Digital Health Training Programs for Medical Students: Scoping Review
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".