Decreasing Hospital-acquired Pressure Injuries During the COVID-19 Pandemic: A 5-step Quality Improvement Approach
Bibliographic record
Abstract
BACKGROUND: Hospital-acquired pressure injuries (HAPIs) are common adverse events with large burdens on patients and health systems. In 2020, during the initial waves of the COVID-19 pandemic, the incidence of admitted patients with HAPIs of stage II and above in our health system rose from 2.92% to 3.80%. In response to rising HAPI rates across our own hospital system, we established a quality aim to reduce HAPIs stage II and above by 50% over 3 years from the onset of the COVID-19 pandemic. METHODS: We designed a multidisciplinary quality improvement HAPI prevention program. Our initiative had 5 key aspects: fostering governance and accountability, providing education and training, changing clinical practice, monitoring data and evaluation, and modernizing environments and equipment. RESULTS: HAPI rate (outcome measure) declined from 3.8% at the onset of the COVID-19 pandemic to 1.6% (58% reduction, P <0.00001) postintervention. Braden Risk Assessment Tool use (process measure) improved from 88.2% to 92.2%. ( P =0.00024). Rate of patient falls with injuries (balancing measure) decreased from 1.5 per 1000 patient days to 1.0 per 1000 patient days ( P =0.0009). CONCLUSIONS: Despite working during the COVID-19 pandemic where organizational resources were constrained and infection control practices were heightened, a multidisciplinary QI HAPI prevention program, informed by evidence-based practices and supported by access to real-time data, led to an ∼58% reduction in the HAPI rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".