Teaching Digital Medicine to Undergraduate Medical Students With an Interprofessional and Interdisciplinary Approach: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: An integration of digital medicine into medical education can help future physicians shape the digital transformation of medicine. OBJECTIVE: We aim to describe and evaluate a newly developed course for teaching digital medicine (the Bielefeld model) for the first time. METHODS: The course was held with undergraduate medical students at Medical School Ostwestfalen-Lippe at Bielefeld University, Germany, in 2023 and evaluated via pretest-posttest surveys. The subjective and objective achievement of superordinate learning objectives and the objective achievement of subordinate learning objectives of the course, course design, and course importance were evaluated using 5-point Likert scales (1=strongly disagree; 5=strongly agree); reasons for absences were assessed using a multiple-choice format, and comments were collected. The superordinate objectives comprised (1) the understanding of factors driving the implementation of digital medical products and processes, (2) the application of this knowledge to a project, and (3) the empowerment to design such solutions in the future. The subordinate objectives comprised competencies related to the first superordinate objective. RESULTS: In total, 10 undergraduate medical students (male: n=4, 40%; female: n=6, 60%; mean age 21.7, SD 2.1 years) evaluated the course. The superordinate objectives were achieved well to very well-the medians for the objective achievement were 4 (IQR 4-5), 4 (IQR 3-5), and 4 (IQR 4-4) scale units for the first, second, and third objectives, respectively, and the medians for the subjective achievement of the first, second, and third objectives were 4 (IQR 3-4), 4.5 (IQR 3-5), and 4 (IQR 3-5) scale units, respectively. Participants mastered the subordinate objectives, on average, better after the course than before (presurvey median 2.5, IQR 2-3 scale units; postsurvey median 4, IQR 3-4 scale units). The course concept was rated as highly suitable for achieving the superordinate objectives (median 5, IQR 4-5 scale units for the first, second, and third objectives). On average, the students strongly liked the course (median 5, IQR 4-5 scale units) and gained a benefit from it (median 4.5, IQR 4-5 scale units). All students fully agreed that the teaching staff was a strength of the course. The category positive feedback on the course or positive personal experience with the course received the most comments. CONCLUSIONS: The course framework shows promise in attaining learning objectives within the realm of digital medicine, notwithstanding the constraint of limited interpretability arising from a small sample size and further limitations. The course concept aligns with insights derived from teaching and learning research and the domain of digital medicine, albeit with identifiable areas for enhancement. A literature review indicates a dearth of publications pertaining to analogous courses in Germany. Future investigations should entail a more exhaustive evaluation of the course. In summary, this course constitutes a valuable contribution to incorporating digital medicine into medical 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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".