Training Physicians in the Digital Health Era: How to Leverage the Residency Elective
Bibliographic record
Abstract
Digital health is an expanding field and is fundamentally changing the ways health care can be delivered to patients. Despite the changing landscape of health care delivery, medical trainees are not routinely exposed to digital health during training. In this viewpoint, we argue that thoughtfully implemented immersive elective internships with digital health organizations, including start-ups, during residency are valuable for residents, residency programs, and digital health companies. This viewpoint represents the opinions of the authors based on their experience as resident physicians working as interns within a start-up health navigation and telehealth company. First, residents were able to apply their expertise beyond the traditional clinical environment, use creativity to solve health care problems, and learn from different disciplines not typically encountered by most physicians in traditional clinical practice. Second, residency programs were able to strengthen their program's educational offerings and better meet the needs of a heterogenous group of residents who are increasingly seeking nontraditional ways to learn more about care delivery transformation. Third, digital health companies were able to expand their clinical team and receive new insights from physicians in training. We believe that immersive elective internships for physicians in training provide opportunities for experiential learning in a fast-paced environment within a field that is rapidly evolving. By creating similar experiences for other resident physicians, residency programs and digital health companies have a key opportunity to influence future physician-leaders and health care innovators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".