AccScience Publishing / JCI / Online First / DOI: 10.36922/jci.4496
Submit to JCI
Cite this article
89
Download
1793
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
PERSPECTIVE ARTICLE

Perspectives on enhancing clinical informatics education in the artificial intelligence era

Hua Min1* ,  Xia Jing2 ,  Yang Gong3 ,  Ping Yu4
Show Less
1 Department of Health Administration and Policy, College of Public Health, George Mason University, Fairfax, Virginia, United States of America
2 Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, South Carolina, United States of America
3 Department of Clinical and Health Informatics, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
4 Centre for Digital Health Transformation, School of Computing and Information Technology, University of Wollongong, Wollongong, Australia
JCI 2025, 1(1), 4496 https://doi.org/10.36922/jci.4496
Received: 12 August 2024 | Accepted: 11 November 2024 | Published online: 4 December 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
XML
Cite
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.

Keywords
Clinical informatics
Education
Competency
Clinical decision support systems
Generative artificial intelligence
Challenges and opportunities
Funding
The project is partially supported by a grant from the United States National Institutes of Health (R01GM138589, 3R01GM138589-03S1).
Conflict of interest
Yang Gong is the Editor-in-Chief, and Hua Min and Xia Jing are Editorial Board Members of this journal, but were not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
References
  1. Ball MJ, Hannah KJ, Cortes-Comerer N, Douglas JV. Editorial: The health informatics series: Evolving with a new discipline. Int J Med Inf. 2023;173:105008. doi: 10.1016/j.ijmedinf.2023.105008

 

  1. Gardner RM, Overhage JM, Steen EB, et al. Core content for the subspecialty of clinical informatics. J Am Med Inform Assoc. 2009;16(2):153-157. doi: 10.1197/jamia.M3045

 

  1. Campbell R. The five “rights” of clinical decision support. J AHIMA. 2013;84(10):42-47; quiz 48.

 

  1. Shah C, Davtyan K, Nasrallah I, Bryan RN, Mohan S. Artificial intelligence-powered clinical decision support and simulation platform for radiology trainee education. J Digit Imaging. 2022;36(1):11-16. doi: 10.1007/s10278-022-00713-9

 

  1. Wulff A, Montag S, Marschollek M, Jack T. Clinical decision-support systems for detection of systemic inflammatory response syndrome, sepsis, and septic shock in critically ill patients: A systematic review. Methods Inf Med. 2019;58(S 02):e43-e57. doi: 10.1055/s-0039-1695717

 

  1. Gavrielides MA, Miller M, Hagemann IS, et al. Clinical decision support for ovarian carcinoma subtype classification: A pilot observer study with pathology trainees. Arch Pathol Lab Med. 2020;144(7):869-877. doi: 10.5858/arpa.2019-0390-OA

 

  1. Jung H, Park HA. Development and evaluation of a prototype CDSS for fall prevention. Stud Health Technol Inform. 2019;264:1700-1701. doi: 10.3233/SHTI190604

 

  1. Capan M, Schubel LC, Pradhan I, et al. Display and perception of risk: Analysis of decision support system display and its impact on perceived clinical risk of sepsis-induced health deterioration. Health Informatics J. 2022;28(1):146045822110730. doi: 10.1177/14604582211073075

 

  1. Shebl NA, Franklin BD, Barber N. Clinical decision support systems and antibiotic use. Pharm World Sci. 2007;29(4): 342-349. doi: 10.1007/s11096-007-9113-3

 

  1. De Wildt KK, Van De Loo B, Linn AJ, et al. Effects of a clinical decision support system and patient portal for preventing medication-related falls in older fallers: Protocol of a cluster randomized controlled trial with embedded process and economic evaluations (ADFICE_IT). PLoS One. 2023;18(9):e0289385. doi: 10.1371/journal.pone.0289385

 

  1. Souza-Pereira L, Pombo N, Ouhbi S, Felizardo V, Garcia N. Clinical decision support systems for chronic diseases: A systematic literature review. Comput Methods Programs Biomed. 2020;195:105565. doi: 10.1016/j.cmpb.2020.105565

 

  1. Lingham V, Chandwarkar A, Miller M, et al. A systematic approach to the design and implementation of clinical informatics fellowship programs. Appl Clin Inform. 2023;14(5):951-960. doi: 10.1055/s-0043-1776404

 

  1. Valenta AL, Berner ES, Boren SA, et al. AMIA board white paper: AMIA 2017 core competencies for applied health informatics education at the master’s degree level. J Am Med Inform Assoc. 2018;25(12):1657-1668. doi: 10.1093/jamia/ocy132

 

  1. Khairat S, Feldman SS, Rana A, et al. Foundational domains and competencies for baccalaureate health informatics education. J Am Med Inform Assoc. 2023;30(10):1599-1607. doi: 10.1093/jamia/ocad147

 

  1. Qiu J, Li L, Sun J, et al. Large AI models in health informatics: Applications, challenges, and the future. IEEE J Biomed Health Inform. 2023;27(12):6074-6087. doi: 10.1109/JBHI.2023.3316750

 

  1. ACGME. Available from: https://apps.acgme.org/ads/ public/programs/search [Last accessed on 2024 Dec 02].

 

  1. Davies A, Mueller J, Moulton G. Core competencies for clinical informaticians: A systematic review. Int J Med Inform. 2020;141:104237. doi: 10.1016/j.ijmedinf.2020.104237

 

  1. Davies A, Hassey A, Williams J, Moulton G. Creation of a core competency framework for clinical informatics: From genesis to maintaining relevance. Int J Med Inform. 2022;168:104905. doi: 10.1016/j.ijmedinf.2022.104905

 

  1. Patton GA, Gardner RM. Medical informatics education: The university of Utah experience. J Am Med Inform Assoc. 1999;6(6):457-465. doi: 10.1136/jamia.1999.0060457

 

  1. Bryson D. Continuing professional development and mentoring. J Vis Commun Med. 2022;45(1):64-66. doi: 10.1080/17453054.2021.2005459

 

  1. Gong Y, Min H, Jing X, Yu P. Challenges and opportunities of artificial intelligence in CDSS and patient safety. In: Mantas J, Hasman A, Demiris G, et al., editors. Studies in Health Technology and Informatics. Amsterdam: IOS Press; 2024. doi: 10.3233/SHTI240638

 

  1. Vaishya R, Misra A, Vaish A. ChatGPT: Is this version good for healthcare and research? Diabetes Metab Syndr. 2023;17(4):102744. doi: 10.1016/j.dsx.2023.102744

 

  1. Soong TK, Ho CM. Artificial intelligence in medical OSCEs: Reflections and future developments. Adv Med Educ Pract. 2021;12:167-173. doi: 10.2147/AMEP.S287926

 

  1. Walsh K, Maloney S. Self-directed learning using clinical decision support: Costs and outcomes. Br J Hosp Med (Lond). 2018;79(7):408-409. doi: 10.12968/hmed.2018.79.7.408

 

  1. Walsh K, Seyidov N, Wroczynski M, Payne G, Bhagavatheeswaran L. Education and clinical decision support for healthcare professionals on emergency preparedness for extremely dangerous pathogens: Report of a conference workshop. BMJ Mil Health. 2020;166(2): 103-104. doi: 10.1136/jramc-2019-001328

 

  1. Berner ES. Clinical Decision Support Systems: Theory and Practice. Berlin, Heidelberg: Springer; 2016.

 

  1. Greenes RA, Del Fiol G, editors. Clinical Decision Support and beyond: Progress and Opportunities in Knowledge- Enhanced Health and Healthcare. 3rd ed. United States: Academic Press, an imprint of Elsevier; 2023.

 

  1. Shortliffe EH, Cimino JJ, Chiang MF, editors. Biomedical Informatics: Computer Applications in Health Care and Biomedicine. 5th ed. Berlin: Springer; 2021.

 

  1. Fultz Hollis K, Hersh WR, editors. Health Informatics: Practical Guide. 8th ed. United States: Lulu; 2022.

 

  1. Coiera E. Guide to Health Informatics. 3rd ed. United States: CRC Press, Taylor & Francis Group; 2015.

 

  1. Silverman H, Lehmann C, Munger B. Milestones: Critical elements in clinical informatics fellowship programs. Appl Clin Inform. 2016;07(01):177-190. doi: 10.4338/ACI-2015-10-SOA-0141

 

  1. CAHIIM. Available from: https://www.cahiim.org [Last accessed on 2024 Sep 25].

 

  1. HIMSS. Available from: https://www.himss.org/what-we-do-opportunities/approved-education-partners [Last accessed on 2024 Sep 25].

 

  1. Gadd CS, Williamson JJ, Steen EB, Fridsma DB. Creating advanced health informatics certification. J Am Med Inform Assoc. 2016;23(4):848-850. doi: 10.1093/jamia/ocw089

 

  1. McNeile McCormick D, Bichel-Findlay J, O’Driscoll D, Butler-Henderson K, Tarabay T. An exploration of the certified health Informatician Australasia (CHIA) participants. In: Bichel-Findlay J, Otero P, Scott P, Huesing E, editors. Studies in Health Technology and Informatics. Amsterdam: IOS Press; 2024. doi: 10.3233/SHTI231162

 

  1. Jaspers MW, Mantas J, Borycki E, Hasman A. IMIA accreditation of biomedical and health informatics education: Current state and future directions. Yearb Med Inform. 2017;26(1):252-256. doi: 10.15265/IY-2017-011

 

  1. Chávez K, Mitchell KMW. Exploring bias in student evaluations: Gender, race, and ethnicity. PS Polit Sci Polit. 2020;53(2):270-274. doi: 10.1017/S1049096519001744

 

  1. Zhang P, Kamel Boulos MN. Generative AI in medicine and healthcare: Promises, opportunities and challenges. Future Internet. 2023;15(9):286. doi: 10.3390/fi15090286

 

  1. Lin X, Liang C, Liu J, Lyu T, Ghumman N, Campbell B. Artificial intelligence-augmented clinical decision support systems for pregnancy care: Systematic review. J Med Internet Res. 2024;26:e54737. doi: 10.2196/54737

 

  1. Wang F, Preininger A. AI in health: State of the art, challenges, and future directions. Yearb Med Inform. 2019;28(1):16-26. doi: 10.1055/s-0039-1677908

 

  1. Liu J, Wang C, Liu S. Utility of ChatGPT in clinical practice. J Med Internet Res. 2023;25:e48568. doi: 10.2196/48568

 

  1. Delsoz M, Raja H, Madadi Y, et al. The use of ChatGPT to assist in diagnosing glaucoma based on clinical case reports. Ophthalmol Ther. 2023;12(6):3121-3132. doi: 10.1007/s40123-023-00805-x

 

  1. Tian S, Jin Q, Yeganova L, et al. Opportunities and challenges for ChatGPT and large language models in biomedicine and health. Brief Bioinform. 2023;25(1):bbad493. doi: 10.1093/bib/bbad493

 

  1. Hamed E, Eid A, Alberry M. Exploring ChatGPT’s potential in facilitating adaptation of clinical guidelines: A case study of diabetic ketoacidosis guidelines. Cureus. 2023;9:e38784. doi: 10.7759/cureus.38784

 

  1. Lin TH, Chung HY, Jian MJ, et al. An advanced machine learning model for a web-based artificial intelligence-based clinical decision support system application: Model development and validation study. J Med Internet Res. 2024;26:e56022. doi: 10.2196/56022

 

  1. Liao Z, Wang J, Shi Z, Lu L, Tabata H. Revolutionary potential of ChatGPT in constructing intelligent clinical decision support systems. Ann Biomed Eng. 2024;52(2):125-129. doi: 10.1007/s10439-023-03288-w

 

  1. Mergen M, Junga A, Risse B, et al. Immersive training of clinical decision making with AI driven virtual patients - a new VR platform called medical tr.AI.ning. GMS J Med Educ. 2023;40:Doc18. doi: 10.3205/ZMA001600

 

  1. Fąferek J, Cariou PL, Hege I, et al. Integrating virtual patients into undergraduate health professions curricula: A framework synthesis of stakeholders’ opinions based on a systematic literature review. BMC Med Educ. 2024;24(1):727. doi: 10.1186/s12909-024-05719-1

 

  1. Bukar UA, Sayeed MS, Fatimah Abdul Razak S, Yogarayan S, Sneesl R. Decision-making framework for the utilization of generative artificial intelligence in education: A case study of ChatGPT. IEEE Access. 2024;12:95368-95389. doi: 10.1109/ACCESS.2024.3425172

 

  1. Temsah MH, Aljamaan F, Malki KH, et al. ChatGPT and the future of digital health: A study on healthcare workers’ perceptions and expectations. Healthcare (Basel). 2023;11(13):1812. doi: 10.3390/healthcare11131812

 

  1. Sandu R, Gide E, Elkhodr M. The role and impact of ChatGPT in educational practices: Insights from an Australian higher education case study. Discov Educ. 2024;3(1):71. doi: 10.1007/s44217-024-00126-6

 

  1. Limna P, Kraiwanit T, Jangjarat K, Klayklung P, Chocksathaporn P. The use of ChatGPT in the digital era: Perspectives on chatbot implementation. J Appl Learn Teach. 2023;6(1):64-74. doi: 10.37074/jalt.2023.6.1.32

 

  1. Zainal H, Tan JK, Xiaohui X, Thumboo J, Yong FK. Clinical informatics training in medical school education curricula: A scoping review. J Am Med Inform Assoc. 2023;30(3):604-616. doi: 10.1093/jamia/ocac245

 

  1. Zondag AGM, Rozestraten R, Grimmelikhuijsen SG, et al. The effect of artificial intelligence on patient-physician trust: Cross-sectional vignette study. J Med Internet Res. 2024;26:e50853. doi: 10.2196/50853
Share
Back to top
Journal of Clinical Informatics, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept