A Clinical Nurse Specialist–Led Interprofessional Quality Improvement Project to Reduce Hospital-Acquired Pressure Ulcers
Bibliographic record
Abstract
PURPOSE: The purpose of this clinical nurse specialist-led interprofessional quality improvement project was to reduce hospital-acquired pressure ulcers (HAPUs) using evidence-based practice. BACKGROUND: Hospital-acquired pressure ulcers (PUs) have been linked to morbidity, poor quality of life, and increasing costs. Pressure ulcer prevention and management remain a challenge for interprofessional teams in acute care settings. RATIONALE: Hospital-acquired PU rate is a critical nursing quality indicator for healthcare organizations and ties directly with Mount Sinai Hospital's (MSH's) mission and vision, which mandates providing the highest quality care to patients and families. DESCRIPTION: This quality improvement project, guided by the Donabedian model, was based on the Registered Nurses' Association of Ontario Best Practice Guideline Risk Assessment & Prevention of Pressure Ulcers. A working group was established to promote evidence-based practice for PU prevention. Initiatives such as documentation standardization, development of staff education and patient and family educational resources, initiation of a hospital-wide inventory for support surfaces, and procurement of equipment were implemented to improve PU prevention and management across the organization. OUTCOME: An 80% decrease in HAPUs has been achieved since the implementation of best practices by the Best Practice Guideline Pressure Ulcer working group. CONCLUSION: The implementation of PU prevention strategies led to a reduction in HAPU rates. The working group will continue to work on building interprofessional awareness and collaboration in order to prevent HAPUs and promote an organizational culture that supports staff development, teamwork and communication. IMPLICATIONS: This quality improvement project is a successful example of an interprofessional clinical nurse specialist-led initiative that impacts patient/family and organization outcomes through the identification and implementation of evidence-based nursing practice.
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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 | 0.033 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".