A comparison of hospital-acquired pressure injuries in intensive care and non-intensive care units: a multifaceted quality improvement initiative
Bibliographic record
Abstract
Hospital-acquired pressure injuries (HAPI) are a significant cause of morbidity and mortality, and represent a major health concern worldwide. Patients suffering from HAPI report a poor quality of life on several dimensions of health. Moreover, HAPI is reported to lengthen in-hospital stay in the acute setting, posing significant healthcare resource utilisations and costs. Given the clinical and economic burden of HAPI, recent best practice guidelines provide recommendations to reduce the prevalence of pressure injuries. Humber River Hospital (HRH), a large community hospital in Toronto, Canada, has a daily census of approximately 500 patients. The aim of this project was to reduce the prevalence of HAPI within the intensive care unit (ICU) and non-ICU setting at HRH within a 1-year period. Using the International Pressure Injury/Ulcer Prevalence (IPUP) Survey we established a baseline prevalence of HAPI of 27.6% (n=315) for non-ICU and 30% for ICU (n=33) patients at our institution in 2015. Using the Plan-Do-Study-Act (PDSA) method for quality improvement, we implemented a multifaceted approach aimed at improving equipment, digital documentation and education on risk assessment, prevention and treatment strategies. Over multiple PDSA cycles, our prevalence of HAPI reduced to 16% for non-ICU patients with no changes to the HAPI prevalence in ICU patients in 2016. Sustainability continues with HAPI prevalence currently at 10% in 2017 for non-ICU patients, which outperforms the Canadian prevalence (13.7%) by census size for 2017. However, the prevalence of HAPI in the ICU increased to 45% in 2017 despite multiple quality improvement initiatives, suggesting critically ill patients represent a unique challenge for reducing HAPI for these patients at our institution.
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 | 0.030 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".