Telemedicine Training in Undergraduate Medical Education: Mixed-Methods Review
Bibliographic record
Abstract
BACKGROUND: Telemedicine has grown exponentially in the United States over the past few decades, and contemporary trends in the health care environment are serving to fuel this growth into the future. Therefore, medical schools are learning to incorporate telemedicine competencies into the undergraduate medical education of future physicians so that they can more effectively leverage telemedicine technologies for improving the quality of care, increasing patient access, and reducing health care expense. This review articulates the efforts of allopathic-degree-granting medical schools in the United States to characterize and systematize the learnings that have been generated thus far in the domain of telemedicine training in undergraduate medical education. OBJECTIVE: The aim of this review was to collect and outline the current experiences and learnings that have been generated as medical schools have sought to implement telemedicine capacity-building into undergraduate medical education. METHODS: We performed a mixed-methods review, starting with a literature review via Scopus, tracking with Excel, and an email outreach effort utilizing telemedicine curriculum data gathered by the Liaison Committee on Medical Education. This outreach included 70 institutions and yielded 7 interviews, 4 peer-reviewed research papers, 6 online documents, and 3 completed survey responses. RESULTS: There is an emerging, rich international body of learning being generated in the field of telemedicine training in undergraduate medical education. The integration of telemedicine-based lessons, ethics case-studies, clinical rotations, and even teleassessments are being found to offer great value for medical schools and their students. Most medical students find such training to be a valuable component of their preclinical and clinical education for a variety of reasons, which include fostering greater familiarity with telemedicine and increased comfort with applying telemedical approaches in their future careers. CONCLUSIONS: These competencies are increasingly important in tackling the challenges facing health care in the 21st century, and further implementation of telemedicine curricula into undergraduate medical education is highly merited.
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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 | 0.032 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".