Improving the quality of pressure ulcer management in a skilled nursing facility
Bibliographic record
Abstract
Pressure ulcers (PUs) are a serious health care problem for nursing home residents and a key quality metric for regulators. Three initiatives were introduced at a 128-bed facility to improve PU prevention. First, a Quality Assurance and Performance Improvement project and a Root Cause Analysis were conducted to improve the facility's wound care programme. Second, a digital wound care management solution was adopted to track wound management. Third, the role of skin integrity coordinator was created as a central point of accountability for wound care-related activities and related performance metrics. Improvements in PU prevention were tracked using Centers of Medicare and Medicaid data, specifically (a) the percentage of long-stay high-risk residents with PUs and (b) the percentage of short-stay residents with PUs that are new or have worsened. PU prevalence for long-stay high-risk residents was 12.99% (Q4 2016), and upon implementation of these initiatives, the facility saw continued reductions in PU prevalence to 2.9% (Q4 2017), while PUs for short-stay residents were maintained at zero throughout this period. This study highlights the power of effective management combined with real-time data analytics, as enabled by digital wound care management, to make significant improvements in health care delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".