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Adéle Stewart-Lord,1 Clare Beanlands,2 Ricardo Khine,3 Shani Shamah,4 Noreen Sinclair,1 Sandie Woods,2 Nick Woznitza,5 Lesley Baillie6 1Therapeutic Radiography Allied Health Sciences School of Health and Social Care London South Bank University, London, UK; 2Occupational Therapy, Allied Health Sciences, London South Bank University, London, UK; 3Therapeutic Radiography, City University of London, London, UK; 4Service-User; Research (Public Patient Involvement) Consultant, Independent, London, UK; 5Diagnostic Radiography, Homerton University Hospital NHS Foundation Trust, London, UK; 6Adult Nursing, London South Bank University, London, UKCorrespondence: Adéle Stewart-LordTherapeutic Radiography Allied Health Sciences School of Health and Social Care London South Bank University, 103 Borough Road, London SE1 0AA, UKTel +44 207 815 7931Fax +44 207 815 7933Email adele.stewart-lord@lsbu.ac.ukObjective: To investigate the profiles of advanced clinical practitioners (ACPs) in the allied health professions (AHPs) and their skills, attributes, experiences and involvement in new models of care.Methods: A 2 phase, cross sectional, mixed method survey of AHP ACPs across London was conducted in 2018– 2019. Online questionnaires were completed by 127 AHP ACPs and then semi-structured interviews were conducted with 15 AHP ACPs.Results: The survey results gave a comprehensive overview of the attributes of AHPs in ACP roles across London. There was considerable variability between role titles, types and levels of qualification, and evolution of the roles. The respondents predominately worked in clinical practice, and less frequently in other ACP domains (research, leadership and management, education). The interview findings provided in-depth insights into the AHP ACP roles within four themes: being advanced, career pathways, outcomes of the advanced practitioner role and influencing and transforming. The “Being advanced” theme highlighted that expert practice comprised confident and autonomous practice, leadership, and applying specialist and expert decision-making skills. “Career pathways” highlighted the diversity within the participants’ roles, titles, career opportunities and development. In the “Outcomes of the advanced clinical practitioner role” theme, the ACPs described their services as prompter, more accessible and providing an improved patient journey. The “Influencing and transforming” theme highlighted networking and dissemination and ideas for innovation, influencing and transforming services.Conclusion: This is the first comprehensive profile of ACP roles across AHPs and indicates that these roles are already having a positive impact on healthcare services and supporting new models of care. However, establishing the necessary infrastructure, standardization and governance for ACP roles across sectors, along with the career pathways, funding, sustainability and education, could increase impact in the future.Keywords: allied health professionals, advanced practice, role development, expert practice, models of care, pathway configuration

Introduction: Feeding via a misplaced nasogastric tube (NGT) is a common but preventable cause of patient harm. The aim of this study was to determine the accuracy of diagnostic radiographers' assessment of NGT position on chest radiographs (CXRs) and safe-to-feed decisions., Methods: A multi-case, multi-reader study was conducted using an online image interpretation platform. A test bank consisting of 15 CXRs with an NGT in-situ was created. Diagnostic radiographers without formal qualifications in CXR interpretation were recruited via two international conferences. Participants placed an electronic marker on each CXR to identify the location of the tip of the NGT and indicated whether or not they believed that the tube was safely positioned., Results: 68 participants were recruited. Each participant reviewed 15 CXRs, providing 1020 unique image assessments. 76 % (n = 778/1020) image assessments were completely correct (both the position of the tip of the NGT was accurately located and an appropriate safe-to-use decision made). In 5 % (n = 56/1020) of cases the NGT was safely positioned and the location of the tip was correctly identified by the participant but the tube was erroneously determined to be unsafe for feeding. In a further 6 % (n = 59/1020) of cases the participant correctly located the tip of an NGT in an unsafe position but indicated that the tube was safe to use. Participants failed to correctly identify the tip of the NGT in the remaining 12 % (n = 127/1020) of cases., Conclusion: Consistent with previous studies involving other staff groups, diagnostic radiographers without formal qualification in CXR interpretation can assess NGT positioning on radiographs with moderate accuracy but require further training, including strategies for the identification of poorly-visualised tube tips, to achieve the 100 % accuracy necessary for this safety-critical task., Implications for Practice: A bespoke training programme which includes teaching on image quality, tube tip identification and assessment of tube positioning using the National Patient Safety Agency (NPSA) four criteria, should be delivered prior to implementation of a radiographer-led NGT CXR evaluation service., Competing Interests: Conflict of interest statement Andy Creeden – No conflicts of interest to declare. Dr Sonyia McFadden– No conflicts of interest to declare. Dr Clare Rainey - No conflicts of interest to declare. Shaunagh Campbell - Travel grant: Qure.ai Technologies. Dr Sarim Ather – Shareholder: RAIQC Ltd. Dr Ashcaan Hajilou - Travel grant: Qure.ai Technologies. Prof Raymond Bond – No conflicts of interest to declare. Dr Patrick McAllister - No conflicts of interest to declare. Dr Nick Woznitza- Consultancy fees: InHealth, SM Radiology; Speakers fees: AstraZenica; Travel grant: Qure.ai Technologies., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated., Methods and Analysis: A dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image., Ethics and Dissemination: The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal., Trial Registration Numbers: This study is registered with ISRCTN (ISRCTN19562541) and ClinicalTrials.gov (NCT06130397). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2)., Competing Interests: Competing interests: JV and DJ of the Steering Committee are employees of Gleamer SAS, France. SA is a shareholder of RAIQC, UK. All other authors declare no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Introduction: Diagnostic imaging is vital in emergency departments (EDs). Accessibility and reporting impacts ED workflow and patient care. With radiology workforce shortages, reporting capacity is limited, leading to image interpretation delays. Turnaround times for image reporting are an ED bottleneck. Artificial intelligence (AI) algorithms can improve productivity, efficiency and accuracy in diagnostic radiology, contingent on their clinical efficacy. This includes positively impacting patient care and improving clinical workflow. The ACCEPT-AI study will evaluate Qure.ai's qER software in identifying and prioritising patients with critical findings from AI analysis of non-contrast head CT (NCCT) scans., Methods and Analysis: This is a multicentre trial, spanning four diverse sites, over 13 months. It will include all individuals above the age of 18 years who present to the ED, referred for an NCCT. The project will be divided into three consecutive phases (pre-implementation, implementation and post-implementation of the qER solution) in a stepped-wedge design to control for adoption bias and adjust for time-based changes in the background patient characteristics. Pre-implementation involves baseline data for standard care to support the primary and secondary outcomes. The implementation phase includes staff training and qER solution threshold adjustments in detecting target abnormalities adjusted, if necessary. The post-implementation phase will introduce a notification (prioritised flag) in the radiology information system. The radiologist can choose to agree with the qER findings or ignore it according to their clinical judgement before writing and signing off the report. Non-qER processed scans will be handled as per standard care., Ethics and Dissemination: The study will be conducted in accordance with the principles of Good Clinical Practice. The protocol was approved by the Research Ethics Committee of East Midlands (Leicester Central), in May 2023 (REC (Research Ethics Committee) 23/EM/0108). Results will be published in peer-reviewed journals and disseminated in scientific findings (ClinicalTrials.gov: NCT06027411) TRIAL REGISTRATION NUMBER: NCT06027411., Competing Interests: Competing interests: DR, AK and SK are Qure.ai employees. DJL receives funding from Qure.ai for an additional investigator-led grant., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice., Methods and Analysis: A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers' performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty., Ethics and Dissemination: The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal., Trial Registration Number: NCT06018545., Competing Interests: Competing interests: DR, SK and ST are employees of Qure AI. NW declares consultancy fees from InHealth and SM Radiology not related to the current submission. MH declares consultancy fees from Qure AI not related to the current submission. DJL declares an institution grant for additional research activity on a separate product unrelated to the current submission. AN declares another NHSX grant in collaboration with Qure AI for research unrelated to the current submission. SA declares grants from Qure AI for other research activity unrelated to the current submission., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The National Optimal Lung Cancer Pathway recommends rapid progression from abnormal chest X-rays (CXRs) to CT. The impact of the more rapid reporting on the whole pathway is unknown. The aim of this study was to determine the impact of immediate reporting of CXRs requested by primary care by radiographers on the time to diagnosis of lung cancer., Method: People referred for CXR from primary care to a single acute district general hospital in London attended sessions that were prerandomised to either immediate radiographer (IR) reporting or standard radiographer (SR) reporting within 24 hours. CXRs were subsequently reported by radiologists blind to the radiographer reports to test the reliability of the radiographer report. Radiographer and local radiologist discordant cases were reviewed by thoracic radiologists, blinded to reporter., Results: 8682 CXRs were performed between 21 June 2017 and 4 August 2018, 4096 (47.2%) for IR and 4586 (52.8%) for SR. Lung cancer was diagnosed in 49, with 27 (55.1%) for IR. The median time from CXR to diagnosis of lung cancer for IR was 32 days (IQR 19, 70) compared with 63 days (IQR 29, 78) for SR (p=0.03).8258 CXRs (95.1%) were reported by both radiographers and local radiologists. In the 1361 (16.5%) with discordance, the reviewing thoracic radiologists were equally likely to agree with local radiologist and radiographer reports., Conclusions: Immediate reporting of CXRs from primary care reduces time to diagnosis of lung cancer by half, likely due to rapid progress to CT. Radiographer reports are comparable to local radiologist reports for accuracy., Trial Registration: International Standard Randomised Controlled Trial Number ISRCTN21818068. Registered on 20 June 2017., Competing Interests: Competing interests: NW: declares consultancy fees for InHealth, SM Radiology, Collective Minds Radiology. SMJ: declares research grants from GRAIL, Owlstone, Cancer Research UK; personal fees from AstraZeneca, BARD1 Bioscience, Achilles Therapeutics, Jansen; Travel fees Takeda; Advisory board Optellum. This work was partly undertaken at UCLH/UCL who received a proportion of funding from the DoH NIHR Biomedical Research Centre’s funding scheme. BG, AD, KP, SR, AB, NH, DT, NA, ES, SG, BG, SS, SE, ZS, SM and SWD: nothing to declare. ITHA-Y: declares medicolegal work and private radiology practice fees. DB: declares personal fees from MSD, BMS, AstraZeneca and Roche., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Background: Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers., Methodology: A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis., Results: Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences., Conclusions: The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses., (© 2023. The Author(s).)

Objectives: Successful lung cancer screening delivery requires sensitive, timely reporting of low-dose computed tomography (LDCT) scans, placing a demand on radiology resources. Trained non-radiologist readers and computer-assisted detection (CADe) software may offer strategies to optimise the use of radiology resources without loss of sensitivity. This report examines the accuracy of trained reporting radiographers using CADe support to report LDCT scans performed as part of the Lung Screen Uptake Trial (LSUT)., Methods: In this observational cohort study, two radiographers independently read all LDCT performed within LSUT and reported on the presence of clinically significant nodules and common incidental findings (IFs), including recommendations for management. Reports were compared against a 'reference standard' (RS) derived from nodules identified by study radiologists without CADe, plus consensus radiologist review of any additional nodules identified by the radiographers., Results: A total of 716 scans were included, 158 of which had one or more clinically significant pulmonary nodules as per our RS. Radiographer sensitivity against the RS was 68-73.7%, with specificity of 92.1-92.7%. Sensitivity for detection of proven cancers diagnosed from the baseline scan was 83.3-100%. The spectrum of IFs exceeded what could reasonably be covered in radiographer training., Conclusion: Our findings highlight the complexity of LDCT reporting requirements, including the limitations of CADe and the breadth of IFs. We are unable to recommend CADe-supported radiographers as a sole reader of LDCT scans, but propose potential avenues for further research including initial triage of abnormal LDCT or reporting of follow-up surveillance scans., Key Points: • Successful roll-out of mass screening programmes for lung cancer depends on timely, accurate CT scan reporting, placing a demand on existing radiology resources. • This observational cohort study examines the accuracy of trained radiographers using computer-assisted detection (CADe) software to report lung cancer screening CT scans, as a potential means of supporting reporting workflows in LCS programmes. • CADe-supported radiographers were less sensitive than radiologists at identifying clinically significant pulmonary nodules, but had a low false-positive rate and good sensitivity for detection of confirmed cancers., (© 2022. The Author(s).)

Introduction: As a profession, radiographers have always been keen on adapting and integrating new technologies. The increasing integration of artificial intelligence (AI) into clinical practice in the last five years has been met with scepticism by some, who predict the demise of the profession, whilst others suggest a bright future with AI, full of opportunities and synergies. Post COVID-19 pandemic need for economic recovery and a backlog of medical imaging and reporting may accelerate the adoption of AI. It is therefore timely to appreciate practitioners' perceptions of AI used in clinical practice and their perception of the short-term impact on the profession., Aim: This study aims to explore the perceptions of AI in the UK radiography workforce and to investigate its current AI applications and future technological expectations of radiographers., Methods: An online survey (Qualtrics Ⓡ ) was created by a team of radiography AI experts. The survey was disseminated via social media and professional networks in the UK. Demographic information and perceptions of the impact of AI on several aspects of the radiography profession were gathered, including the current use of AI in practice, future expectations and the perceived impact of AI on the profession., Results: 411 responses were collected (80% diagnostic radiographers (DR); 20% therapeutic radiographers (TR)). Awareness of AI used in clinical practice is low, with DR respondents suggesting AI will have the most value/potential in cross sectional imaging and image reporting. TR responses linked AI as having most value in treatment planning, contouring, and image acquisition/matching. Respondents felt that AI will impact radiographers' daily work (DR, 79.6%; TR, 88.9%) by standardising some aspects of patient care and technical factors of radiography practice. A mixed response about impact on careers was reported., Conclusions: Respondents were unsure about the ways in which AI is currently used in practice and how AI will impact on careers in the future. It was felt that AI integration will lead to increased job opportunities to contribute to decision making as an end user. Job security was not identified as a cause for concern., (Copyright © 2022. Published by Elsevier Inc.)

Therapeutic radiographers (TRs) are well-placed to deliver advice to cancer patients; however, limited research exists on their practices in providing advice on healthy lifestyle behaviours. Through an online survey, this study aimed to explore TRs' current practices, barriers, and facilitators around delivering healthy behaviour advice to cancer patients. An online survey was sent to 72 radiotherapy departments in the UK and 583 TRs responded to the survey. Findings showed that levels of enquiry and provision of advice on healthy behaviours were low, with less than 25% advising patients on physical activity, healthy eating, weight management, smoking cessation, and reducing alcohol intake as standard practice. Lack of knowledge, resources, and training were identified as barriers, in addition to perceived lack of patient interest and lack of time. TRs reported a strong desire to undergo training to enable them to deliver health behaviour advice to patients, with an identified preference for online training. Cancer patients look to healthcare professionals for advice on health behaviours, and TRs are well-placed to deliver this advice. The findings of this study provide insight into the areas that need addressing to enable TRs to support positive health behaviours among cancer patients., (© 2020. The Author(s).)