Implementing Technology in Practice: Factors Associated with Clinicians’ Satisfaction with an AI Wound Assessment Solution
Bibliographic record
Abstract
Introduction: A digital wound care management application (Swift) leveraging Artificial Intelligence (AI) technology is used by healthcare providers to improve the quality of wound care.Methods: Our observational cross-sectional study invited clinicians using Swift to evaluate wounds at their practice to participate in an online survey to assess their practice patterns, overall satisfaction with the solution, and perspectives on the perceived benefits of using it through a five-point Likert scale and open-ended questions.Results: Overall, our study recorded 81% satisfaction among clinicians.Our findings noted a significantly higher satisfaction (85.5% vs. 76.5%,P=0.034) and agreement on perceived clinical benefits, such as tracking clinical changes in wounds (88.7% vs. 83.6%,P=0.045), saving time in assessing wounds (81.1% vs.71.6%, P=0.023), and effective collaboration (76.1% vs. 70.4%,P=0.044), among those who used the solution for more than nine months compared to those who used the solution for less than nine months.Using the logistic regression model, the likelihood of clinicians' satisfaction with the technology increased two-fold with the prolonged use of the technology (OR 2.334, 95% CI 1.940-5.792,P = 0.042) and when the solution was seen to enable more efficient collaboration (OR 2.291, 95% CI 2.928-5.656,P = 0.047).Conclusion: Clinician satisfaction with technology changes over time.Therefore, it is essential when implementing a new technology to investigate its ability to meet clinical needs and improve the user experience.A holistic understanding of what drives clinician satisfaction in practice is essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".