C8 Health, a Platform for the Implementation of Best Practices: Survey-Based Usability Study
Bibliographic record
Abstract
Abstract Background Mobile health (mHealth) apps are increasingly integrated into clinical workflows to support decision-making and adherence to best practices. Usability is a critical determinant of adoption, engagement, and long-term use. Objective This study aimed to evaluate the usability of the C8 Health platform deployed in the Department of Anesthesiology at MetroHealth in 2024. Methods A quality improvement initiative was conducted using the mHealth App Usability Questionnaire (MAUQ). A total of 142 anesthesiology clinicians participated by completing the questionnaire. This study was reported in accordance with the SQUIRE (Standards for Quality Improvement Reporting Excellence) guidelines for quality improvement reporting. Results The overall MAUQ scores indicated high usability, with mean scores greater than 5.5 across core items (overall mean MAUQ score 5.73, SD 0.81). These findings suggest strong user satisfaction and positive engagement with the C8 Health platform among anesthesiology clinicians. Conclusions The C8 Health platform demonstrated high usability in an anesthesiology setting. These results support its integration as a clinical decision support tool to enhance workflow efficiency and adherence to best practices.
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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 | 0.043 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".