Using Mixed Reality Headsets to Deliver Remote Bedside Teaching During the COVID-19 Pandemic: Feasibility Trial of HoloLens 2
Bibliographic record
Abstract
BACKGROUND: COVID-19 has had a catastrophic impact in terms of human lives lost. Medical education has also been impacted as appropriately stringent infection control policies precluded medical trainees from attending clinical teaching. Lecture-based education has been easily transferred to a digital platform, but bedside teaching has not. OBJECTIVE: This study aims to assess the feasibility of using a mixed reality (MR) headset to deliver remote bedside teaching. METHODS: Two MR sessions were led by senior doctors wearing the HoloLens headset. The trainers selected patients requiring their specialist input. The headset allowed bidirectional audiovisual communication between the trainer and trainee doctors. Trainee doctor conceptions of bedside teaching, impact of the COVID-19 pandemic on bedside teaching, and the MR sessions were evaluated using pre- and postround questionnaires, using Likert scales. Data related to clinician exposure to at-risk patients and use of personal protective equipment (PPE) were collected. RESULTS: Prequestionnaire respondents (n=24) strongly agreed that bedside teaching is key to educating clinicians (median 7, IQR 6-7). Postsession questionnaires showed that, overall, users subjectively agreed the MR session was helpful to their learning (median 6, IQR 5.25-7) and that it was worthwhile (median 6, IQR 5.25-7). Mixed reality versus in-person teaching led to a 79.5% reduction in cumulative clinician exposure time and 83.3% reduction in PPE use. CONCLUSIONS: This study is proof of principle that HoloLens can be used effectively to deliver clinical bedside teaching. This novel format confers significant advantages in terms of minimizing exposure of trainees to COVID-19, reducing PPE use, enabling larger attendance, and delivering convenient and accessible real-time clinical training.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".