{"title":"Perspectives on enhancing clinical informatics education in the artificial intelligence era.","authors":"Hua Min, Xia Jing, Yang Gong, Ping Yu","doi":"","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":520440,"journal":{"name":"Journal of clinical informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11884742/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of clinical informatics","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/4 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.