User Experience Evaluation of a Spinal Surgery Robot: Workload, Usability, and Satisfaction Study
Bibliographic record
Abstract
BACKGROUND: Robotic spine surgery has continued to evolve since its US Food and Drug Administration approval in 2004, with products now including real-time video guidance and navigation during surgery. As the market for robotic surgical devices evolves, it is important to consider usability factors. OBJECTIVE: The primary objective of this study was to determine the user experience of a surgical-assistive robotic device. The secondary objective was to evaluate workload, usability, the After-Scenario Questionnaire (ASQ), and the System Usability Scale (SUS). In addition, this study compares the workload, usability, and satisfaction survey of the device among different occupational groups using the device. METHODS: Doctors (n=15) and nurses (n=15), the intended users of the surgical assistant robot, participated in the usability evaluation. Participants performed essential scenarios for the surgical assistant robot and provided scenario-specific satisfaction (ASQ), workload (NASA Task Load Index), and usability (SUS) scores. RESULTS: Both doctors and nurses had task success rates of 85% or higher for each scenario. ASQ results showed that both doctors and nurses were least satisfied with ease of completing the task of registration (group 1: mean 4.73, SD 1.57 and group 2: mean 4.47, SD 1.8), amount of time it took (group 1: mean 4.47, SD 1.63 and group 2: mean 4.40, SD 2.09), and support information satisfaction (group 1: mean 5.13, SD 1.50 and group 2: mean 5.13, SD 1.89). All participants had low workloads, and the overall Task Load Index score had a P value of .77, which is greater than .05. The SUS results showed that the overall usability mean for doctors was 64.17 (SD 16.52) and the mean for nurses was 61.67 (SD 19.18), with a P value of .84, which is greater than .05, indicating no difference between the 2 groups. CONCLUSIONS: In this study, doctors and nurses evaluated the interaction of the device in a simulated environment, the operating room. By evaluating the use experience and usability of the device with real intended users, we can develop a more effective and convenient user interface.
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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".