A Quality Improvement Initiative to Decrease Central Line–Associated Bloodstream Infections During the COVID-19 Pandemic: A “Zero Harm” Approach
Bibliographic record
Abstract
INTRODUCTION: Central line-associated bloodstream infections (CLABSIs) are associated with significant patient harm and health care costs. Central line-associated bloodstream infections are preventable through quality improvement initiatives. The COVID-19 pandemic has caused many challenges to these initiatives. Our community health system in Ontario, Canada, had a baseline rate of 4.62 per 1000 line days during the baseline period. OBJECTIVES: Our aim was to reduce CLABSIs by 25% by 2023. METHODS: An interprofessional quality aim committee performed a root cause analysis to identify areas for improvement. Change ideas included improving governance and accountability, education and training, standardizing insertion and maintenance processes, updating equipment, improving data and reporting, and creating a culture of safety. Interventions occurred over 4 Plan-Do-Study-Act cycles. The outcome was CLABSI rate per 1000 central lines: process measures were rate of central line insertion checklists used and central line capped lumens used, and balancing measure was the number of CLABSI readmissions to the critical care unit within 30 days. RESULTS: Central line-associated bloodstream infections decreased over 4 Plan-Do-Study-Act cycles from a baseline rate of 4.62 (July 2019-February 2020) to 2.34 (December 2021-May 2022) per 1000 line days (51%). The rate of central line insertion checklists used increased from 22.8% to 56.9%, and central line capped lumens used increased from 72% to 94.3%. Mean CLABSI readmissions within 30 days decreased from 1.49 to 0.1798. CONCLUSIONS: Our multidisciplinary quality improvement interventions reduced CLABSIs by 51% across a health system during the COVID-19 pandemic.
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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 | 0.039 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| 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".