A Usability Evaluation of a Touchscreen Workstation on Wheels in a Simulated Emergency Department Workflow
Bibliographic record
Abstract
Background: Touchscreens have become ubiquitous in our daily lives, offering a comfortable and natural human-technology interactive experience. There exists a gap in the literature regarding the usability and efficiency of a touchscreen workstation on wheels (WOW) within an emergency department (ED) workflow, specifically with electronic medical record (EMR) systems designed for keyboard and mouse. Methods: This was a randomized, controlled, 2-intervention-2-period crossover study comparing a touchscreen to a non-touchscreen WOW. Participants were asked to complete a series of seven tasks that are typically done in the ED followed by the completion of a post-study questionnaire. Results: A total of 24 people (12 attendings, 12 resident physicians) participated in the study. Results from the linear mixed model regression analyses showed no evidence to reject the hypothesis that the average time to complete each task and the average total time to complete all tasks combined were similar (p>0.05) between the touchscreen and non-touchscreen WOW. Results from the post-study questionnaire using a 7-point Likert scale (Figure 1) demonstrated that the majority (>50%) of participants agreed to most questions favoring intention to use (BU), ease of use (PEOU), perceived usefulness (PU), and attitude towards utilization (AU) of the touchscreen WOW. Conclusion: This study builds on previous work on touchscreen devices by specifically evaluating the usability and efficiency of touchscreen WOWs in a controlled, simulation-based setting, differentiating from prior studies on tablets at the bedside. Future studies, should evaluate the impact of touchscreen-friendly EMR designs on clinical workflows in the ED.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".