Abstract WP267: Clinical Simulation-based Usability Testing of a Mobile Telestroke System
Bibliographic record
Abstract
Background: Mobile prehospital telestroke presents a novel solution to improve stroke diagnosis and reduce treatment times. This study aims to 1) understand perceptions of a mobile ambulance-based telestroke system from all users and 2) evaluate system usability during ambulance transport. Methods: A Critical Care ambulance was equipped with a mobile telemedicine system to perform remote stroke assessments. Scripted scenarios were performed by trained actors during transport and evaluated by physicians using the NIH stroke scale (NIHSS). Scores obtained during transport were compared with independent bedside and original scripted NIHSS scores. Participants completed the System Usability Scale (SUS), NASA task load index (NASA TLX), audio-video quality scale and a modified Acceptability of Technology survey after completing the NIHSS evaluations. In addition, interviews were conducted to evaluate user’s experience and perceptions. Descriptive analysis was used for all surveys. Weighted kappa was used to compare the agreement in NIHSS scores. A regression model was used to further account for variations. Results: Ten scripted scenarios were simulated twice during the mobile transport and once at bedside. All simulations were completed except for one. NIHSS scores between mobile, bedside and original scripted scenarios revealed good agreement [weighted kappa=0.76 (95% CI: 0.63-0.9, p=0.63)]. There were no statistically significant differences in NIHSS scores between mobile and bedside evaluations. The results were independent of stroke scenarios, physicians, and actors. Overall, 92% and 81% raters deemed video and audio quality as “good” or “excellent” (rating < 3) respectively. The overall mean SUS score was 69.1 (13.3). Content analysis identified strengths, usability issues (i.e. audibility and equipment stability during transport), and safety concerns. Conclusion: This study used in-situ simulation to evaluate the viability of a mobile telestroke system. Simulating stroke scenarios using actors during a real ambulance transport allowed us to assess a health technology without risking patient safety while capturing realistic environmental factors.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".