A Quality Improvement Initiative to Decrease Central Line–Associated Bloodstream Infections During the COVID-19 Pandemic: A “Zero Harm” Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it