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Digital skills training in health is crucial to ensure that the health care workforce is equipped to leverage the potential of digital technologies in delivering efficient and effective care. Identifying existing training programs can be valuable to describe gaps and opportunities for acceleration in the digital age. This study aimed to map and assess existing continuous education and professional development training options in digital skills in health. As part of the EU-funded project entitled \"TRANSiTION - Digital Transition and Digital Resilience in Oncology,\" an expert-based approach was implemented to identify training programs in 14 European countries. The data were collected via an online survey that was developed for the purpose of this study and consisted of 23 questions categorized in 5 domains: general information, reaction, learning, behavior, and results. The analysis was performed using the 4-level Kirkpatrick model and the Digital Competence Framework for Citizens. The analysis of the data showed that 39.6% (19/48) of cases reported no official training in digital skills for the health care workforce, despite the fact that in 95.8% (45/47) of the cases digital solutions were used in the daily practice. Digital skills were a professional qualification in 31.3% (4.38/14) of the countries, and 32 out of 57 programs were provided by academic institutions. Half of the countries scored lower than the mean in the overall performance status according to the Kirkpatrick model, which reflects the gap in knowledge and skills of the workforce. Countries scored at the average or lower in all models' domains: reaction 92.9% (13/14), behavior 78.6% (11/14), learning 71.4% (10/14), and results 50% (7/14). The quality of the programs was poor, as less than half of the competencies were met, and the evaluation reflects the great need for improving health workforce education in digital skills and the application of health technologies in practice. There was variance in the availability and quality of digital skills training across Europe. The development of a comprehensive training program targeted to improve health care professionals' and health managers' knowledge and skills, as well as the incorporation of digital tools into practice, is crucial.

With the continuous advancement of medical technology and the frequent occurrence of disaster events, the training of health care workers in disaster nursing has become increasingly significant. However, traditional training methods often struggle to engage learners' interest and enthusiasm, making it challenging to simulate emergencies in real-life scenarios effectively. Gamification, as an innovative pedagogical approach that enhances the enjoyment and practicality of learning through the incorporation of game elements, has garnered considerable attention in the realm of disaster nursing education for health care workers in recent years. This review systematically evaluates its effectiveness and explores its advantages in improving training outcomes. This review aims to evaluate the effectiveness of gamified teaching methodologies in disaster nursing education and to identify the outcome of 16 indicators used in existing studies. This study was conducted following the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines, using the PICO-SD framework (Population, Intervention, Control, Outcome, Study Design) to establish the inclusion criteria. The researchers systematically searched 8 databases on February 10, 2025, including the Cochrane Library, PubMed, CINAHL (EBSCO), Embase, Web of Science, CNKI, Wanfang, and SCOPUS. Ultimately, 16 quasi-experimental studies investigating the application of gamified teaching in disaster nursing education were included in the analysis. For randomized controlled trials (RCTs), the Cochrane Risk of Bias Assessment Tool (RoB 2.0) was used for quality assessment; for quasi-experimental studies, the Joanna Briggs Institute Risk of Bias Tool for Non-Randomized Intervention Studies was used for methodological quality evaluation. Given the heterogeneity of study designs and the diversity of study indicators, this study used a narrative synthesis to integrate the findings. The studies included in this review comprised 1 RCT and 15 quasi-experimental designs. Six gamified formats exhibited positive outcomes. The effectiveness of these formats was assessed through various metrics, including theoretical knowledge (14 studies), practical skills (11 studies), learner satisfaction (9 studies), knowledge retention (4 studies), and self-efficacy (2 studies). All formats demonstrated improvements in knowledge and skills, with high levels of satisfaction reported. However, data on long-term retention were limited. Gamification teaching methods have shown significant potential to enhance core competencies such as emergency response, decision-making, and teamwork in disaster nursing education and have been effective in reinforcing learning engagement through elements such as cooperation, competition, scoring, and scenario simulation. However, there is a lack of standardized assessment frameworks for skill acquisition, longitudinal studies tracking behavior in real-life scenarios, and rigorous RCTs comparing it with traditional instruction. Although the existing evidence has not systematically confirmed its full effectiveness, based on the findings, this paper provides practical recommendations for developing and implementing gamified teaching strategies in disaster nursing education to enhance students' knowledge acquisition and practice.

Virtual reality (VR) simulators are increasingly used in dental education, offering advantages such as repeatable practice and immediate feedback. However, evidence comparing their efficacy to traditional phantom heads for veneer preparation training remains limited. This study aimed to compare the effectiveness of 2 widely used VR simulators (Unidental and Simodont) against traditional phantom heads for veneer tooth preparation training and evaluate the impact of training sequence (simulator-first vs phantom-head-first) on skill acquisition. A randomized controlled trial was conducted with 80 fourth-year dental students from Peking University School of Stomatology. Participants were stratified by gender and academic performance, then equally allocated to 8 groups. Groups 1-3 trained exclusively using Unidental, Simodont, or phantom heads, respectively, while groups 4-8 followed hybrid sequences combining simulator and phantom-head training. Each participant performed veneer preparations on a maxillary central incisor. Preparations were evaluated by a blinded instructor using a validated 100-point rubric assessing marginal integrity (30%), preparation depth (25%), proximal contour (25%), and surface smoothness (20%). Posttraining questionnaires (100-point scale) compared user perceptions of simulator realism, haptic feedback, and educational value. There were no statistically significant differences in the preparation quality among groups using different training methods (Unidental: 88.9, SD 3.6; Simodont: 88.6, SD 1.6; phantom heads: 89.4, SD 2.8; P=.81) or different training methodologies (simulator-first vs phantom-head-first) (simulator first: P=.18; phantom head first: P=.09, different sequences of Unidental: P=.16; different sequences of Simodont: P=.11). However, significant differences were observed between the evaluations of the 2 simulators in terms of realism of the odontoscope's reflection (Simodont: 55.6, SD 33.7; Unidental: 87.5, SD 13.9; P<.001), force feedback (Simodont: 66.2, SD 22.4; Unidental: 50.8, SD 18.9; P=.007), and simulation of the tooth preparation process (Simodont: 64.4, SD 16.0; Unidental: 50.6, SD 16.6; P=.003). Evaluation results showed no statistical differences between the 2 simulators in display effect (Simodont: 77.43, SD 21.58; Unidental: 71.68, SD 20.70; P=.24), synchronism of virtual and actual dental instruments (Simodont: 67.86, SD 19.31; Unidental: 59.29, SD 20.10; P=.11), and dental bur operation simulation (Simodont: 63.32, SD 19.99; Unidental: 55.79, SD 19.62; P=.16). The Unidental simulator was rated better than the Simodont simulator in terms of the realism of odontoscope's reflection. In all other aspects, Simodont was superior to Unidental. There was no significant difference in the students' attitudes towards the 2 simulators (improve skills: P=.19; inspire to learn: P=.29; will to use: P=.40; suitable for training: P=.39). The study found no significant differences in training outcomes between VR simulators and traditional phantom heads for veneer preparation, suggesting that VR technology may serve as a viable alternative or supplementary tool in dental education. However, the absence of significant differences does not imply equivalence, as formal equivalence testing was not performed. Future studies should incorporate equivalence testing and explore cost-effectiveness, long-term skill retention, and adaptability to complex clinical scenarios.

A significant gap exists in the knowledge and procedural skills of medical graduates when it comes to managing emergencies. In response, highly immersive virtual reality (VR)-based learning environments have been developed to train clinical competencies. However, robust evidence on how VR-based methods affect both short- and long-term learning outcomes, as well as physiological and perceived stress, remains limited. This study aimed to assess the effectiveness of VR-based simulation training, augmented with automated feedback, compared with video seminars at improving emergency medical competency among medical students. Furthermore, the study investigated the relationship between learning outcomes and physiological stress markers. The evaluation of participants' perceived stress and estimated learning success was also performed to provide a more comprehensive insight into VR's potential role in emergency training. In total, 72 senior medical students underwent VR-based emergency training (intervention) or viewed video seminars (control) on 2 topics (acute myocardial infarction and exacerbated chronic obstructive pulmonary disease) in an intraindividual crossover design. Levels of applied knowledge were assessed objectively by open-response tests pre- and postintervention and after 30 days. In addition, 2 electrodermal activity markers representing physiological stress response were measured during VR sessions using a wearable sensor. Participants also rated their estimated learning success and perceived stress. They also completed self-ratings of perceived stress and estimated learning success. Short-term knowledge gains were comparable between the VR (mean 26.6%, SD 15.3%) and control (mean 27.2%, SD 16%) condition. However, VR training produced significantly higher long-term knowledge gains (VR: mean 17.8%, SD 15.1% vs control: mean 11.9%, SD 18%; difference: -5.9, 95% CI -11.5 to -0.4). Overall retention scores were likewise higher for VR (mean 75.4%, SD 12.5%) than for video-based learning (mean 69.0%, SD 14.5%), a difference that was more pronounced in the myocardial infarction scenario. Participants rated the VR format as significantly more effective (mean 4.83, SD 0.41, on a 5-point scale) than the video seminar (mean 3.44, SD 1.00). While physiological stress markers increased during VR sessions, their correlation with knowledge gains was weak and negative. No significant relationship was detectable between perceived stress and objective learning outcomes. VR-based simulation training with automated feedback may offer long-term learning advantages over a traditional video seminar in emergency-medicine education. Given the time constraints and resource limitations of clinical education, self-moderated VR-based learning may represent a valuable addition to conventional training methods. Future research could investigate the learning effects of VR scenarios regarding the retention of practical skills, as well as the impact of repeated or team-based scenarios.

Digital tools, services, and information in patient care demand new competencies in outpatient care, and the workforce is faced with the need to deal with digitalization. In a targeted certificate course (Certification of Digitalization Officers in Medical Practices and Psychotherapeutic Practices, Digi-Manager), medical assistants are trained to serve as digitalization officers, enabling them to implement the requirements of digitalized health care within their practices. As part of an accompanying study, the course is evaluated by the participants, and the change in their digital literacy is recorded. We measured different knowledge, skills, and attitude dimensions at 3 different times-before, during, and after the course-and used ANOVA to examine significant changes. Digi-Managers started the course with an already high self-assessment of their digital literacy. Skills and knowledge increased significantly in all categories (cognitive, technical, ethical, and health information) from the initial to the final measurement, as did self-confidence in the use of general software and hardware. Positive attitude remained stable over the training period, and the course was rated very positively by participants across all areas. Training programs on digital topics for health care professionals are necessary, and this certification course is a role model for successful further education through a mixture of theoretical knowledge transfer and practical application. Especially, the use of a digital maturity model and a digital laboratory was a unique and useful feature. Further research needs to go into alternative assessment methods of digital literacy, as the results suggest that self-assessment measures self-efficacy and confidence, rather than pure competence. Nevertheless, the increase in self-assessed competence suggests that the training was successful.

Artificial intelligence advancements have enabled large language models to significantly impact radiology education and diagnostic accuracy. This study evaluates the performance of mainstream large language models, including GPT-4, Claude, Bard, Tongyi Qianwen, and Gemini Pro, in radiology board exams. A comparative analysis of 150 multiple-choice questions from radiology board exams without images was conducted. Models were assessed on their accuracy for text-based questions and were categorized by cognitive levels and medical specialties using χ2 tests and ANOVA. GPT-4 achieved the highest accuracy (83.3%, 125/150), significantly outperforming all other models. Specifically, Claude achieved an accuracy of 62% (93/150; P<.001), Bard 54.7% (82/150; P<.001), Tongyi Qianwen 70.7% (106/150; P=.009), and Gemini Pro 55.3% (83/150; P<.001). The odds ratios compared to GPT-4 were 0.33 (95% CI 0.18-0.60) for Claude, 0.24 (95% CI 0.13-0.44) for Bard, and 0.25 (95% CI 0.14-0.45) for Gemini Pro. Tongyi Qianwen performed relatively well with an accuracy of 70.7% (106/150; P=0.02) and had an odds ratio of 0.48 (95% CI 0.27-0.87) compared to GPT-4. Performance varied across question types and specialties, with GPT-4 excelling in both lower-order and higher-order questions, while Claude and Bard struggled with complex diagnostic questions. GPT-4 and Tongyi Qianwen show promise in medical education and training. The study emphasizes the need for domain-specific training datasets to enhance large language models' effectiveness in specialized fields like radiology.

Future health professionals, including dentists, must critically engage with digital health technologies to enhance patient care. While digital health is increasingly being integrated into the curricula of health professions, its interpretation varies widely depending on the discipline, health care setting, and local factors. This viewpoint proposes a structured set of domains to guide the designing of a digital health curriculum tailored to the unique needs of dentistry in Australia. The paper aims to share a premise for curriculum development that aligns with the current evidence and the national digital health strategy, serving as a foundation for further discussion and implementation in dental programs.

Artificial intelligence (AI) systems are becoming increasingly relevant in everyday clinical practice, with Food and Drug Administration-approved AI solutions now available in many specialties. This development has far-reaching implications for doctors and the future medical profession, highlighting the need for both practicing physicians and medical students to acquire the knowledge, skills, and attitudes necessary to effectively use and evaluate these technologies. Currently, however, there is limited experience with AI-focused curricular training and continuing education. This paper first introduces a novel blended learning curriculum including one module on AI for medical students in Germany. Second, this paper presents findings from a qualitative postcourse evaluation of students' knowledge and attitudes toward AI and their overall perception of the course. Clinical-year medical students can attend a 5-day elective course called \"Medicine in the Digital Age,\" which includes one dedicated AI module alongside 4 others on digital doctor-patient communication; digital health applications and smart devices; telemedicine; and virtual/augmented reality and robotics. After course completion, participants were interviewed in semistructured small group interviews. The interview guide was developed deductively from existing evidence and research questions compiled by our group. A subset of interview questions focused on students' knowledge, skills, and attitudes regarding medical AI, and their overall course assessment. Responses were analyzed using Mayring's qualitative content analysis. This paper reports on the subset of students' statements about their perception and attitudes toward AI and the elective's general evaluation. We conducted a total of 18 group interviews, in which all 35 (100%) participants (female=11, male=24) from 3 consecutive course runs participated. This produced a total of 214 statements on AI, which were assigned to the 3 main categories \"Areas of Application,\" \"Future Work,\" and \"Critical Reflection.\" The findings indicate that students have a nuanced and differentiated understanding of AI. Additionally, 610 statements concerned the elective's overall assessment, demonstrating great learning benefits and high levels of acceptance of the teaching concept. All 35 students would recommend the elective to peers. The evaluation demonstrated that the AI module effectively generates competences regarding AI technology, fosters a critical perspective, and prepares medical students to engage with the technology in a differentiated manner. The curriculum is feasible, beneficial, and highly accepted among students, suggesting it could serve as a teaching model for other medical institutions. Given the growing number and impact of medical AI applications, there is a pressing need for more AI-focused curricula and further research on their educational impact.

Medical education has undergone professionalization during the last decades, and internationally, educators are trained in specific medical education courses also known as \"train the trainer\" courses. As these courses have developed organically based on local needs, the lack of a general structure and terminology can confuse and hinder educators' information and development. The first aim of this study was to conduct a national search, analyze the findings, and provide a presentation of medical education courses based on international theoretical frameworks to support Swiss course providers and educators searching for courses. The second aim was to provide a blueprint for such a procedure to be used by the international audience. In this study, we devised a scholarly approach to sorting and presenting medical education courses to make their content accessible to medical educators. This approach is presented in detailed steps and our openly available exemplary database to make it serve as a blueprint for other settings. Following our constructivist paradigm, we examined content from medical education courses using a theory-informed inductive data approach. Switzerland served as an example, covering 4 languages and different approaches to medical education. Data were gathered through an online search and a nationwide survey with course providers. The acquired data and a concurrently developed keyword system to standardize course terminology are presented using Obsidian, a software that shows data networks. Our iterative search included several strategies (web search, survey, provider enquiry, and snowballing) and yielded 69 courses in 4 languages, with varying terminology, target audiences, and providers. The database of courses is interactive and openly accessible. An open-access template database structure is also available. This study proposes a novel method for sorting and visualizing medical education courses and the competencies they cover to provide an easy-to-use database, helping medical educators' practical and scholarly development. Notably, our analysis identified a specific emphasis on undergraduate teaching settings, potentially indicating a gap in postgraduate educational offerings. This aspect could be pivotal for future curriculum development and resource allocation. Our method might guide other countries and health care professions, offering a straightforward means of cataloging and making information about medical education courses widely available and promotable.

Quality clinical care is supported by effective patient-centered communication. Health care professionals can improve their communication skills through simulation-based training, but our knowledge about virtual simulation and its effectiveness and use in training health professionals and students is still growing rapidly. The objective of this study was to review the current academic literature to identify and evaluate the virtual simulation tools used to train communication skills in health care students and professionals. This review was carried out in June 2023 by collecting data from the MEDLINE/PubMed and Web of Science electronic databases. Once applicable studies were identified, we recorded data related to type of technology used, learning objectives, degree of learning autonomy, outcomes, and other details. We found 35 articles that had developed and/or applied a virtual environment for training communication skills aimed at patients, in which 24 different learning tools were identified. Most had been developed to independently train communication skills in English, either generally or in the specific context of medical history (anamnesis) interviews. Many of these tools used a virtual patient that looked like a person and had the ability to vocally respond. Almost half of the tools analyzed allowed the person being trained to respond orally using natural language. Of note, not all these studies described the technology they had used in detail. Many different learning tools with very heterogeneous characteristics are being used for the purposes of communication skills training. Continued research will still be required to develop virtual tools that include the most advanced features to achieve high-fidelity simulation training.