Group Electronic Monitoring of Hand Hygiene on Inpatient Units: A Multicenter Cluster Randomized Quality Improvement Study
Bibliographic record
Abstract
Background: The current approach to measuring hand hygiene (HH) relies on human auditors who capture <1% of HH opportunities and rapidly become recognized by staff, resulting in inflation in performance. Our goal was to assess the impact of group electronic monitoring coupled with unit-led quality improvement on HH performance and prevention of healthcare-associated transmission and infection. Methods: A stepped-wedge cluster randomized quality improvement study was undertaken across 5 acute-care hospitals in Ontario, Canada. Overall, 746 inpatient beds were electronically monitored across 26 inpatient medical and surgical units. Daily HH performance as measured by group electronic monitoring was reported to inpatient units who discussed results to guide unit-led improvement strategies. The primary outcome was monthly HH adherence (%) between baseline and intervention. Secondary outcomes included transmission of antibiotic resistant organisms such as methicillin resistant Staphylococcus aureus (MRSA) and other healthcare-associated infections. Results: After adjusting for the correlation within inpatient units, there was a significant overall improvement in HH adherence associated with the intervention (IRR, 1.73; 95% CI, 1.47–1.99; P < .0001). Monthly HH adherence relative to the intervention increased from 29% (1,395,450 of 4,544,144) to 37% (598,035 of 1,536,643) within 1 month, followed by consecutive incremental increases up to 53% (804,108 of 1,515,537) by 10 months (P < .0001). We identified a trend toward reduced healthcare-associated transmission of MRSA (0.74; 95% CI, 0.53–1.04; P = .08). Conclusions: The introduction of a system for group electronic monitoring led to rapid, significant, and sustained improvements in HH performance within a 2-year period. Funding: None Disclosures: None
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.
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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".