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Design, Implementation, and Analysis of an Assessment and Accreditation Model to Evaluate a Digital Competence Framework for Health Professionals: Mixed Methods Study. 设计、实施和分析评估与认证模型,以评估卫生专业人员的数字能力框架:混合方法研究。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-10-17 DOI: 10.2196/53462
Francesc Saigí-Rubió, Teresa Romeu, Eulàlia Hernández Encuentra, Montse Guitert, Erik Andrés, Elisenda Reixach

Background: Although digital health is essential for improving health care, its adoption remains slow due to the lack of literacy in this area. Therefore, it is crucial for health professionals to acquire digital skills and for a digital competence assessment and accreditation model to be implemented to make advances in this field.

Objective: This study had two objectives: (1) to create a specific map of digital competences for health professionals and (2) to define and test a digital competence assessment and accreditation model for health professionals.

Methods: We took an iterative mixed methods approach, which included a review of the gray literature and consultation with local experts. We used the arithmetic mean and SD in descriptive statistics, P values in hypothesis testing and subgroup comparisons, the greatest lower bound in test diagnosis, and the discrimination index in study instrument analysis.

Results: The assessment model designed in accordance with the competence content defined in the map of digital competences and based on scenarios had excellent internal consistency overall (greatest lower bound=0.91). Although most study participants (110/122, 90.2%) reported an intermediate self-perceived digital competence level, we found that the vast majority would not attain a level-2 Accreditation of Competence in Information and Communication Technologies.

Conclusions: Knowing the digital competence level of health professionals based on a defined competence framework should enable such professionals to be trained and updated to meet real needs in their specific professional contexts and, consequently, take full advantage of the potential of digital technologies. These results have informed the Health Plan for Catalonia 2021-2025, thus laying the foundations for creating and offering specific training to assess and certify the digital competence of such professionals.

背景:尽管数字医疗对改善医疗保健至关重要,但由于缺乏这方面的知识,其应用仍然缓慢。因此,卫生专业人员必须掌握数字技能,并实施数字能力评估和认证模式,以推动这一领域的发展:本研究有两个目标:(1)为卫生专业人员绘制具体的数字能力地图;(2)确定并测试卫生专业人员的数字能力评估和认证模式:我们采用了迭代混合方法,包括查阅灰色文献和咨询当地专家。我们在描述性统计中使用算术平均数和标度,在假设检验和亚组比较中使用 P 值,在测试诊断中使用最大下限,在研究工具分析中使用区分度指数:根据数字能力地图中定义的能力内容设计的基于情景的评估模型总体上具有极好的内部一致性(最大下限=0.91)。虽然大多数研究参与者(110/122,90.2%)报告的自我认知数字能力水平处于中等水平,但我们发现绝大多数人无法达到信息和通信技术能力二级认证:在确定的能力框架基础上了解卫生专业人员的数字化能力水平,应能使这些专业人员得到培训和更新,以满足其特定专业背景下的实际需求,从而充分利用数字化技术的潜力。这些结果为《2021-2025 年加泰罗尼亚健康计划》提供了信息,从而为创建和提供专门培训以评估和认证此类专业人员的数字化能力奠定了基础。
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引用次数: 0
Medical Education and Artificial Intelligence: Web of Science-Based Bibliometric Analysis (2013-2022). 医学教育与人工智能:基于科学网的文献计量分析(2013-2022 年)》。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-10-10 DOI: 10.2196/51411
Shuang Wang, Liuying Yang, Min Li, Xinghe Zhang, Xiantao Tai

Background: Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted.

Objective: This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013-2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education.

Methods: Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer.

Results: A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was "Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey." Keyword analysis revealed that "radiology," "medical physics," "ehealth," "surgery," and "specialty" were the primary focus, whereas "big data" and "management" emerged as research frontiers.

Conclusions: The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions.

背景:人工智能(AI)技术的逐步发展促进了其与各学科的融合。特别是,将人工智能注入医学教育已成为一种重要趋势,并取得了值得关注的研究成果。因此,有必要对当前人工智能在医学教育中的研究情况进行全面回顾和分析:本研究旨在利用 CiteSpace 和 VOSviewer 对 2013-2022 年间的相关论文进行文献计量分析。本研究直观地反映了人工智能在医学教育中的现有研究现状和趋势:在 Web of Science 核心数据库中系统检索了 2013 年至 2022 年间发表的与人工智能和医学教育相关的文章。两名审稿人根据论文标题和摘要对初步检索到的论文进行了仔细检查,以剔除与主题无关的论文。然后使用 CiteSpace 和 VOSviewer 对所选论文进行分析,并对国家、机构、作者、参考文献和关键词进行可视化处理:从 2013 年到 2022 年,共发现 195 篇与医学教育中的人工智能相关的论文。随着时间的推移,每年发表的论文呈上升趋势。美国是这一研究领域最活跃的国家,哈佛医学院和多伦多大学是最活跃的机构。该领域的著名作者包括文森特-比索内特、夏洛特-布莱克特、罗兰多-F-德尔-梅斯特罗、尼科尔-莱多斯、尼坎-米尔奇、亚历山大-温克勒-施瓦茨和雷凯-伊拉马兹。引用率最高的论文是 "医学生对人工智能的态度:多中心调查"。关键词分析显示,"放射学"、"医学物理学"、"电子健康"、"外科 "和 "专科 "是主要关注点,而 "大数据 "和 "管理 "则成为研究前沿:本研究强调了人工智能在医学教育研究中的巨大潜力。目前的研究方向包括放射学、医学信息管理和其他方面。技术进步有望进一步拓宽这些方向。加强地区间合作、提高研究质量迫在眉睫。这些研究结果为研究人员提供了宝贵的见解,有助于他们确定研究视角并指导未来的研究方向。
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引用次数: 0
Navigating Nephrology's Decline Through a GPT-4 Analysis of Internal Medicine Specialties in the United States: Qualitative Study. 通过对美国内科专科的 GPT-4 分析了解肾脏病学的衰退:定性研究。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-10-10 DOI: 10.2196/57157
Jing Miao, Charat Thongprayoon, Oscar Garcia Valencia, Iasmina M Craici, Wisit Cheungpasitporn

Background: The 2024 Nephrology fellowship match data show the declining interest in nephrology in the United States, with an 11% drop in candidates and a mere 66% (321/488) of positions filled.

Objective: The study aims to discern the factors influencing this trend using ChatGPT, a leading chatbot model, for insights into the comparative appeal of nephrology versus other internal medicine specialties.

Methods: Using the GPT-4 model, the study compared nephrology with 13 other internal medicine specialties, evaluating each on 7 criteria including intellectual complexity, work-life balance, procedural involvement, research opportunities, patient relationships, career demand, and financial compensation. Each criterion was assigned scores from 1 to 10, with the cumulative score determining the ranking. The approach included counteracting potential bias by instructing GPT-4 to favor other specialties over nephrology in reverse scenarios.

Results: GPT-4 ranked nephrology only above sleep medicine. While nephrology scored higher than hospice and palliative medicine, it fell short in key criteria such as work-life balance, patient relationships, and career demand. When examining the percentage of filled positions in the 2024 appointment year match, nephrology's filled rate was 66%, only higher than the 45% (155/348) filled rate of geriatric medicine. Nephrology's score decreased by 4%-14% in 5 criteria including intellectual challenge and complexity, procedural involvement, career opportunity and demand, research and academic opportunities, and financial compensation.

Conclusions: ChatGPT does not favor nephrology over most internal medicine specialties, highlighting its diminishing appeal as a career choice. This trend raises significant concerns, especially considering the overall physician shortage, and prompts a reevaluation of factors affecting specialty choice among medical residents.

背景:2024 年肾脏病学研究员匹配数据显示,美国人对肾脏病学的兴趣在下降,候选人减少了 11%,仅有 66%(321/488)的职位被填补:本研究旨在利用领先的聊天机器人模型 ChatGPT 分析影响这一趋势的因素,以深入了解肾脏病学与其他内科专业的吸引力对比:该研究使用 GPT-4 模型对肾脏病学和其他 13 个内科专业进行了比较,根据智力复杂性、工作与生活的平衡、程序参与、研究机会、患者关系、职业需求和经济报酬等 7 项标准对每个专业进行了评估。每项标准的分数从 1 到 10 分不等,累计分数决定排名。该方法包括通过指示 GPT-4 在反向情景中偏向其他专科而非肾脏科来抵消潜在的偏见:结果:GPT-4 对肾脏病学的排名仅高于睡眠医学。虽然肾脏病学的得分高于临终关怀和姑息医学,但在工作与生活的平衡、患者关系和职业需求等关键标准上却不尽如人意。在考察 2024 年任命年匹配的职位填补率时,肾脏病学的填补率为 66%,仅高于老年医学的 45%(155/348)。肾脏内科在智力挑战和复杂性、程序参与、职业机会和需求、研究和学术机会以及经济补偿等 5 项标准中的得分下降了 4%-14%:结论:与大多数内科专科相比,ChatGPT 并不青睐肾脏病学,这表明肾脏病学作为一种职业选择的吸引力正在下降。这一趋势引起了人们的极大关注,特别是考虑到整体医生短缺的问题,并促使人们重新评估影响住院医生选择专业的因素。
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引用次数: 0
Integrating Digital Assistive Technologies Into Care Processes: Mixed Methods Study. 将数字辅助技术融入护理流程:混合方法研究。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-10-09 DOI: 10.2196/54083
Sebastian Hofstetter, Max Zilezinski, Dominik Behr, Bernhard Kraft, Christian Buhtz, Denny Paulicke, Anja Wolf, Christina Klus, Dietrich Stoevesandt, Karsten Schwarz, Patrick Jahn

Background: Current challenges in patient care have increased research on technology use in nursing and health care. Digital assistive technologies (DATs) are one option that can be incorporated into care processes. However, how the application of DATs should be introduced to nurses and care professionals must be clarified. No structured and effective education concepts for the patient-oriented integration of DATs in the nursing sector are currently available.

Objective: This study aims to examine how a structured and guided integration and education concept, herein termed the sensitization, evaluative introduction, qualification, and implementation (SEQI) education concept, can support the integration of DATs into nursing practices.

Methods: This study used an explanatory, sequential study design with a mixed methods approach. The SEQI intervention was run in 26 long-term care facilities oriented toward older adults in Germany after a 5-day training course in each. The participating care professionals were asked to test 1 of 6 DATs in real-world practice over 3 days. Surveys (n=112) were then administered that recorded the intention to use DATs at 3 measurement points, and guided qualitative interviews with care professionals (n=12) were conducted to evaluate the learning concepts and effects of the intervention.

Results: As this was a pilot study, no sample size calculation was carried out, and P values were not reported. The participating care professionals were generally willing to integrate DATs-as an additional resource-into nursing processes even before the 4-stage SEQI intervention was presented. However, the intervention provided additional background knowledge and sensitized care professionals to the digital transformation, enabling them to evaluate how DATs fit in the health care sector, what qualifies these technologies for correct application, and what promotes their use. The care professionals expressed specific ideas and requirements for both technology-related education concepts and nursing DATs.

Conclusions: Actively matching technical support, physical limitations, and patients' needs is crucial when selecting DATs and integrating them into nursing processes. To this end, using a structured process such as SEQI that strengthens care professionals' ability to integrate DATs can help improve the benefits of such technology in the health care setting. Practical, application-oriented learning can promote the long-term implementation of DATs.

背景:当前病人护理面临的挑战增加了护理和医疗保健技术应用方面的研究。数字辅助技术(DAT)是一种可纳入护理流程的选择。然而,必须明确如何向护士和护理专业人员介绍如何应用 DAT。目前,护理行业还没有以患者为导向整合 DATs 的结构化有效教育理念:本研究旨在探讨一种结构化和指导性的整合与教育理念(在此称为 "宣传、评估性介绍、鉴定和实施"(SEQI)教育理念)如何支持将 DATs 整合到护理实践中:本研究采用了解释性、顺序研究设计和混合方法。在德国的 26 家面向老年人的长期护理机构中,每家机构在接受了为期 5 天的培训后,都开展了 SEQI 干预活动。参加培训的护理专业人员被要求在 3 天的实际操作中测试 6 个 DAT 中的 1 个。然后进行了调查(112 人),记录了在 3 个测量点使用 DAT 的意向,并对护理专业人员(12 人)进行了定性访谈,以评估学习理念和干预效果:由于这是一项试点研究,因此没有计算样本量,也没有报告 P 值。参与研究的护理专业人员普遍愿意将 DATs 作为一种额外资源纳入护理流程,甚至在 4 阶段 SEQI 干预介绍之前就已如此。然而,干预措施提供了更多的背景知识,使护理专业人员对数字化转型更加敏感,使他们能够评估 DAT 在医疗保健领域的适应性、正确应用这些技术的条件以及促进其使用的因素。护理专业人员对与技术相关的教育理念和护理 DAT 表达了具体的想法和要求:结论:在选择 DAT 并将其融入护理流程时,积极匹配技术支持、身体限制和患者需求至关重要。为此,使用 SEQI 等结构化流程来加强护理专业人员整合 DAT 的能力,有助于提高此类技术在医疗环境中的效益。以实际应用为导向的学习可以促进 DAT 的长期实施。
{"title":"Integrating Digital Assistive Technologies Into Care Processes: Mixed Methods Study.","authors":"Sebastian Hofstetter, Max Zilezinski, Dominik Behr, Bernhard Kraft, Christian Buhtz, Denny Paulicke, Anja Wolf, Christina Klus, Dietrich Stoevesandt, Karsten Schwarz, Patrick Jahn","doi":"10.2196/54083","DOIUrl":"10.2196/54083","url":null,"abstract":"<p><strong>Background: </strong>Current challenges in patient care have increased research on technology use in nursing and health care. Digital assistive technologies (DATs) are one option that can be incorporated into care processes. However, how the application of DATs should be introduced to nurses and care professionals must be clarified. No structured and effective education concepts for the patient-oriented integration of DATs in the nursing sector are currently available.</p><p><strong>Objective: </strong>This study aims to examine how a structured and guided integration and education concept, herein termed the sensitization, evaluative introduction, qualification, and implementation (SEQI) education concept, can support the integration of DATs into nursing practices.</p><p><strong>Methods: </strong>This study used an explanatory, sequential study design with a mixed methods approach. The SEQI intervention was run in 26 long-term care facilities oriented toward older adults in Germany after a 5-day training course in each. The participating care professionals were asked to test 1 of 6 DATs in real-world practice over 3 days. Surveys (n=112) were then administered that recorded the intention to use DATs at 3 measurement points, and guided qualitative interviews with care professionals (n=12) were conducted to evaluate the learning concepts and effects of the intervention.</p><p><strong>Results: </strong>As this was a pilot study, no sample size calculation was carried out, and P values were not reported. The participating care professionals were generally willing to integrate DATs-as an additional resource-into nursing processes even before the 4-stage SEQI intervention was presented. However, the intervention provided additional background knowledge and sensitized care professionals to the digital transformation, enabling them to evaluate how DATs fit in the health care sector, what qualifies these technologies for correct application, and what promotes their use. The care professionals expressed specific ideas and requirements for both technology-related education concepts and nursing DATs.</p><p><strong>Conclusions: </strong>Actively matching technical support, physical limitations, and patients' needs is crucial when selecting DATs and integrating them into nursing processes. To this end, using a structured process such as SEQI that strengthens care professionals' ability to integrate DATs can help improve the benefits of such technology in the health care setting. Practical, application-oriented learning can promote the long-term implementation of DATs.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11499723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142393971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Gender and Sexuality Awareness in Medical Education and Practice: Mixed Methods Study. 缩小差距:关于医学教育与实践中的性别和性意识的混合方法研究》(Bridging the Gap: A Mixed-Method Study on Gender and Sexuality Awareness in Medical Education and Practice)。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-10-08 DOI: 10.2196/59009
Rola Khamisy-Farah, Eden Biras, Rabie Shehadeh, Ruba Tuma, Hisham Atwan, Anna Siri, Manlio Converti, Francesco Chirico, Łukasz Szarpak, Carlo Biz, Raymond Farah, Nicola Bragazzi

Background: The integration of gender and sexuality awareness in health care is increasingly recognized as vital for patient outcomes. Despite this, there is a notable lack of comprehensive data on the current state of physicians' training and perceptions in these areas, leading to a gap in targeted educational interventions and optimal health care delivery.

Objective: The study's aim was to explore the experiences and perceptions of attending and resident physicians regarding the inclusion of gender and sexuality content in medical school curricula and professional practice in Israel.

Methods: This cross-sectional survey targeted a diverse group of physicians across various specializations and experience levels. Distributed through Israeli Medical Associations and professional networks, it included sections on experiences with gender and sexuality content, perceptions of knowledge, the impact of medical school curricula on professional capabilities, and views on integrating gender medicine in medical education. Descriptive and correlational analyses, along with gender-based and medical status-based comparisons, were used, complemented, and enhanced by qualitative analysis of participants' replies.

Results: The survey, encompassing 189 respondents, revealed low-to-moderate exposure to gender and sexuality content in medical school curricula, with a similar perception of preparedness. A need for more comprehensive training was widely recognized. The majority valued training in these areas for enhancing professional capabilities, identifying 10 essential gender-related knowledge areas. The preference for integrating gender medicine throughout medical education was significant. Gender-based analysis indicated variations in exposure and perceptions.

Conclusions: The study highlights a crucial need for the inclusion of gender and sexuality awareness in medical education and practice. It suggests the necessity for curriculum development, targeted training programs, policy advocacy, mentorship initiatives, and research to evaluate the effectiveness of these interventions. The findings serve as a foundation for future directions in medical education, aiming for a more inclusive, aware, and prepared medical workforce.

背景:人们日益认识到,将性别意识和性意识纳入医疗保健对患者的治疗效果至关重要。尽管如此,有关医生在这些领域的培训和认知现状的综合数据却明显不足,导致在有针对性的教育干预和优化医疗服务方面存在差距:本研究旨在探讨以色列主治医师和住院医师对将性别和性行为内容纳入医学院课程和专业实践的经验和看法:这项横断面调查的对象是不同专业和经验水平的医生群体。调查通过以色列医学协会和专业网络进行,内容包括性别与性行为内容的经验、对知识的看法、医学院课程对专业能力的影响以及对将性别医学纳入医学教育的看法。我们采用了描述性和相关性分析,以及基于性别和医疗状况的比较,并对参与者的回答进行了定性分析,以补充和完善这些分析:结果:189 位受访者参与的调查显示,医学院课程中有关性别和性问题的内容较少,而且受访者对准备情况的看法也差不多。人们普遍认为需要进行更全面的培训。大多数人重视这些领域的培训以提高专业能力,并确定了十个与性别相关的基本知识领域。将性别医学纳入整个医学教育的倾向非常明显。基于性别的分析表明,在接触和认知方面存在差异:这项研究强调了将性别和性意识纳入医学教育和实践的迫切需要。研究表明,有必要进行课程开发、有针对性的培训计划、政策宣传、导师倡议以及评估这些干预措施有效性的研究。研究结果为医学教育的未来发展方向奠定了基础,旨在培养一支更具包容性、意识更强、准备更充分的医务人员队伍:
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引用次数: 0
Assessment of ChatGPT-4 in Family Medicine Board Examinations Using Advanced AI Learning and Analytical Methods: Observational Study. 使用先进的人工智能学习和分析方法评估全科医学考试中的 ChatGPT-4:观察研究。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-10-08 DOI: 10.2196/56128
Anthony James Goodings, Sten Kajitani, Allison Chhor, Ahmad Albakri, Mila Pastrak, Megha Kodancha, Rowan Ives, Yoo Bin Lee, Kari Kajitani

Background: This research explores the capabilities of ChatGPT-4 in passing the American Board of Family Medicine (ABFM) Certification Examination. Addressing a gap in existing literature, where earlier artificial intelligence (AI) models showed limitations in medical board examinations, this study evaluates the enhanced features and potential of ChatGPT-4, especially in document analysis and information synthesis.

Objective: The primary goal is to assess whether ChatGPT-4, when provided with extensive preparation resources and when using sophisticated data analysis, can achieve a score equal to or above the passing threshold for the Family Medicine Board Examinations.

Methods: In this study, ChatGPT-4 was embedded in a specialized subenvironment, "AI Family Medicine Board Exam Taker," designed to closely mimic the conditions of the ABFM Certification Examination. This subenvironment enabled the AI to access and analyze a range of relevant study materials, including a primary medical textbook and supplementary web-based resources. The AI was presented with a series of ABFM-type examination questions, reflecting the breadth and complexity typical of the examination. Emphasis was placed on assessing the AI's ability to interpret and respond to these questions accurately, leveraging its advanced data processing and analysis capabilities within this controlled subenvironment.

Results: In our study, ChatGPT-4's performance was quantitatively assessed on 300 practice ABFM examination questions. The AI achieved a correct response rate of 88.67% (95% CI 85.08%-92.25%) for the Custom Robot version and 87.33% (95% CI 83.57%-91.10%) for the Regular version. Statistical analysis, including the McNemar test (P=.45), indicated no significant difference in accuracy between the 2 versions. In addition, the chi-square test for error-type distribution (P=.32) revealed no significant variation in the pattern of errors across versions. These results highlight ChatGPT-4's capacity for high-level performance and consistency in responding to complex medical examination questions under controlled conditions.

Conclusions: The study demonstrates that ChatGPT-4, particularly when equipped with specialized preparation and when operating in a tailored subenvironment, shows promising potential in handling the intricacies of medical board examinations. While its performance is comparable with the expected standards for passing the ABFM Certification Examination, further enhancements in AI technology and tailored training methods could push these capabilities to new heights. This exploration opens avenues for integrating AI tools such as ChatGPT-4 in medical education and assessment, emphasizing the importance of continuous advancement and specialized training in medical applications of AI.

背景:本研究探讨了 ChatGPT-4 在通过美国全科医学委员会(ABFM)认证考试方面的能力。早期的人工智能(AI)模型在医学委员会考试中表现出局限性,本研究针对现有文献中的这一空白,评估了 ChatGPT-4 的增强功能和潜力,尤其是在文档分析和信息综合方面:主要目的是评估 ChatGPT-4 在获得大量备考资源和使用复杂数据分析的情况下,能否达到或超过全科医学考试合格分数线:在这项研究中,ChatGPT-4 被嵌入到一个专门的子环境 "人工智能全科医学委员会考试者 "中,该子环境的设计非常接近 ABFM 认证考试的条件。该子环境使人工智能能够访问和分析一系列相关的学习材料,包括一本主要的医学教科书和基于网络的补充资源。向人工智能提出了一系列 ABFM 类型的考试问题,反映了考试的典型广度和复杂性。重点是评估人工智能准确解释和回答这些问题的能力,在这个受控的子环境中利用其先进的数据处理和分析能力:在我们的研究中,我们对 ChatGPT-4 在 300 道 ABFM 考试练习题上的表现进行了量化评估。自定义机器人版本的人工智能正确回答率为 88.67%(95% CI 85.08%-92.25%),常规版本的正确回答率为 87.33%(95% CI 83.57%-91.10%)。包括 McNemar 检验(P=.45)在内的统计分析显示,两个版本的准确率没有显著差异。此外,错误类型分布的卡方检验(P=.32)显示,不同版本的错误模式没有明显差异。这些结果凸显了 ChatGPT-4 在可控条件下回答复杂体检问题时的高水平表现和一致性:本研究表明,ChatGPT-4,尤其是在配备了专业准备和在量身定制的子环境中运行时,在处理错综复杂的医学考试方面表现出了巨大的潜力。虽然它的表现与通过 ABFM 认证考试的预期标准相当,但人工智能技术和量身定制的培训方法的进一步提升可以将这些能力推向新的高度。这一探索为将 ChatGPT-4 等人工智能工具整合到医学教育和评估中开辟了道路,强调了在人工智能医学应用方面不断进步和专业培训的重要性。
{"title":"Assessment of ChatGPT-4 in Family Medicine Board Examinations Using Advanced AI Learning and Analytical Methods: Observational Study.","authors":"Anthony James Goodings, Sten Kajitani, Allison Chhor, Ahmad Albakri, Mila Pastrak, Megha Kodancha, Rowan Ives, Yoo Bin Lee, Kari Kajitani","doi":"10.2196/56128","DOIUrl":"10.2196/56128","url":null,"abstract":"<p><strong>Background: </strong>This research explores the capabilities of ChatGPT-4 in passing the American Board of Family Medicine (ABFM) Certification Examination. Addressing a gap in existing literature, where earlier artificial intelligence (AI) models showed limitations in medical board examinations, this study evaluates the enhanced features and potential of ChatGPT-4, especially in document analysis and information synthesis.</p><p><strong>Objective: </strong>The primary goal is to assess whether ChatGPT-4, when provided with extensive preparation resources and when using sophisticated data analysis, can achieve a score equal to or above the passing threshold for the Family Medicine Board Examinations.</p><p><strong>Methods: </strong>In this study, ChatGPT-4 was embedded in a specialized subenvironment, \"AI Family Medicine Board Exam Taker,\" designed to closely mimic the conditions of the ABFM Certification Examination. This subenvironment enabled the AI to access and analyze a range of relevant study materials, including a primary medical textbook and supplementary web-based resources. The AI was presented with a series of ABFM-type examination questions, reflecting the breadth and complexity typical of the examination. Emphasis was placed on assessing the AI's ability to interpret and respond to these questions accurately, leveraging its advanced data processing and analysis capabilities within this controlled subenvironment.</p><p><strong>Results: </strong>In our study, ChatGPT-4's performance was quantitatively assessed on 300 practice ABFM examination questions. The AI achieved a correct response rate of 88.67% (95% CI 85.08%-92.25%) for the Custom Robot version and 87.33% (95% CI 83.57%-91.10%) for the Regular version. Statistical analysis, including the McNemar test (P=.45), indicated no significant difference in accuracy between the 2 versions. In addition, the chi-square test for error-type distribution (P=.32) revealed no significant variation in the pattern of errors across versions. These results highlight ChatGPT-4's capacity for high-level performance and consistency in responding to complex medical examination questions under controlled conditions.</p><p><strong>Conclusions: </strong>The study demonstrates that ChatGPT-4, particularly when equipped with specialized preparation and when operating in a tailored subenvironment, shows promising potential in handling the intricacies of medical board examinations. While its performance is comparable with the expected standards for passing the ABFM Certification Examination, further enhancements in AI technology and tailored training methods could push these capabilities to new heights. This exploration opens avenues for integrating AI tools such as ChatGPT-4 in medical education and assessment, emphasizing the importance of continuous advancement and specialized training in medical applications of AI.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11479358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142393970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Psychological Safety Competency Training During the Clinical Internship From the Perspective of Health Care Trainee Mentors in 11 Pan-European Countries: Mixed Methods Observational Study. 从 11 个泛欧国家医疗保健实习生导师的角度看临床实习期间的心理安全能力培训:混合方法观察研究。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-10-07 DOI: 10.2196/64125
Irene Carrillo, Ivana Skoumalová, Ireen Bruus, Victoria Klemm, Sofia Guerra-Paiva, Bojana Knežević, Augustina Jankauskiene, Dragana Jocic, Susanna Tella, Sandra C Buttigieg, Einav Srulovici, Andrea Madarasová Gecková, Kaja Põlluste, Reinhard Strametz, Paulo Sousa, Marina Odalovic, José Joaquín Mira
<p><strong>Background: </strong>In the field of research, psychological safety has been widely recognized as a contributing factor to improving the quality of care and patient safety. However, its consideration in the curricula and traineeship pathways of residents and health care students is scarce.</p><p><strong>Objective: </strong>This study aims to determine the extent to which health care trainees acquire psychological safety competencies during their internships in clinical settings and identify what measures can be taken to promote their learning.</p><p><strong>Methods: </strong>A mixed methods observational study based on a consensus conference and an open-ended survey among a sample of health care trainee mentors from health care institutions in a pan-European context was conducted. First, we administered an ad hoc questionnaire to assess the perceived degree of acquisition or implementation and significance of competencies (knowledge, attitudes, and skills) and institutional interventions in psychological safety. Second, we asked mentors to propose measures to foster among trainees those competencies that, in the first phase of the study, obtained an average acquisition score of <3.4 (scale of 1-5). A content analysis of the information collected was carried out, and the spontaneity of each category and theme was determined.</p><p><strong>Results: </strong>In total, 173 mentors from 11 pan-European countries completed the first questionnaire (response rate: 173/256, 67.6%), of which 63 (36.4%) participated in the second consultation. The competencies with the lowest acquisition level were related to warning a professional that their behavior posed a risk to the patient, managing their possible bad reaction, and offering support to a colleague who becomes a second victim. The mentors' proposals for improvement of this competency gap referred to training in communication skills and patient safety, safety culture, work climate, individual attitudes, a reference person for trainees, formal incorporation into the curricula of health care degrees and specialization pathways, specific systems and mechanisms to give trainees a voice, institutional risk management, regulations, guidelines and standards, supervision, and resources to support trainees. In terms of teaching methodology, the mentors recommended innovative strategies, many of them based on technological tools or solutions, including videos, seminars, lectures, workshops, simulation learning or role-playing with or without professional actors, case studies, videos with practical demonstrations or model situations, panel discussions, clinical sessions for joint analysis of patient safety incidents, and debriefings to set and discuss lessons learned.</p><p><strong>Conclusions: </strong>This study sought to promote psychological safety competencies as a formal part of the training of future health care professionals, facilitating the translation of international guidelines into practice
背景:在研究领域,心理安全已被广泛认为是提高护理质量和患者安全的一个促进因素。然而,在住院医师和医护学生的课程和实习途径中很少考虑到这一点:本研究旨在确定医护受训人员在临床实习期间获得心理安全能力的程度,并确定可以采取哪些措施来促进他们的学习:方法: 我们在泛欧医疗机构的医疗实习生导师样本中开展了一项基于共识会议和开放式调查的混合方法观察研究。首先,我们发放了一份特别问卷,以评估心理安全方面的能力(知识、态度和技能)和机构干预措施的获得或实施程度及重要性。其次,我们要求辅导员提出措施,以培养学员在第一阶段研究中获得平均成果分的能力:共有来自 11 个泛欧洲国家的 173 名导师填写了第一份问卷(回复率:173/256,67.6%),其中 63 人(36.4%)参加了第二次咨询。掌握程度最低的能力涉及警告专业人员其行为对患者构成风险、处理他们可能出现的不良反应,以及为成为第二受害者的同事提供支持。导师们为改善这一能力差距而提出的建议包括:沟通技巧和患者安全方面的培训、安全文化、工作氛围、个人态度、受训者的参照人、正式纳入医疗学位和专业路径的课程、让受训者发表意见的具体制度和机制、机构风险管理、法规、指导方针和标准、监督以及支持受训者的资源。在教学方法方面,导师们推荐了一些创新策略,其中许多是基于技术工具或解决方案,包括视频、研讨会、讲座、讲习班、模拟学习或有专业演员或无专业演员的角色扮演、案例研究、带有实际演示或示范情况的视频、小组讨论、共同分析患者安全事件的临床会议,以及总结和讨论经验教训的汇报会:这项研究旨在促进心理安全能力,将其作为未来医疗保健专业人员培训的正式组成部分,从而推动将国际准则转化为泛欧洲范围内的实践和临床环境。
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引用次数: 0
Performance of ChatGPT on Nursing Licensure Examinations in the United States and China: Cross-Sectional Study. 中美护理执业资格考试 ChatGPT 成绩:横断面研究。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-10-03 DOI: 10.2196/52746
Zelin Wu, Wenyi Gan, Zhaowen Xue, Zhengxin Ni, Xiaofei Zheng, Yiyi Zhang
<p><strong>Background: </strong>The creation of large language models (LLMs) such as ChatGPT is an important step in the development of artificial intelligence, which shows great potential in medical education due to its powerful language understanding and generative capabilities. The purpose of this study was to quantitatively evaluate and comprehensively analyze ChatGPT's performance in handling questions for the National Nursing Licensure Examination (NNLE) in China and the United States, including the National Council Licensure Examination for Registered Nurses (NCLEX-RN) and the NNLE.</p><p><strong>Objective: </strong>This study aims to examine how well LLMs respond to the NCLEX-RN and the NNLE multiple-choice questions (MCQs) in various language inputs. To evaluate whether LLMs can be used as multilingual learning assistance for nursing, and to assess whether they possess a repository of professional knowledge applicable to clinical nursing practice.</p><p><strong>Methods: </strong>First, we compiled 150 NCLEX-RN Practical MCQs, 240 NNLE Theoretical MCQs, and 240 NNLE Practical MCQs. Then, the translation function of ChatGPT 3.5 was used to translate NCLEX-RN questions from English to Chinese and NNLE questions from Chinese to English. Finally, the original version and the translated version of the MCQs were inputted into ChatGPT 4.0, ChatGPT 3.5, and Google Bard. Different LLMs were compared according to the accuracy rate, and the differences between different language inputs were compared.</p><p><strong>Results: </strong>The accuracy rates of ChatGPT 4.0 for NCLEX-RN practical questions and Chinese-translated NCLEX-RN practical questions were 88.7% (133/150) and 79.3% (119/150), respectively. Despite the statistical significance of the difference (P=.03), the correct rate was generally satisfactory. Around 71.9% (169/235) of NNLE Theoretical MCQs and 69.1% (161/233) of NNLE Practical MCQs were correctly answered by ChatGPT 4.0. The accuracy of ChatGPT 4.0 in processing NNLE Theoretical MCQs and NNLE Practical MCQs translated into English was 71.5% (168/235; P=.92) and 67.8% (158/233; P=.77), respectively, and there was no statistically significant difference between the results of text input in different languages. ChatGPT 3.5 (NCLEX-RN P=.003, NNLE Theoretical P<.001, NNLE Practical P=.12) and Google Bard (NCLEX-RN P<.001, NNLE Theoretical P<.001, NNLE Practical P<.001) had lower accuracy rates for nursing-related MCQs than ChatGPT 4.0 in English input. English accuracy was higher when compared with ChatGPT 3.5's Chinese input, and the difference was statistically significant (NCLEX-RN P=.02, NNLE Practical P=.02). Whether submitted in Chinese or English, the MCQs from the NCLEX-RN and NNLE demonstrated that ChatGPT 4.0 had the highest number of unique correct responses and the lowest number of unique incorrect responses among the 3 LLMs.</p><p><strong>Conclusions: </strong>This study, focusing on 618 nursing MCQs including NCLEX-RN and
背景:创建像 ChatGPT 这样的大型语言模型(LLM)是人工智能发展的重要一步,由于其强大的语言理解和生成能力,在医学教育中显示出巨大的潜力。本研究旨在定量评估和全面分析 ChatGPT 在处理中国和美国全国护士执业资格考试(NNLE)试题(包括美国注册护士执业资格考试(NCLEX-RN)和全国护士执业资格考试)中的表现:本研究的目的是考察法学硕士对 NCLEX-RN 和 NNLE 多项选择题(MCQs)在不同语言输入中的反应。评估法学硕士是否可作为护理学的多语言学习辅助工具,并评估他们是否拥有适用于临床护理实践的专业知识库:首先,我们编制了 150 个 NCLEX-RN 实用 MCQs、240 个 NNLE 理论 MCQs 和 240 个 NNLE 实用 MCQs。然后,使用 ChatGPT 3.5 的翻译功能将 NCLEX-RN 问题从英文翻译成中文,将 NNLE 问题从中文翻译成英文。最后,将 MCQ 的原始版本和翻译版本分别输入 ChatGPT 4.0、ChatGPT 3.5 和 Google Bard。根据准确率对不同的 LLM 进行比较,并比较不同语言输入之间的差异:结果:ChatGPT 4.0 对 NCLEX-RN 实际问题和中文翻译的 NCLEX-RN 实际问题的准确率分别为 88.7%(133/150)和 79.3%(119/150)。尽管差异有统计学意义(P=.03),但正确率总体上令人满意。ChatGPT 4.0 正确回答了约 71.9%(169/235)的无学分制理论 MCQ 和 69.1%(161/233)的无学分制实践 MCQ。ChatGPT 4.0 处理翻译成英语的 NNLE 理论 MCQ 和 NNLE 实用 MCQ 的准确率分别为 71.5%(168/235;P=.92)和 67.8%(158/233;P=.77),不同语言文本输入的结果差异无统计学意义。ChatGPT 3.5(NCLEX-RN P=.003,NNLE 理论 PC 结论:本研究以包括 NCLEX-RN 和 NNLE 考试在内的 618 个护理 MCQ 为研究对象,发现 ChatGPT 4.0 的准确性优于 ChatGPT 3.5 和 Google Bard。它在处理英文和中文输入方面表现出色,突出了其作为护理教育和临床决策的重要工具的潜力。
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引用次数: 0
Bridging the Telehealth Digital Divide With Collegiate Navigators: Mixed Methods Evaluation Study of a Service-Learning Health Disparities Course. 通过大学生导航员弥合远程医疗数字鸿沟:服务学习型健康差异课程的混合方法评估研究。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-10-01 DOI: 10.2196/57077
Zakaria Nadeem Doueiri, Rika Bajra, Malathi Srinivasan, Erika Schillinger, Nancy Cuan

Background: Limited digital literacy is a barrier for vulnerable patients accessing health care.

Objective: The Stanford Technology Access Resource Team (START), a service-learning course created to bridge the telehealth digital divide, trained undergraduate and graduate students to provide hands-on patient support to improve access to electronic medical records (EMRs) and video visits while learning about social determinants of health.

Methods: START students reached out to 1185 patients (n=711, 60% from primary care clinics of a large academic medical center and n=474, 40% from a federally qualified health center). Registries consisted of patients without an EMR account (at primary care clinics) or patients with a scheduled telehealth visit (at a federally qualified health center). Patient outcomes were evaluated by successful EMR enrollments and video visit setups. Student outcomes were assessed by reflections coded for thematic content.

Results: Over 6 academic quarters, 57 students reached out to 1185 registry patients. Of the 229 patients contacted, 141 desired technical support. START students successfully established EMR accounts and set up video visits for 78.7% (111/141) of patients. After program completion, we reached out to 13.5% (19/141) of patients to collect perspectives on program utility. The majority (18/19, 94.7%) reported that START students were helpful, and 73.7% (14/19) reported that they had successfully connected with their health care provider in a digital visit. Inability to establish access included a lack of Wi-Fi or device access, the absence of an interpreter, and a disability that precluded the use of video visits. Qualitative analysis of student reflections showed an impact on future career goals and improved awareness of health disparities of technology access.

Conclusions: Of the patients who desired telehealth access, START improved access for 78.7% (111/141) of patients. Students found that START broadened their understanding of health disparities and social determinants of health and influenced their future career goals.

背景有限的数字知识是弱势患者获得医疗服务的障碍:斯坦福大学技术访问资源小组(START)是一门为弥合远程医疗数字鸿沟而开设的服务学习课程,它培训本科生和研究生为患者提供实践支持,以改善电子病历(EMR)和视频访问的访问,同时学习健康的社会决定因素:START 学生接触了 1185 名患者(其中 711 人,60% 来自大型学术医疗中心的初级保健诊所;474 人,40% 来自联邦合格保健中心)。登记包括没有 EMR 账户的患者(初级保健诊所)或已安排远程医疗就诊的患者(联邦合格医疗中心)。通过成功注册 EMR 和设置视频访问来评估患者的治疗效果。学生的成果通过对主题内容进行编码的反思进行评估:在 6 个学季中,57 名学生接触了 1185 名登记患者。在联系的 229 名患者中,有 141 人需要技术支持。START 学生成功建立了 EMR 账户,并为 78.7% 的患者(111/141)建立了视频访问。计划完成后,我们联系了 13.5%(19/141)的患者,收集他们对计划效用的看法。大多数患者(18/19,94.7%)表示 START 学生提供了帮助,73.7%(14/19)的患者表示他们在数字就诊中成功地与医疗服务提供者建立了联系。无法建立连接的原因包括缺乏 Wi-Fi 或设备连接、没有口译员以及残疾导致无法使用视频访问。对学生反思的定性分析显示,这对学生未来的职业目标产生了影响,并提高了他们对技术接入的健康差异的认识:结论:在希望获得远程医疗服务的患者中,START 改善了 78.7%(111/141)的患者获得远程医疗服务的机会。学生们发现,START 拓宽了他们对健康差距和健康的社会决定因素的理解,并影响了他们未来的职业目标。
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引用次数: 0
Knowledge Mapping and Global Trends in the Field of the Objective Structured Clinical Examination: Bibliometric and Visual Analysis (2004-2023). 客观结构化临床检查领域的知识图谱和全球趋势:文献计量和视觉分析(2004-2023 年)》。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-09-30 DOI: 10.2196/57772
Hongjun Ba, Lili Zhang, Xiufang He, Shujuan Li

Background: The Objective Structured Clinical Examination (OSCE) is a pivotal tool for assessing health care professionals and plays an integral role in medical education.

Objective: This study aims to map the bibliometric landscape of OSCE research, highlighting trends and key influencers.

Methods: A comprehensive literature search was conducted for materials related to OSCE from January 2004 to December 2023, using the Web of Science Core Collection database. Bibliometric analysis and visualization were performed with VOSviewer and CiteSpace software tools.

Results: Our analysis indicates a consistent increase in OSCE-related publications over the study period, with a notable surge after 2019, culminating in a peak of activity in 2021. The United States emerged as a significant contributor, responsible for 30.86% (1626/5268) of total publications and amassing 44,051 citations. Coauthorship network analysis highlighted robust collaborations, particularly between the United States and the United Kingdom. Leading journals in this domain-BMC Medical Education, Medical Education, Academic Medicine, and Medical Teacher-featured the highest volume of papers, while The Lancet garnered substantial citations, reflecting its high impact factor (to be verified for accuracy). Prominent authors in the field include Sondra Zabar, Debra Pugh, Timothy J Wood, and Susan Humphrey-Murto, with Ronaldo M Harden, Brian D Hodges, and George E Miller being the most cited. The analysis of key research terms revealed a focus on "education," "performance," "competence," and "skills," indicating these are central themes in OSCE research.

Conclusions: The study underscores a dynamic expansion in OSCE research and international collaboration, spotlighting influential countries, institutions, authors, and journals. These elements are instrumental in steering the evolution of medical education assessment practices and suggest a trajectory for future research endeavors. Future work should consider the implications of these findings for medical education and the potential areas for further investigation, particularly in underrepresented regions or emerging competencies in health care training.

背景:客观结构化临床考试(OSCE)是评估医疗保健专业人员的重要工具,在医学教育中发挥着不可或缺的作用:本研究旨在绘制 OSCE 研究的文献计量图,突出研究趋势和主要影响因素:方法:使用 Web of Science Core Collection 数据库对 2004 年 1 月至 2023 年 12 月期间与 OSCE 相关的资料进行了全面的文献检索。使用 VOSviewer 和 CiteSpace 软件工具进行了文献计量分析和可视化:我们的分析表明,在研究期间,与欧安组织相关的出版物持续增加,2019 年后明显激增,2021 年达到活动高峰。美国是重要的贡献者,发表的论文占论文总数的30.86%(1626/5268),引用次数达44051次。合作网络分析凸显了强大的合作关系,尤其是美国和英国之间的合作。该领域的主要期刊--《BMC Medical Education》、《Medical Education》、《Academic Medicine》和《Medical Teacher》--发表的论文数量最多,而《柳叶刀》则获得了大量引用,反映出其影响因子较高(准确性有待核实)。该领域的著名作者包括 Sondra Zabar、Debra Pugh、Timothy J Wood 和 Susan Humphrey-Murto,其中 Ronaldo M Harden、Brian D Hodges 和 George E Miller 的论文被引用次数最多。对关键研究术语的分析表明,研究重点是 "教育"、"表现"、"能力 "和 "技能",这表明这些是欧安组织研究的核心主题:本研究强调了欧安组织研究和国际合作的动态扩展,突出了有影响力的国家、机构、作者和期刊。这些因素有助于引导医学教育评估实践的发展,并为未来的研究工作指明了方向。未来的工作应考虑这些发现对医学教育的影响以及进一步研究的潜在领域,尤其是在代表性不足的地区或医疗培训的新兴能力方面。
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JMIR Medical Education
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