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Introduction The general consensus regarding the non-operative management of thoracolumbar (TL) spine fractures revolves around the use of thoracolumbar spine orthosis (TLSO). The efficacy of TLSO bracing remains controversial within the current literature, with several studies showing that prolonged brace use is associated with diminished lung capacity, skin breakdown, and paraspinal muscular atrophy, with no significant difference in pain and functional outcomes between patients treated with or without TLSO. Aims The aim of this study was to identify the number of braces issued over the duration of the study and understand the cost implication, added length of stay, and patient satisfaction based on a questionnaire and reflect on whether we need to change our practice on the use of TLSO. Methods Data was collected retrospectively over an 18-month period with 75 patients being initially identified for the study. A total of 42 records were included in the final study after the application of inclusion/exclusion criteria. Patient-related outcomes were recorded through a questionnaire. Results Of the patients, 60% report not receiving adequate advice regarding the duration of treatment, 43% reported that the brace interfered with their activities of daily living (ADLs), and 73% came off the brace earlier than advised, with 60% of patients reporting that they would rather be without the brace if given the option. The average increase in length of stay waiting for bracing was three days, with the estimated cost incurred being approximately £114,000. Conclusion With equivalence between treatment with or without a brace, there is a need to rethink current practice and move toward a case-by-case, patient-centered approach to minimize costs and improve patient satisfaction.
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BACKGROUND: Artificial intelligence (AI) technologies are increasingly used in clinical practice. Although there is robust evidence that AI innovations can improve patient care, reduce clinicians' workload and increase efficiency, their impact on medical training and education remains unclear. METHODS: A survey of trainee doctors' perceived impact of AI technologies on clinical training and education was conducted at UK NHS postgraduate centers in London between October and December 2020. Impact assessment mirrored domains in training curricula such as 'clinical judgement', 'practical skills' and 'research and quality improvement skills'. Significance between Likert-type data was analysed using Fisher's exact test. Response variations between clinical specialities were analysed using k-modes clustering. Free-text responses were analysed by thematic analysis. RESULTS: Two hundred ten doctors responded to the survey (response rate 72%). The majority (58%) perceived an overall positive impact of AI technologies on their training and education. Respondents agreed that AI would reduce clinical workload (62%) and improve research and audit training (68%). Trainees were skeptical that it would improve clinical judgement (46% agree, p = 0.12) and practical skills training (32% agree, p < 0.01). The majority reported insufficient AI training in their current curricula (92%), and supported having more formal AI training (81%). CONCLUSIONS: Trainee doctors have an overall positive perception of AI technologies' impact on clinical training. There is optimism that it will improve 'research and quality improvement' skills and facilitate 'curriculum mapping'. There is skepticism that it may reduce educational opportunities to develop 'clinical judgement' and 'practical skills'. Medical educators should be mindful that these domains are protected as AI develops. We recommend that 'Applied AI' topics are formalized in curricula and digital technologies leveraged to deliver clinical education.
