Human factors evaluation of an innovative wound care technology
Bibliographic record
Abstract
Pressure injuries and other chronic wounds, such as diabetic foot ulcers and venous leg ulcers, can cause significant pain, increased morbidity and mortality for patients, and result in substantial costs for both the patients and the healthcare system. In the Canadian province of Alberta, a provincial point prevalence audit found one in six patients in acute care facilities had a pressure injury, with 71 % of those deemed hospital-acquired (Alberta Health Services, 2021). As such, the provincial healthcare delivery provider, Alberta Health Services (AHS), wanted to identify an innovative solution to address this problem. In response to this need, NanoSALV Catalytic Advanced Wound Care Treatment Matrix, a Health Canada-approved medical device, was identified by AHS as a promising technology to support wound healing. To help inform decision-making regarding the adoption of this innovative wound care technology, evidence regarding the implementation feasibility of NanoSALV into current practice was needed. The project team conducted a human factors evaluation, gathering perspectives from patients and providers across various environments where these wounds are often treated, including long-term care, in-patient care, outpatient clinics, and patient’s homes. This evaluation was conducted concurrently with a clinical trial assessing NanoSALV's effectiveness in healing chronic wounds unresponsive to current state dressings. The human factors evaluation consisted of observations and interviews, and included 20 participants from multiple roles, including healthcare providers in long-term care, in-patient, and outpatient settings, and patients and family caregivers in home settings. A task analysis was conducted based on the wound dressing observations to better understand the implementation feasibility of NanoSALV, compared to a selected current state silver-based dressing, AQUACEL Ag+. Thematic analysis and journey mapping were conducted based on participant interviews to compare the user experience and satisfaction between NanoSALV and the current state from different patient and provider perspectives. The evaluation indicated that the procedures for changing wound dressings, whether using the current state dressing or NanoSALV, followed the same sequence of steps, with NanoSALV requiring fewer subtasks in the application step, demonstrating the feasibility of implementing NanoSALV into clinical practice. Desirability from the perspective of each of the settings (i.e., long-term care, in-patient, outpatient, and at-home management) for NanoSALV adoption included its ease of application and potential to enhance patient independence and participation in wound care. Some areas for improvement include better communication of the appropriate amount of product needed and making the product packaging easier to open. It was found that it was feasible to integrate NanoSALV into existing workflow practices in the wound care pathway and the technology was perceived as a desirable and feasible solution for chronic wound management from the perspective of all potential users.
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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 | 0.023 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".