Using Simulations to Train Medical Students for Unanticipated Technology Failures in Telemedicine
Bibliographic record
Abstract
Simulations offer a safe environment for health professional training and the opportunity to predictably and consistently introduce events or variables that may be rare or dangerous in a live setting. Exposing trainees to unanticipated events during simulations can improve their ability to adapt and improvise. The COVID-19 pandemic accelerated the adoption of telehealth worldwide and highlighted the need for better training in health professional schools. In the United States, the Association of American Medical Colleges (AAMC) published new telehealth competency standards in 2021. The AAMC stated that health care providers should be aware of the risks of technology failures, capable of troubleshooting them, and lead systems interventions to improve safety. However, the AAMC does not provide guidance on the specific failures or solutions. In this study, we developed a set of technology failures that can be simulated in a telehealth curriculum. We incorporated one technology failure into a simulated telehealth encounter and gathered students' (N = 53) feedback on the exercise. Students' feedback was overwhelmingly positive. They agreed that integrating technology failures into telehealth simulations provides important practice managing these events during clinical encounters. While telehealth is an important healthcare delivery modality that can improve access-to-care, it is imperative to train medical students to navigate technology failures so that can adeptly manage these issues in clinical practice.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".