Rate of Healthcare Worker–Patient Interaction and Hand Hygiene Opportunities in an Acute Care Setting
Bibliographic record
Abstract
OBJECTIVE: Identify factors affecting the rate of hand hygiene opportunities in an acute care hospital. DESIGN: Prospective observational study. SETTING: Medical and surgical in-patient units, medical-surgical intensive care unit (MSICU), neonatal intensive care unit (NICU), and emergency department (ED) of an academic acute care hospital from May to August, 2012. PARTICIPANTS: Healthcare workers. METHODS: One-hour patient-based observations measured patient interactions and hand hygiene opportunities as defined by the "Four Moments for Hand Hygiene." Rates of patient interactions and hand hygiene opportunities per patient-hour were calculated, examining variation by room type, healthcare worker type, and time of day. RESULTS: During 257 hours of observation, 948 healthcare worker-patient interactions and 1,605 hand hygiene opportunities were identified. Moments 1, 2, 3, and 4 comprised 42%, 10%, 9%, and 39% of hand hygiene opportunities. Nurses contributed 77% of opportunities, physicians contributed 8%, other healthcare workers contributed 11%, and housekeeping contributed 4%. The mean rate of hand hygiene opportunities per patient-hour was 4.2 for surgical units, 4.5 for medical units, 5.2 for ED, 10.4 for NICU, and 13.2 for MSICU (P < .001). In non-ICU settings, rates of hand hygiene opportunities decreased over the course of the day. Patients with transmission-based precautions had approximately half as many interactions (rate ratio [RR], 0.55 [95% confidence interval (CI), 0.37-0.80]) and hand hygiene opportunities per hour (RR, 0.47 [95% CI, 0.29-0.77]) as did patients without precautions. CONCLUSIONS: Measuring hand hygiene opportunities across clinical settings lays the groundwork for product use-based hand hygiene measurement. Additional work is needed to assess factors affecting rates in other hospitals and health care settings.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".