A Mobile App (BEDSide Mobility) to Support Nurses’ Tasks at the Patient's Bedside: Usability Study
Bibliographic record
Abstract
BACKGROUND: The introduction of clinical information systems has increased the amount of clinical documentation. Although this documentation generally improves patient safety, it has become a time-consuming task for nurses, which limits their time with the patient. On the basis of a user-centered methodology, we have developed a mobile app named BEDSide Mobility to support nurses in their daily workflow and to facilitate documentation at the bedside. OBJECTIVE: The aim of the study was to assess the usability of the BEDSide Mobility app in terms of the navigation and interaction design through usability testing. METHODS: Nurses were asked to complete a scenario reflecting their daily work with patients. Their interactions with the app were captured with eye-tracking glasses and by using the think aloud protocol. After completing the tasks, participants filled out the system usability scale questionnaire. Descriptive statistics were used to summarize task completion rates and the users' performance. RESULTS: A total of 10 nurses (aged 21-50) participated in the study. Overall, they were satisfied with the navigation, layout, and interaction design of the app, with the exception of one user who was unfamiliar with smartphones. The problems identified were related to the ambiguity of some icons, the navigation logic, and design inconsistency. CONCLUSIONS: Besides the usability issues identified in the app, the participants' results do indicate good usability, high acceptance, and high satisfaction with the developed app. However, the results must be taken with caution because of the poor ecological validity of the experimental setting.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".