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Introduction: Simulation-based training (SBT) aids healthcare providers in acquiring the technical skills necessary to improve patient outcomes and safety. However, since SBT may require significant resources, training all skills to a comparable extent is impractical. Hence, a strategic prioritization of technical skills is necessary. While the European Training Requirements in Neonatology provide guidance on necessary skills, they lack prioritization. We aimed to identify and prioritize technical skills for a SBT curriculum in neonatology. Methods: A three-round modified Delphi process of expert neonatologists and neonatal trainees was performed. In round one, the participants listed all the technical skills newly trained neonatologists should master. The content analysis excluded duplicates and non-technical skills. In round two, the Copenhagen Academy for Medical Education and Simulation Needs Assessment Formula (CAMES-NAF) was used to preliminarily prioritize the technical skills according to frequency, importance of competency, SBT impact on patient safety, and feasibility for SBT. In round three, the participants further refined and reprioritized the technical skills. Items achieving consensus (agreement of ≥75%) were included. Results: We included 168 participants from 10 European countries. The response rates in rounds two and three were 80% (135/168) and 87% (117/135), respectively. In round one, the participants suggested 1964 different items. Content analysis revealed 81 unique technical skills prioritized in round two. In round three, 39 technical skills achieved consensus and were included. Conclusion: We reached a European consensus on a prioritized list of 39 technical skills to be included in a SBT curriculum in neonatology. [ABSTRACT FROM AUTHOR]

This study aimed to develop a deep learning model to assess the quality of fetal echocardiography and to perform prospective clinical validation. The model was trained on data from the 18–22-week anomaly scan conducted in seven hospitals from 2008 to 2018. Prospective validation involved 100 patients from two hospitals. A total of 5363 images from 2551 pregnancies were used for training and validation. The model's segmentation accuracy depended on image quality measured by a quality score (QS). It achieved an overall average accuracy of 0.91 (SD 0.09) across the test set, with images having above-average QS scoring 0.97 (SD 0.03). During prospective validation of 192 images, clinicians rated 44.8% (SD 9.8) of images as equal in quality, 18.69% (SD 5.7) favoring auto-captured images and 36.51% (SD 9.0) preferring manually captured ones. Images with above average QS showed better agreement on segmentations (p < 0.001) and QS (p < 0.001) with fetal medicine experts. Auto-capture saved additional planes beyond protocol requirements, resulting in more comprehensive echocardiographies. Low QS had adverse effect on both model performance and clinician's agreement with model feedback. The findings highlight the importance of developing and evaluating AI models based on 'noisy' real-life data rather than pursuing the highest accuracy possible with retrospective academic-grade data. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) is becoming increasingly used in medical education, but our understanding of the validity of AI‐based assessments (AIBA) as compared with traditional clinical expert‐based assessments (EBA) is limited. In this study, the authors aimed to compare and contrast the validity evidence for the assessment of a complex clinical skill based on scores generated from an AI and trained clinical experts, respectively. Methods: The study was conducted between September 2020 to October 2022. The authors used Kane's validity framework to prioritise and organise their evidence according to the four inferences: scoring, generalisation, extrapolation and implications. The context of the study was chorionic villus sampling performed within the simulated setting. AIBA and EBA were used to evaluate performances of experts, intermediates and novice based on video recordings. The clinical experts used a scoring instrument developed in a previous international consensus study. The AI used convolutional neural networks for capturing features on video recordings, motion tracking and eye movements to arrive at a final composite score. Results: A total of 45 individuals participated in the study (22 novices, 12 intermediates and 11 experts). The authors demonstrated validity evidence for scoring, generalisation, extrapolation and implications for both EBA and AIBA. The plausibility of assumptions related to scoring, evidence of reproducibility and relation to different training levels was examined. Issues relating to construct underrepresentation, lack of explainability, and threats to robustness were identified as potential weak links in the AIBA validity argument compared with the EBA validity argument. Conclusion: There were weak links in the use of AIBA compared with EBA, mainly in their representation of the underlying construct but also regarding their explainability and ability to transfer to other datasets. However, combining AI and clinical expert‐based assessments may offer complementary benefits, which is a promising subject for future research. Is artificial intelligence (AI) ready to replace clinicians in clinical skills assessment? In this paper, Johnsson et al. use Kane's framework to compare validity evidence between the two. [ABSTRACT FROM AUTHOR]

Introduction: Dyad learning occurs when two students work together to acquire new skills and knowledge. Several studies have provided evidence to support the educational rationale for dyad learning in the controlled simulated setting. However, the role of dyad learning in the clinical setting remains uncertain. Unlike the simulated setting, learning in the clinical setting depends on a complex interplay between medical students, doctors, nurses and patients potentially making dyad learning less valuable in clerkships. The objective of this study was to explore how key stakeholders perceive the value of implementing dyad learning during medical students' clinical clerkships. Methods: In a constructivist qualitative study, we conducted 51 semi‐structured interviews with 36 key stakeholders involved in dyad learning, including 10 medical students, 12 doctors, five nurses and nine patients. Data were coded inductively using thematic analysis, then coded deductively using stakeholder theory as a theoretical framework. Results: We found that stakeholders generally perceived the educational impact of dyad learning in the clinical setting similarly but disagreed on its value. Students emphasised that dyad learning made them participate more actively during patient encounters and patients did not mind having two students present. Doctors and nurses considered dyad learning disruptive to the balance between service and training and reported that it did not resonate with their perception of good patient care. Conclusion: Dyad learning enables students to be more active during their clinical clerkships, but it easily disrupts the balance between service and training. This disruption may be exacerbated by the shifted balance in priorities and values between different stakeholder groups, as well as by making implicit teaching obligations more explicit for supervising doctors and nurses. Consequently, implementing dyad learning may not be perceived as valuable by doctors and nurses in the clinical setting, regardless of its pedagogical rationale. The authors demonstrate that collaborative learning between pairs of medical students is perceived and valued differently by different stakeholders in the clinical setting, particularly with respect to tge patient perspective. [ABSTRACT FROM AUTHOR]

Data science is an inter-disciplinary field that uses computer-based algorithms and methods to gain insights from large and often complex datasets. Data science, which includes Artificial Intelligence techniques such as Machine Learning (ML), has been credited with the promise to transform Health Professions Education (HPE) by offering approaches to handle big (and often messy) data. To examine this promise, we conducted a critical review to explore: (1) published applications of data science and ML in HPE literature and (2) the potential role of data science and ML in shifting theoretical and epistemological perspectives in HPE research and practice. Existing data science studies in HPE are often not informed by theory, but rather oriented towards developing applications for specific problems, uses, and contexts. The most common areas currently being studied are procedural (e.g., computer-based tutoring or adaptive systems and assessment of technical skills). We found that epistemic beliefs informing the use of data science and ML in HPE poses a challenge for existing views on what constitutes objective knowledge and the role of human subjectivity for instruction and assessment. As a result, criticisms have emerged that the integration of data science in the field of HPE is in danger of becoming technically driven and narrowly focused in its approach to teaching, learning and assessment. Our findings suggest that researchers tend to formalize around the epistemological stance driven largely by traditions of a research paradigm. Future data science studies in HPE need to involve both education scientists and data scientists to ensure mutual advancements in the development of educational theory and practical applications. This may be one of the most important tasks in the integration of data science and ML in HPE research in the years to come. [ABSTRACT FROM AUTHOR]

Objectives This study is designed to provide an overview of why, how, when and for whom collaborative learning of clinical skills may work in health professions education. Why Collaborative learning of clinical skills may influence learning positively according to the non-medical literature. Training efficiency may therefore be improved if the outcomes of collaborative learning of clinical skills are superior or equivalent to those attained through individual learning. How According to a social interaction perspective, collaborative learning of clinical skills mediates its effects through social interaction, motivation, accountability and positive interdependence between learners. Motor skills learning theory suggests that positive effects rely on observational learning and action imitation, and negative effects may include decreased hands-on experience. Finally, a cognitive perspective suggests that learning is dependent on cognitive co-construction, shared knowledge and reduced cognitive load. When and for whom The literature on the collaborative learning of clinical skills in health science education is reviewed to support or contradict the hypotheses provided by the theories outlined above. Collaborative learning of clinical skills leads to improvements in self-efficacy, confidence and performance when task processing is observable or communicable. However, the effects of collaborative learning of clinical skills may decrease over time as benefits in terms of shared cognition, scaffolding and cognitive co-construction are outweighed by reductions in hands-on experience and time on task. Conclusions Collaborative learning of clinical skills has demonstrated promising results in the simulated setting. However, further research into how collaborative learning of clinical skills may work in clinical settings, as well as into the role of social dynamics between learners, is required. [ABSTRACT FROM AUTHOR]

The author discusses the costs and economic aspects of medical education. Topics include the costs to stakeholders such as government, students, and educational institutions associated with failure in health profession education, the use of summative evaluations to determine whether educational programs work and formative evaluations to determine how to improve educational programs, and the consideration of a program's value when performing a cost analysis.