Impact on management performance outcomes of standardizing wound care using integrated care bundles – results of a retrospective analysis
Bibliographic record
Abstract
AIM: Integrated care bundles (ICBs) are interventions that used together synergistically improve care outcomes through standardized care pathways and can help to optimize healthcare expenditure. For wound care, ICBs may need to incorporate low or non-adherent dressings for patients with fragile and/or sensitive skin to help reduce the risk of damage. This retrospective analysis of real-world data assessed the impact on wound management outcomes of an ICB where a specific non-adhesive foam dressing was selected for patients with fragile skin and appropriate wound types. METHOD: A comprehensive wound care program using ICBs was implemented and tracked using a real-time electronic database at two large healthcare organizations in Ontario, Canada (December 2015 to March 2018). Anonymized patient data were analyzed retrospectively. RESULTS: 4421 patients with chronic wounds received an ICB including a non-adhesive foam dressing; 2242 patients did not receive an ICB. With use of the ICB including a non-adhesive foam dressing versus no ICB: mean Bates-Jensen Wound Assessment Tool (BWAT) Score was lower (27.4 vs 33.2); mean time to healing was shorter (12.7 vs 25.5 weeks) and mean time between dressing changes was longer (3.1 vs 1.9 days). Mean labor costs were lower with use of the ICB including a non-adhesive foam dressing (CAD 1766 vs 6488; p < 0.05). No adverse events related to the non-adhesive foam dressing were reported and the overall safety profile improved with use of the ICB. CONCLUSION: Implementing ICBs can help to improve wound management outcomes and reduce labor costs compared with non-standardized care without an ICB.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".