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Cited by 106Innovations in Medical EducationHealth and Medical EducationArtificial Intelligence in Healthcare and Education

Digital health training programs for medical students: scoping review

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Tudor Car, Lorainne, Kyaw, Bhone Myint, Nannan Panday, Rishi S, van der Kleij, Rianne, Chavannes, Niels, Majeed, Azeem, Car, Josip·2021·JMIR Medical Education
DOI 10.2196/28275PMC PMC8339984

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Abstract

Background: Medical schools worldwide are accelerating the introduction of digital health courses into their curricula. The COVID-19 pandemic has contributed to this swift and widespread transition to digital health and education. However, the need for digital health competencies goes beyond the COVID-19 pandemic because they are becoming essential for the delivery of effective, efficient, and safe care. Objective: This review aims to collate and analyze studies evaluating digital health education for medical students to inform the development of future courses and identify areas where curricula may need to be strengthened. Methods: We carried out a scoping review by following the guidance of the Joanna Briggs Institute, and the results were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We searched 6 major bibliographic databases and gray literature sources for articles published between January 2000 and November 2019. Two authors independently screened the retrieved citations and extracted the data from the included studies. Discrepancies were resolved by consensus discussions between the authors. The findings were analyzed using thematic analysis and presented narratively. Results: A total of 34 studies focusing on different digital courses were included in this review. Most of the studies (22/34, 65%) were published between 2010 and 2019 and originated in the United States (20/34, 59%). The reported digital health courses were mostly elective (20/34, 59%), were integrated into the existing curriculum (24/34, 71%), and focused mainly on medical informatics (17/34, 50%). Most of the courses targeted medical students from the first to third year (17/34, 50%), and the duration of the courses ranged from 1 hour to 3 academic years. Most of the studies (22/34, 65%) reported the use of blended education. A few of the studies (6/34, 18%) delivered courses entirely digitally by using online modules, offline learning, massive open online courses, and virtual patient simulations. The reported courses used various assessment approaches such as paper-based assessments, in-person observations, and online assessments. Most of the studies (30/34, 88%) evaluated courses mostly by using an uncontrolled before-and-after design and generally reported improvements in students’ learning outcomes. Conclusions: Digital health courses reported in literature are mostly elective, focus on a single area of digital health, and lack robust evaluation. They have diverse delivery, development, and assessment approaches. There is an urgent need for high-quality studies that evaluate digital health education.

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  • Seven studies met the ROBIS criteria for low risk, one study was of unclear risk, and three studies were of high risk.

    “Seven studies met the ROBIS criteria for low risk [12,13,38,39,40,41,42], one study was of unclear risk [43], and three studies were of high risk [44–46].”

    From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [41]

  • Eleven studies suggest that physicians should understand the role of data science in AI.

    “Eleven studies suggest that physicians should understand the role of data science in AI [12,13,16,37,39,41,42,45].”

    From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [41]

  • Three key types of communication were identified: communication for clinical decision-making, communication for implementation, and communication for knowledge dissemination.

    “Three key types of communication were identified: communication for clinical decision-making [13], communication for implementation [13,37], and communication for knowledge dissemination [12,13,37,41,42].”

    From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [41]

  • Knowledge related to data stewardship, data preprocessing and acquisition, standardization, data analytics, health data infrastructure, programming, and big data may help physicians appreciate obstacles faced by data scientists and improve multidisciplinary collaboration.

    “In particular, knowledge related to data stewardship [12,13,37,41], data preprocessing and acquisition [13], standardization [13], data analytics [13,17,39], health data infrastructure [45], programming [39,41], and big data [13,45], may help physicians appreciate the obstacles faced by data scientists and improve the multidisciplinary collaboration [13,39].”

    From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [41]

  • Four key relationships for communication were identified: data scientist to physician, physician to physician/allied healthcare professional, physician to patient, and physician to payer.

    “Furthermore, four key relationships for communication were identified: data scientist to physician [13], physician to physician/allied healthcare professional [12,13,42], physician to patient [12,13,37,41,42], and physician to payer [13,37].”

    From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [41]

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Research works printed but not linked: 2

  1. [12]WHO guideline: recommendations on digital interventions for health system strengthening: executive summary World Health Organization 2019 2021-07-03 https://apps.who.int/iris/handle/10665/311977 31162915
  2. [13]Institute of Medicine (US) Committee on Assuring the Health of the Public in the 21st Century The Future of the Public's Health in the 21st Century 2002 Washington (DC) National Academies Press (US) 25057638

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Authors
Tudor Car, L., Kyaw, B. M., Nannan Panday, R. S., van der Kleij, R., Chavannes, N., Majeed, A., & Car, J.
Affiliations
  • Nanyang Technological University (SG)
  • Leiden University Medical Center (NL)
  • Imperial College London (GB)
ORCID
Venue
JMIR Medical Education
Publisher
JMIR Publications Inc.
Published
2021-07-21
Volume
7
Issue
3
Pages
e28275
ISSN
2369-3762 (ISSN-L)
DOI
10.2196/28275
PMC
PMC8339984
OpenAlex
W3184246982
Cited by
106
References
58
Citations by year
2026: 20 · 2025: 18 · 2024: 29 · 2023: 19 · 2022: 19 · 2021: 1
FWCI
3.16
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Medical Education (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Leiden Repository (Leiden University) (submittedVersion, cc-by)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • Leiden Repository (Leiden University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DR-NTU (Nanyang Technological University) (submittedVersion, cc-by)
  • Spiral (Imperial College London) (submittedVersion, other-oa)
Funders
  • National Institute for Health and Care Research (NIHR202425)
  • Department of Health and Social Care
  • Imperial College London
  • Nanyang Technological University
  • Lee Kong Chian School of Medicine, Nanyang Technological University
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Health Sciences › Health Professions › General Health Professions › Health and Medical Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
digital health education, medical students, medical curriculum, medical informatics, blended learning, online learning, virtual patient simulation, course evaluation, learning outcomes, elective courses, COVID-19 pandemic, digital health competencies, assessment methods, massive open online courses, online modules, offline learning, student satisfaction, student attrition

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