Virtual 3D Simulation Technology for Interprofessional Team Training
Bibliographic record
Abstract
Background In the hospital, interprofessional team members must work collaboratively. That creates a gap in medical practice, particularly in a hectic emergency that may lead to medical errors, with associated ethical, legal, and financial consequences. Mannequin-based simulation can be a solution to bridge this gap in team training. While mannequin-based simulations are effective as a synchronous method, they are expensive, time and space-bound, use hospital resources, and require the whole team to be present. Objective To develop a prototype of a 3D virtual simulation emergency room (ER) environment for interprofessional team training. And to assess the usability of the prototype in a simulation environment for team training using a clinical scenario. Methods Tools and technologies used for this prototype included the Unity platform, C# programming language, and Photon Voice 2. With 3DS Max, we modified and created 3d assets in the ER simulation room. Adobe XD was used to create interactive prototype iterations. Clinical cases were developed with simple algorithms to prove the concepts. We used complex algorithms with Artificial Intelligence and Machine Learning capabilities for the final product. We conducted two usability tests (n = 10, n = 9) using a think-aloud method, a semi-structured follow-up interview, and a survey. Results A prototype was built to achieve asynchronous team training with easy user access from anywhere in the world. The prototype, which includes voice communication and control of the avatars by players from a distance, supported the usability of the technology for asynchronous team training in health education. Conclusions CyberPatient ER can be used as an additional tool to support team training and communication skills in the health education and healthcare environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".