Articles: anesthesiology.
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The anaesthetic training programme in the United Kingdom (UK) spans over seven years and is overseen by the Royal College of Anaesthetists (RCOA). Junior doctors in England are currently striking amid ongoing pay negotiations with the government, and almost all junior doctors are worried about the cost of living. This article provides an overview of the average financial cost of training for doctors in the anaesthetic training programme. ⋯ The cost includes: student loan repayment (with interest rates), compulsory membership fees (including the Royal College of Anaesthetists and General Medical Council), postgraduate examinations (Fellowship of the Royal College of Anaesthetist exams are compulsory to complete training) and medical indemnity. The average trainee spends between 5.6% and 7.4% of their annual salary on non-reimbursable costs. This article delineates for aforementioned expenses and compares them with the training programs in Australia and New Zealand, given their status as frequent emigration destinations for UK doctors.
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The American Statistical Association has highlighted problems with null hypothesis significance testing and outlined alternative approaches that may 'supplement or even replace P-values'. One alternative is to report the false positive risk (FPR), which quantifies the chance the null hypothesis is true when the result is statistically significant. ⋯ PROSPERO (CRD42023350783).
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Acta Anaesthesiol Scand · Jan 2024
ReviewContinuing professional development (CPD) for anesthetists: A systematic review.
In accordance with the focus on patient safety and quality in healthcare, continuing professional development (CPD) has received increasing levels of attention as a means to ensure physicians maintain their clinical competencies and are fit to practice. There is some evidence of a beneficial effect of CPD, though few studies have evaluated its effect within anesthesia. The primary aim of this systematic review was to establish which CPD activities anesthetists are engaged in and their effectiveness. The secondary aim was to explore which methods are employed to evaluate anesthetists' clinical performance. ⋯ Anesthetists are engaged in a variety of CPD activities, with evidence of high levels of satisfaction and a positive learning effect. However, the impact on clinical practice and patient outcomes remains unclear and the role of assessment is less well-defined. There is a need for further, high-quality studies, evaluating a broader range of outcomes, in order to identify which methods are most effective to train and assess specialists in anesthesia.
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Diversion of controlled substances in the perioperative setting is an ongoing challenge, with consequences for patients, anesthesiologists, perioperative staff, and health care facilities alike. Perioperative environments are at high risk for diversion, since controlled substances are frequently handled in these settings, with varying levels of oversight. In this narrative review, we summarize strategies for preventing diversion of controlled substances in perioperative settings (i.e., operating rooms, endoscopy suites, and postanesthesia recovery units). ⋯ Although awareness of perioperative controlled substance diversion has been improving, there are too few data to suggest an optimal approach. Anesthesia departments will need to work collaboratively with hospital pharmacies and actively select strategies that are reasonable given local resources.
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Anesthesia and analgesia · Apr 2024
User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications.
Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians. ⋯ Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.