Use of Artificial Intelligence‐Driven Wound Care Management to Enhance Access to Care Rural and Northern Communities
Bibliographic record
Abstract
Wound care remains a high-priority area for improvement in the Canadian health care system. Older adults aged 65 and older are disproportionately affected by chronic and non-healing wounds and often experience multiple co-morbid conditions, challenges which can be further complicated by living in rural and northern areas. A workshop-based multi-methods study was conducted to describe rural and northern perspectives on opportunities and feasibility to implement innovative wound care technologies. Each workshop included pre- and post- workshop surveys, a live demonstration of Swift Skin and Wound, a Q&A session, and facilitated discussion exploring the technology's feasibility, usability, and accessibility in northern and rural care contexts. Participants who volunteered for the study included care staff and healthcare executives (N = 11), described their perspectives on implementing AI-driven digital wound care management solutions with a focus on integration into health care settings. Three themes were identified including: confidence and optimism in improving wound care management, recognition of the superiority of AI-driven digital wound care solutions over current practices, and the importance of adaptable change processes for successful adoption. While generalizability may be limited, findings suggest that adopting AI-driven wound care tools could improve wound assessment accuracy and streamline care for aging populations in rural and northern areas.
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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".