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

Xinran Lin, Yu Lv, Qian Xiang, Minhong Cai, Pingping Wang

Abstract

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.

Introduction

Hand hygiene is widely recognized as one of the most fundamental, effective, and cost‐effective measures for preventing healthcare‐associated infections [1–5]. These infections significantly contribute to patient morbidity and mortality while imposing substantial economic burdens on healthcare systems [6–9].

Materials and methods

This scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) methodological framework [36]. The JBI framework was selected because this review aimed to map the breadth, characteristics.

Results

A total of 800 records were identified through database searching, grey literature searching, and citation tracking, including 725 records from databases and 75 records from other methods. Before screening, 302 records were removed, including 297 duplicate records identified from databases and 5 duplicate records identified from other methods.

Discussion

The integration of AI has the potential to address many of the inherent constraints of conventional hand hygiene monitoring. Compared with human observation, AI-based systems enable continuous and objective monitoring while minimizing Hawthorne-related distortion.

Conclusion 

AI-based technologies offer a promising and evolving approach to support hand hygiene monitoring in healthcare settings. Across computer vision, wearable sensor, radar/radio frequency-based, and IoT-integrated systems, existing studies suggest that these technologies can support automated hand hygiene monitoring.

Citation: 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 21(4): e0347683. https://doi.org/10.1371/journal.pone.0347683

Editor: Asli Suner Karakulah, Ege University, Faculty of Medicine, TÜRKIYE

Received: October 30, 2025; Accepted: April 6, 2026; Published: April 21, 2026

Copyright: © 2026 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: AI, Artificial Intelligence; IoT, Internet of Things; ICU, Intensive Care Unit; JBI, Joanna Briggs Institute; LoRaWAN, Long Range Wide Area Network; PCC, Population, Concept, Context; PRISMA-ScR, Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews; RF, Radio Frequency; RGB, Red-Green-Blue; WHO, World Health Organization