System Overview
I worked with nursing educators to build two Unity VR scenarios—chest pain treatment and oxygen therapy—for procedural practice and competency evaluation. Patient avatars and real-time verbal feedback support practice in simulated acute care settings.
Architecture
VR Client
The learner enters an interactive clinical scenario with a patient avatar, medical equipment, and task-specific prompts. The scenes were built in Unity.
Input and Interaction Layer
The learner performs clinical actions and engages with the simulated patient. The system presents scenario steps and captures choices needed for the training flow.
Scenario and Feedback Layer
Scenario logic coordinates the simulated case and provides real-time verbal feedback through the patient avatar. The implementation supports repeated practice and structured evaluation.
Core Features
- Two clinical cases: The chest pain scenario and oxygen therapy scenario cover distinct procedural workflows.
- Interactive patient avatars: Learners practice with simulated patients rather than a static checklist.
- Verbal feedback: Patient responses provide immediate cues during the scenario.
- Evaluation support: The prototype was used in iterative expert and student evaluations.
Interaction Pipeline
- A learner selects a clinical case and enters the VR scenario.
- The system presents the patient and case context.
- The learner performs tasks and interacts with the patient avatar.
- Scenario logic updates the case and produces verbal feedback.
- Researchers gather learner feedback to guide subsequent design changes.
Technical and Design Challenges
Translating clinical practice into VR
The team worked with nursing educators to represent meaningful clinical actions within the limits of an interactive simulation.
Designing for assessment and learning
The system had to support both procedural practice and study-based evaluation. Expert and student feedback informed revisions to the two cases.
Evaluation and Preprint
I implemented interactive scenarios, developed interview questions, interviewed participants, and collected quantitative subjective evaluations. The 2026 preprint reports the iterative evaluation and is under review at ACM Transactions on Computing for Healthcare. It describes a supplemental training tool, not a replacement for clinical instruction.