Automatic detection of hand hygiene using computer vision technology. (26th July 2020)

• Record Type: Journal Article
• Authors: Singh, Amit; Haque, Albert; Alahi, Alexandre; Yeung, Serena; Guo, Michelle; Glassman, Jill R; Beninati, William; Platchek, Terry; Fei-Fei, Li; Milstein, Arnold
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 8(2020)
• Page Start: 1316
• Page End: 1320
• Publication Date: 2020-07-26
• Subjects: computer vision -- hand hygiene -- healthcare acquired infections -- patient safety -- machine learning -- artificial intelligence -- depth sensing; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa115
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Hand hygiene is essential for preventing hospital-acquired infections but is difficult to accurately track. The gold-standard (human auditors) is insufficient for assessing true overall compliance. Computer vision technology has the ability to perform more accurate appraisals. Our primary objective was to evaluate if a computer vision algorithm could accurately observe hand hygiene dispenser use in images captured by depth sensors. Materials and Methods: Sixteen depth sensors were installed on one hospital unit. Images were collected continuously from March to August 2017. Utilizing a convolutional neural network, a machine learning algorithm was trained to detect hand hygiene dispenser use in the images. The algorithm's accuracy was then compared with simultaneous in-person observations of hand hygiene dispenser usage. Concordance rate between human observation and algorithm's assessment was calculated. Ground truth was established by blinded annotation of the entire image set. Sensitivity and specificity were calculated for both human and machine-level observation. Results: A concordance rate of 96.8% was observed between human and algorithm (kappa = 0.85). Concordance among the 3 independent auditors to establish ground truth was 95.4% (Fleiss's kappa = 0.87). Sensitivity and specificity of the machine learning algorithm were 92.1% and 98.3%, respectively. Human observations showed sensitivity and specificity of 85.2% and 99.4%, respectively. Conclusions: A computer vision algorithm was equivalent to human observation in detecting hand hygiene dispenser use. Computer vision monitoring has the potential to provide a more complete appraisal of hand hygiene activity in hospitals than the current gold-standard given its ability for continuous coverage of a unit in space and time.