TACTICS VR Stroke Telehealth Virtual Reality Training for Health Care Professionals Involved in Stroke Management at Telestroke Spoke Hospitals: Module Design and Implementation Study
Bibliographic record
Abstract
BACKGROUND: Stroke management in rural areas is more variable and there is less access to reperfusion therapies, when compared with metropolitan areas. Delays in treatment contribute to worse patient outcomes. To improve stroke management in rural areas, health districts are implementing telestroke networks. The New South Wales Telestroke Service provides neurologist-led telehealth to 23 rural spoke hospitals aiming to improve treatment delivery and patient outcomes. The training of clinical staff was identified as a critical aspect for the successful implementation of this service. Virtual reality (VR) training has not previously been used in this context. OBJECTIVE: We sought to develop an evidence-based VR training module specifically tailored for stroke telehealth. During implementation, we aimed to assess the feasibility of workplace deployment and collected feedback from spoke hospital staff involved in stroke management on training acceptability and usability as well as perceived training impact. METHODS: The TACTICS VR Stroke Telehealth application was developed with subject matter experts. During implementation, both quantitative and qualitative data were documented, including VR use and survey feedback. VR hardware was deployed to 23 rural hospitals, and use data were captured via automated Wi-Fi transfer. At 7 hospitals in a single local health district, staff using TACTICS VR were invited to complete surveys before and after training. RESULTS: TACTICS VR Stroke Telehealth was deployed to rural New South Wales hospitals starting on April 14, 2021. Through August 20, 2023, a total of 177 VR sessions were completed. Survey respondents (n=20) indicated a high level of acceptability, usability, and perceived training impact (eg, accuracy and knowledge transfer; mean scores 3.8-4.4; 5=strongly agree). Furthermore, respondents agreed that TACTICS VR increased confidence (13/18, 72%), improved understanding (16/18, 89%), and improved awareness (17/18, 94%) regarding stroke telehealth. A comparison of matched pre- and posttraining responses revealed that training improved the understanding of telehealth workflow practices (after training: mean 4.2, SD 0.6; before training: mean 3.2, SD 0.9; P<.001), knowledge on accessing stroke telehealth (mean 4.1, SD 0.6 vs mean 3.1, SD 1.0; P=.001), the awareness of stroke telehealth (mean 4.1, SD 0.6 vs mean 3.4, SD 0.9; P=.03), ability to optimally communicate with colleagues (mean 4.2, SD 0.6 vs mean 3.7, SD 0.9; P=.02), and ability to make improvements (mean 4.0, SD 0.6 vs mean 3.5, SD 0.9; P=.03). Remote training and deployment were feasible, and limited issues were identified, although uptake varied widely (0-66 sessions/site). CONCLUSIONS: TACTICS VR Stroke Telehealth is a new VR application specifically tailored for stroke telehealth workflow training at spoke hospitals. Training was considered acceptable, usable, and useful and had positive perceived training impacts in a real-world clinical implementation context. Additional work is required to optimize training uptake and integrate training into existing education pathways.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".