Hand hygiene monitoring technology: protocol for a systematic review
Bibliographic record
Abstract
BACKGROUND: Healthcare worker hand hygiene is thought to be one of the most important strategies to prevent healthcare-associated infections, but compliance is generally poor. Hand hygiene improvement interventions must include audits of compliance (almost always with feedback), which are most often done by direct observation - a method that is expensive, subjective, and prone to bias. New technologies, including electronic and video hand hygiene monitoring systems, have the potential to provide continuous and objective monitoring of hand hygiene, regular feedback, and for some systems, real-time reminders. We propose a systematic review of the evidence supporting the effectiveness of these systems. The primary objective is to determine whether hand hygiene monitoring systems yield sustainable improvements in hand hygiene compliance when compared to usual care. METHODS/DESIGN: MEDLINE, EMBASE, CINAHL, and other relevant databases will be searched for randomized control studies and quasi-experimental studies evaluating a video or electronic hand hygiene monitoring system. A standard data collection form will be used to abstract relevant information from included studies. Bias will be assessed using the Cochrane Effective Practice and Organization of Care Group Risk of Bias Assessment Tool. Studies will be reviewed independently by two reviewers, with disputes resolved by a third reviewer. The primary outcome is directly observed hand hygiene compliance. Secondary outcomes include healthcare-associated infection incidence and improvements in hand hygiene compliance as measured by alternative metrics. Results will be qualitatively summarized with comparisons made between study quality, the measured outcome, and study-specific factors that may be expected to affect outcome (for example, study duration, frequency of feedback, use of real-time reminders). Meta-analysis will be performed if there is more than one study of similar systems with comparable outcome definitions. DISCUSSION: Electronic and video monitoring systems have the potential to improve hand hygiene compliance and prevent healthcare-associated infection, but are expensive, difficult to install and maintain, and may not be accepted by all healthcare workers. This review will assess the current evidence of effectiveness of these systems before their widespread adoption. STUDY REGISTRATION: PROSPERO registration number: CRD42013004519.
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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 | 0.088 | 0.111 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.015 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.106 | 0.016 |
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".