Virtual Wound Care in Australian Nursing Homes: Protocol for a Pilot and Feasibility Study
Bibliographic record
Abstract
Background: Chronic wounds, those which have not healed in a timely manner, are a significant health and economic burden. Older people, especially those living in nursing homes, are disproportionately affected by chronic wounds, and effective management and prevention is a persistent challenge. Specialized wound care can improve outcomes; however, access is limited by aged care workforce shortages, fragmented care, and lack of local services, especially in rural and nursing home settings. Virtual wound care interventions such as WoundView (Coviu Global Pty Ltd), a novel computer vision-based artificial intelligence wound analysis app embedded in Coviu's existing telehealth platform, offer a potential solution to enhance engagement with specialized wound care services. Objective: This protocol aims to outline a pilot and feasibility study for WoundView to assess the acceptability and feasibility of the intervention in preparation for a planned implementation study. The pilot and feasibility study will estimate recruitment and retention rates along with protocol adherence and adaptations. Qualitative exploration of the acceptability of recruitment processes, training and education, participant assessments, intervention delivery, and secondary outcome measures will inform the development of an implementation study of WoundView. Methods: The WoundView pilot and feasibility study is a prospective, nonrandomized study in 2 nursing homes in New South Wales, Australia. The research population will comprise up to 10 nursing home residents, 10 to 30 nursing home staff, and 10 wound care clinicians. All resident participants will receive the intervention, WoundView, as routine clinical care throughout the study period. Virtual care will be conducted with a specialized wound care clinic using WoundView's wound analysis and telehealth features to guide the clinical management of chronic wounds. Wound measures, health-related quality of life, virtual care activity, hospitalization rates, health resource use case studies, and participant satisfaction will be assessed. Nursing home staff and wound care clinicians' satisfaction with WoundView will be collected through brief surveys and in-depth interviews. Results: The WoundView pilot and feasibility study was approved by the university's ethics committee and registered on the Australian New Zealand Clinical Trial Registry. Recruitment and enrollment for the study began in May 2025. Results are expected in the second half of 2025. Conclusions: The design and implementation of virtual care interventions in nursing homes is an underinvestigated issue. Outcomes from this study will contribute to the design of an implementation study testing WoundView in a range of nursing homes around Australia. The integration of WoundView is expected to transform the use of virtual care for wound management and lead to earlier intervention and increased access to specialist wound advice services for nursing home residents.
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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 | 0.090 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.050 | 0.011 |
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".