Cost of simulation-based mastery learning for abdominal ultrasound

BACKGROUND: Ultrasound is an essential diagnostic examination used in several medical specialties. However, the quality of ultrasound examinations is dependent on mastery of certain skills, which may be difficult and costly to attain in the clinical setting. This study aimed to explore mastery learning for trainees practicing general abdominal ultrasound using a virtual reality simulator and to evaluate the associated cost per student achieving the mastery learning level.METHODS: Trainees were instructed to train on a virtual reality ultrasound simulator until the attainment of a mastery learning level was established in a previous study. Automated simulator scores were used to track performances during each round of training, and these scores were recorded to determine learning curves. Finally, the costs of the training were evaluated using a micro-costing procedure.RESULTS: Twenty-one out of the 24 trainees managed to attain the predefined mastery level two times consecutively. The trainees completed their training with a median of 2h38min (range: 1h20min-4h30min) using a median of 7 attempts (range: 3-11 attempts) at the simulator test. The cost of training one trainee to the mastery level was estimated to be USD 638.CONCLUSION: Complete trainees can obtain mastery learning levels in general abdominal ultrasound examinations within 3 hours of training in the simulated setting and at an average cost of USD 638 per trainee. Future studies are needed to explore how the cost of simulation-based training is best balanced against the costs of clinical training.</p

Simulation-based assessment of upper abdominal ultrasound skills

Background: Ultrasound is a safe and effective diagnostic tool used within several specialties. However, the quality of ultrasound scans relies on sufficiently skilled clinician operators. The aim of this study was to explore the validity of automated assessments of upper abdominal ultrasound skills using an ultrasound simulator. Methods: Twenty five novices and five experts were recruited, all of whom completed an assessment program for the evaluation of upper abdominal ultrasound skills on a virtual reality simulator. The program included five modules that assessed different organ systems using automated simulator metrics. We used Messick’s framework to explore the validity evidence of these simulator metrics to determine the contents of a final simulator test. We used the contrasting groups method to establish a pass/fail level for the final simulator test. Results: Thirty seven out of 60 metrics were able to discriminate between novices and experts (p &lt; 0.05). The median simulator score of the final simulator test including the metrics with validity evidence was 26.68% (range: 8.1–40.5%) for novices and 85.1% (range: 56.8–91.9%) for experts. The internal structure was assessed by Cronbach alpha (0.93) and intraclass correlation coefficient (0.89). The pass/fail level was determined to be 50.9%. This pass/fail criterion found no passing novices or failing experts. Conclusions: This study collected validity evidence for simulation-based assessment of upper abdominal ultrasound examinations, which is the first step toward competency-based training. Future studies may examine how competency-based training in the simulated setting translates into improvements in clinical performances.</p

Simulation-based assessment of robotic cardiac surgery skills: An international multicenter, cross-specialty trial

Objective: This study aimed to investigate the validity of simulation-based assessment of robotic-assisted cardiac surgery skills using a wet lab model, focusing on the use of a time-based score (TBS) and modified Global Evaluative Assessment of Robotic Skills (mGEARS) score. Methods: We tested 3 wet lab tasks (atrial closure, mitral annular stitches, and internal thoracic artery [ITA] dissection) with both experienced robotic cardiac surgeons and novices from multiple European centers. The tasks were assessed using 2 tools: TBS and mGEARS score. Reliability, internal consistency, and the ability to discriminate between different levels of competence were evaluated. Results: The results demonstrated a high internal consistency for all 3 tasks using mGEARS assessment tool. The mGEARS score and TBS could reliably discriminate between different levels of competence for the atrial closure and mitral stitches tasks but not for the ITA harvesting task. A generalizability study also revealed that it was feasible to assess competency of the atrial closure and mitral stitches tasks using mGEARS but not the ITA dissection task. Pass/fail scores were established for each task using both TBS and mGEARS assessment tools. Conclusions: The study provides sufficient evidence for using TBS and mGEARS scores in evaluating robotic-assisted cardiac surgery skills in wet lab settings for intracardiac tasks. Combining both assessment tools enhances the evaluation of proficiency in robotic cardiac surgery, paving the way for standardized, evidence-based preclinical training and credentialing. Clinical trial registry number: NCT05043064.</p

Theory-Based Approaches to Support Dermoscopic Image Interpretation Education: A Review of the Literature

Introduction: Efficient interpretation of dermoscopic images relies on pattern recognition, and the development of expert-level proficiency typically requires extensive training and years of practice. While traditional methods of transferring knowledge have proven effective, technological advances may significantly improve upon these strategies and better equip dermoscopy learners with the pattern recognition skills required for real-world practice. Objectives: A narrative review of the literature was performed to explore emerging directions in medical image interpretation education that may enhance dermoscopy education. This article represents the first of a two-part review series on this topic. Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collaboration (ISIC)assembled a 12-member Education Working Group that comprises international dermoscopy experts and educational scientists. Based on a preliminary literature review and their experiences as educators, the group developed and refined a list of innovative approaches through multiple rounds of discussion and feedback. For each approach, literature searches were performed for relevant articles. Results: Through a consensus-based approach, the group identified a number of emerging directions in image interpretation education. The following theory-based approaches will be discussed in this first part: whole-task learning, microlearning, perceptual learning, and adaptive learning. Conclusions: Compared to traditional methods, these theory-based approaches may enhance dermoscopy education by making learning more engaging and interactive and reducing the amount of time required to develop expert-level pattern recognition skills. Further exploration is needed to determine how these approaches can be seamlessly and successfully integrated to optimize dermoscopy education

Instructional Strategies to Enhance Dermoscopic Image Interpretation Education: a Review of the Literature

Introduction: In image interpretation education, many educators have shifted away from traditional methods that involve passive instruction and fragmented learning to interactive ones that promote active engagement and integrated knowledge. By training pattern recognition skills in an effective manner, these interactive approaches provide a promising direction for dermoscopy education. Objectives: A narrative review of the literature was performed to probe emerging directions in medical image interpretation education that may support dermoscopy education. This article represents the second of a two-part review series. Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collaboration (ISIC) assembled an Education Working Group that comprises international dermoscopy experts and educational scientists. Based on a preliminary literature review and their experiences as educators, the group developed and refined a list of innovative approaches through multiple rounds of discussion and feedback. For each approach, literature searches were performed for relevant articles. Results: Through a consensus-based approach, the group identified a number of theory-based approaches, as discussed in the first part of this series. The group also acknowledged the role of motivation, metacognition, and early failures in optimizing the learning process. Other promising teaching tools included gamification, social media, and perceptual and adaptive learning modules (PALMs). Conclusions: Over the years, many dermoscopy educators may have intuitively adopted these instructional strategies in response to learner feedback, personal observations, and changes in the learning environment. For dermoscopy training, PALMs may be especially valuable in that they provide immediate feedback and adapt the training schedule to the individual’s performance

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI