Artificial intelligence for monitoring hand hygiene compliance in healthcare settings: A scoping review

Clinical Snapshot

Key Findings

• P Value: Not applicable for scoping review

• Effect Size: Computer vision: 95% accuracy (setting-specific) vs 56% (generalizable models)

• Primary Outcome: Technical pathway distribution and performance metrics of AI-based hand hygiene monitoring systems

• Nnt Or Sensitivity: Wearable systems: 5-10% lower specificity than vision-based approaches

• Confidence Interval: Not reported

Clinical Application

Currently limited to research settings; significant implementation barriers including privacy concerns, workflow integration, and cost-effectiveness remain unaddressed Relevant to Australian healthcare infection prevention programs, but would require TGA approval for medical device applications and consideration of privacy regulations under Australian Privacy Act Healthcare workers in acute care settings, particularly ICUs where computer vision models showed highest accuracy

Abstract

BACKGROUND: Hand hygiene is a fundamental measure for preventing healthcare-associated infections, yet traditional monitoring methods are significantly limited by the Hawthorne effect, high resource demands, and an inability to assess procedural quality. Artificial intelligence (AI) technology has emerged as a transformative, automated, and objective approach to address these long-standing challenges. OBJECTIVE: This scoping review sought to systematically map the existing evidence, technical pathways, performance metrics, and implementation challenges of AI for monitoring hand hygiene compliance in healthcare settings. METHODS: Following the Joanna Briggs Institute (JBI) methodological framework and PRISMA-ScR guidelines, we searched five major databases (PubMed, Scopus, Embase, Web of Science, and IEEE Xplore) for articles published between January 2000 and September 2025, supplemented by grey literature searching and backward citation tracking. Two reviewers independently screened records, assessed full-text reports for eligibility, and extracted data, which were synthesized using descriptive statistics and thematic analysis. RESULTS: Of 800 records identified through database and supplementary searches, 45 studies (2007-2025) were included. The primary technical pathways identified were computer vision (53.3%), wearable sensors (24.4%), Internet of Things-integrated systems (13.3%), and radar/radio frequency-based systems (8.9%). While computer vision achieved high accuracy (95%) in setting-specific ICU models, performance dropped to 56% in generalizable models. Wearable systems demonstrated portability but showed 5%-10% lower specificity than vision-based approaches. Most evidence is derived from small-scale technical validations, with a significant lack of formal fairness analysis and evaluation of clinical workflows or cost-effectiveness. CONCLUSION: AI-based hand hygiene monitoring shows promise for supporting more objective and scalable hand hygiene surveillance in healthcare settings. However, the field remains at a largely pre-translational stage. Future research should shift from technical feasibility toward implementation science, focusing on establishing standardized motion databases, evaluating ethical governance (e.g., privacy and automation bias), and conducting pragmatic trials to demonstrate sustained clinical benefit and organizational sustainability.

References

1. 1.Lin, X., Lv, Y., Xiang, Q., Cai, M., & Wang, P. (2026). Artificial intelligence for monitoring hand hygiene compliance in healthcare settings: A scoping review. PLoS One. https://doi.org/10.1371/journal.pone.0347683