients had three radiographic features of failure.166 patients were used for ML algorithm testing of 3 scenarios to detect those who were revised. 1) The use of revision as an end point was associated with increased variability in accuracy. The area under the curve (AUC) was 23–97%. 2) Using 2/3 radiographic features associated with failure was associated with improved results, AUC: 75–100%. 3) Using 3/3 radiographic features, had less variability, reduced AUC of 73%, but 5/6 patients who had been revised were identified (total 66 identified).The best algorithm identified the greatest number of revised hips (5/6), predicting failure 2–8 years before revision, before all radiographic features were visible and before a significant fall in the Oxford Hip score. True-Positive: 0.77, False Positive: 0.29.ML algorithms can identify failing THA before visible features on radiographs or before PROM scores deteriorate. This is an important finding that could identify failing THA early.