Time is tissue. Want to save millions in wound care? Start early: a QI project to expedite referral of high-risk wound care patients to specialised care
Bibliographic record
Abstract
INTRODUCTION: Wound care is a multibillion-dollar industry, and most research and treatment are geared towards late-stage or end-stage care. The longer a patient has a wound, the more likely it is that complications (like sepsis or vascular compromise) will occur that will both extend treatment and multiply costs. We postulated that much of the suffering and healthcare costs of chronic wounds could be avoided by early identification of high-risk patients and subsequent earlier intervention. In an established regional wound clinic, our aim was to decrease referral times by 50% within 1 year, and to demonstrate the beneficial outcomes on wound healing and total cost of care. METHODS: A prospective interventional quality improvement study was performed between June 2017 and June 2018. We determined baseline referral times to the clinic and then performed three interventions. The effects on referral time, healing time and number of home care visits to achieve wound healing were collected and displayed on annotated control charts. The cost of care and potential for cost avoidance was determined by an analysis of the medical encounters of twenty chronic wound patients. RESULTS: We achieved a 53.6% reduction in average referral times to the clinic, a 59.6% reduction in average healing times and a 66.0% reduction in the average number of home care visits required to achieve healing. Our cost analysis suggested the potential for significant cost avoidance (87.7%) compared with delayed treatment outside the clinic. CONCLUSIONS: Early identification and treatment of patients at high risk for wound chronicity and complications, followed by early referral to and treatment at a specialised wound clinic, resulted in faster healing and reduced health system costs.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".