Perspectives on enhancing clinical informatics education in the artificial intelligence era.

Journal of clinical informatics Pub Date : 2024-01-01 Epub Date: 2024-12-04
Hua Min, Xia Jing, Yang Gong, Ping Yu
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Abstract

Objectives: This paper aims to analyze clinical informatic (CI) - a subfield of biomedical and health informatics (BHI) - programs to identify challenges and provide solutions for CI education. Using an online clinical decision support system (CDSS) course as a case study, we demonstrate how these challenges can be addressed. In addition, we discuss the potential impact of generative artificial intelligence (AI), along with the opportunities and risks it presents for CI education.

Methods: This is a perspective paper. The viewpoint analysis is based on a review of formal academic and training programs offered by the American Medical Informatics Association (AMIA) Academic Forum members, Accreditation Council for Graduate Medical Education (ACGME)-accredited CI programs, current literature, and experiences and insights of the authors, who are all CI or BHI educators. An online CDSS course serves as a case study.

Results: We identified the following challenges in CI education: the absence of consensus on CI curriculum content, the diversity of student backgrounds, issues with timely and accurate evaluation of both teaching and learning, insufficient long-term mentoring, and the impact of new AI technologies like generative AI. We used an online CDSS course as an example to demonstrate the solutions in course design, textbook selection, teaching methods, and class project development. These solutions include developing standardized course content for the CI curriculum, incorporating group projects to accommodate diverse student backgrounds, implementing multi-level evaluations, providing ongoing mentoring and support, and cautiously integrating generative AI technologies.

Conclusions: This paper identifies challenges in CI education, shares practical solutions, and discusses the potential impact of generative AI, a double-edged sword for teaching and learning. It provides a foundation and practical reference for CI education, situating it within the broader context of BHI, its foundational discipline. We aim to achieve safer and better healthcare through CI education and practice.

人工智能时代加强临床信息学教育的几点思考
目的:本文旨在分析临床信息学(CI) -生物医学和健康信息学(BHI)的一个分支-计划,以确定CI教育的挑战并提供解决方案。使用在线临床决策支持系统(CDSS)课程作为案例研究,我们展示了如何解决这些挑战。此外,我们还讨论了生成式人工智能(AI)的潜在影响,以及它为CI教育带来的机遇和风险。方法:这是一篇透视论文。观点分析是基于对美国医学信息学协会(AMIA)学术论坛成员提供的正式学术和培训计划、研究生医学教育认证委员会(ACGME)认可的CI计划、当前文献以及作者的经验和见解的审查,他们都是CI或BHI教育者。一个在线CDSS课程可以作为一个案例研究。结果:我们确定了CI教育中的以下挑战:CI课程内容缺乏共识,学生背景的多样性,教学和学习的及时和准确评估问题,长期指导不足,以及新人工智能技术(如生成式人工智能)的影响。以CDSS在线课程为例,从课程设计、教材选择、教学方法、课堂项目开发等方面阐述了解决方案。这些解决方案包括为CI课程开发标准化的课程内容,结合小组项目以适应不同的学生背景,实施多层次评估,提供持续的指导和支持,以及谨慎地整合生成式人工智能技术。结论:本文指出了CI教育中的挑战,分享了实用的解决方案,并讨论了生成式人工智能的潜在影响,这是一把教学和学习的双刃剑。它为CI教育提供了基础和实践参考,将其置于其基础学科BHI的更广泛背景下。我们的目标是通过CI教育和实践实现更安全和更好的医疗保健。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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