Use of an Immersive Virtual Reality Application to Educate Medical Students in Patient Handover: Pilot Study
Bibliographic record
Abstract
Background: Patient handover is a daily task for doctors and nurses, and structured handovers have been proven to positively impact patient outcomes. To teach the handover procedure, different communication tools have been applied, such as the ISBAR (introduction and identification, situation, background, assessment and actions, and recommendation) method. Objective: This study aimed to assess the effectiveness and user engagement of the first-time use of supplementary handover training in virtual reality (VR) for medical students as an addition to an existing curriculum. Furthermore, the VR program was tested for its usability, immersion, visually induced motion sickness (VIMS), and eye strain. Participants were evaluated for their motivation, time spent studying, and experience in VR, as well as their impressions of the use of VR in medical education. Methods: Handover training using the ISBAR method and patient actors is part of the curriculum in surgery of the eighth semester of human medicine studies in Mainz. Knowledge is tested via an Objective Structured Clinical Examination (OSCE) using patient actors. We developed an immersive VR application using 360° video surroundings with structured patient cases. This application was offered as an optional supplementary training in groups of three with a peer tutor. Parameters evaluated included participants' characteristics, usability, and VIMS. Furthermore, a survey of the entire semester was conducted regarding their experience using VR and their enjoyment of studying. Finally, OSCE scores were collected and compared between the groups. Results: The study was conducted over two semesters, and 92 of 385 (23.9%) volunteering students were recruited. The median age was 25 (IQR 23-25) years, and the majority were female (n=61, 68.5%). There were few to no issues regarding VIMS and eye strain (median eye strain 1, IQR 1-2; median VIMS 1, IQR 1-2). There was no significant difference in students' motivation (mid rank participant 107.84; mid rank nonparticipant 122.61; P=.11) and the amount studied for the subject (mid rank participant 113.88; mid rank nonparticipants 119.42; P=.54). Students felt significantly more confident in patient handover after the additional training (7-point Likert scale; mean pretraining 3.96, SD 1.39; mean post-training 3.17, SD 1.41; P<.01) and reported significantly more fun studying than their peers who did not participate in the additional training (mean participants 2.8, SD 1.54; mean nonparticipant 3.69, SD 1.73; P<.01). OSCE scores did not differ between the groups (median score 17 in both groups, IQR participants 16-19; IQR nonparticipants 16-18; P=.62). Conclusions: This study shows that applications in VR, if implemented in a structured curriculum, can be a helpful and safe addition to the teaching of communication skills. VR applications should be considered as a time-flexible, safe, fun, and motivating educational tool as an addition to curricular teaching.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".