Association Between Hospital Outbreaks and Hand Hygiene: Insights from Electronic Monitoring
Bibliographic record
Abstract
BACKGROUND: Hand hygiene (HH) is an important patient safety measure linked to the prevention of health care-associated infection, yet how outbreaks affect HH performance has not been formally evaluated. METHODS: A controlled, interrupted time series was performed across 5 acute-care academic hospitals using group electronic monitoring. This system captures 100% of all hand sanitizer and soap dispenser activations via a wireless signal to a wireless hub; the number of activations is divided by a previously validated estimate of the number of daily HH opportunities per patient bed, multiplied by the hourly census of patients on the unit. Daily HH adherence 60 days prior and 90 days following outbreaks on inpatient units was compared to control units not in outbreaks over the same period, using a Poisson regression model adjusting for correlations within hospitals and units. Predictors of HH improvement were assessed in this multivariate model. RESULTS: In the 60 days prior to outbreaks, units destined for outbreaks had significantly lower HH adherence compared to control units (incidence rate ratio [IRR], 0.91; 95% confidence interval [CI], .90-.93; P < .0001). Following an outbreak, the HH adherence among the outbreak units increased above that of the controls (IRR, 1.04; 95% CI, 1.02-1.06; P < .0001). Greater improvements were noted for outbreaks on surgical units, for outbreaks involving antibiotic-resistant organisms and enteric pathogens, and in those outbreaks where health-care workers became ill. CONCLUSIONS: Hospital outbreaks tend to occur in units with lower HH adherence and are associated with rapid improvements in HH performance. Group electronic monitoring of HH could be used to develop novel, prospective feedback interventions designed to avert hospital outbreaks.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".