Reducing blood stream infection in patients on hemodialysis: Incorporating patient engagement into a quality improvement activity
Bibliographic record
Abstract
Introduction Infection in our immunocompromised patients is the second leading cause of death, according to the Centers for Disease Control and Prevention (CDC). In an effort to improve quality of care, engage patients in their own care, and reduce morbidity and mortality secondary to infection, the Network designed a joint quality improvement/patient engagement activity to decrease bloodstream infection (BSI) rates. Methods Dialysis facilities were ranked utilizing 2014 National Healthcare Safety Network (NHSN) data. Selection included 20% of Network 13 facilities (n = 58) with the highest BSI rates, which captured 31% of the patient population. Findings Statistically significant (P < 0.001) improvement was reached in the reduction of BSIs; increasing patient engagement in the infection control process; and, correct completion of hand hygiene audits. Significant (P < 0.01) improvement was reached in correct completion of cannulation audits. There was also improvement in the catheter audits, but results were not significant. Discussion Involving patients in the infection control process contributed to our successful outcomes and could be replicated to meet the needs of the end stage renal disease community as a whole.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".