Reducing CLABSI Rates in Adult ICUs: A Multi-Center Performance Improvement Project (2020-2021)
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Central Line-Associated Bloodstream Infection (CLABSI) remains a leading cause of death among critically ill patients. Implementing preventive measures and adhering to best practices are crucial actions to proactively prevent its occurrence. This project aimed to reduce the overall CLABSI rate in adult medical/surgical Intensive Care Units (ICUs) of hospitals under the Ministry of Defense Health Services (MODHS) in Saudi Arabia. The baseline CLABSI rate was 2 cases per 1000 catheter days during the first quarter of 2020, while the target was to achieve a rate equal to or lower than 0.8 as reported by the American National Healthcare Safety Network (NHSN) in 2013. METHODS: The initiative was carried out across 15 hospitals under the purview of MODHS. Data on CLABSI incidents were collected from the ICUs dedicated to adult medical and surgical care. The project utilized the Institute for Healthcare Improvement collaborative model to achieve breakthrough improvement in a short-term learning system that facilitated the collaboration of participating hospitals in the pursuit of enhancements in CLABSI rates. The project involved 3 cycles, each consisting of a learning session followed by an action period. RESULTS: The data revealed a continuous improvement in the overall CLABSI rate within MODHS hospitals, progressing positively for 4 consecutive quarters and attaining a value of 0.3 during the third quarter of 2021. This signifies an impressive 85% reduction from the initial baseline of 2, and the rate remains below the project benchmark of 0.8. CONCLUSION: The project successfully employed collaborative learning cycles, fostering effective knowledge-sharing among teams and promoting active engagement. This approach proved instrumental in achieving learning objectives, identifying gaps, and determining appropriate courses of action. Key factors for the project's success included standardizing the change package, conducting regular training sessions, encouraging open discussions, and sharing experiences.
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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 | 0.022 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".