An Edge AI-based AR System for Hand Hygiene Training in Undergraduates of Health Related Disciplines

Background The ongoing COVID-19 pandemic has posed serious threats to public health worldwide. Although children have mild symptoms, they have been found to more likely transmit the virus within the household than the other age groups. The importance of hand hygiene cannot be underestimated in both healthcare and community settings. The promotion of hand hygiene is the best practices in preventing health care-associated infection. It has been estimated that hand hygiene could reduce over 500 000 attributable deaths per year. It was found that the annual economic impact of health care-associated infection in the US was approximately US$6.5 billion in 2004 and every US$1 spent on hand hygiene promotion could result in a US$23.7 benefit. Education plays a key role in setting a good practice base in hand hygiene.

The traditional training methods for students of health related disciplines include in-class lecture and practical, but students seldom receive individualized instructions and feedbacks due to limited manpower and facilities. Hence, there is an urgent need of designing an automated and individualized training system. This proposed project aims to design and test an innovative education tool for hand hygiene, with the following specific objectives:

1. develop a prototype of the AI-empowered AR system for hand hygiene training and assessment in undergraduates of health-related disciplines;
2. conduct a non-inferiority randomized controlled trial (RCT) to test the effectiveness of this AI-based AR training system on knowledge and practice of hand hygiene in students of health related disciplines;
3. evaluate the effects of the AI-based AR training system and collect feedbacks from participants.

If demonstrated effective, this system can be integrated into the curriculum of students of health related disciplines as an e-learning approach. In future, this system can also be used at point-of-care for real-time monitoring and audit of healthcare workers in other healthcare settings.

Methods Study design: This is a non-inferiority two-arm RCT.

Subject recruitment Cross-discipline undergraduate students who are taking subjects in School of Nursing, School of Optometry, and Department of Applied Biology and Chemical Technology will be recruited during January to June 2024. The participants will subscribe a timeslot of training sessions (each session has 4 participants). All participants who attend the same session will be assigned to the same group by cluster randomization. A statistician will assign the groups in advance by a random number generator. The participants will not be informed about their group before arriving the study site (the Squina International Centre for Infection Control in PolyU).

Sample size for a cluster randomized controlled trial Assuming that the number of participant in each cluster ranges from 5 to 20, there will be approximately 6-20 clusters in each group. The standard deviation of subjects is assumed to be 2.00, the intracluster correlation coefficient is 0.010, and the coefficient of variation of cluster sizes is 0.500. The sample size of 200-240 can achieve >91% power to detect a difference between two group means (confidence interval -1.0 and 1.0) in a two-sided t-test with a significance level of 0.050.

Pre-intervention assessment During the site visit, the participants will sign a consent form first and be asked to fill in a questionnaire on knowledge and practice of hand hygiene.

Participants will need to put fluorescent powder on both of their hands and then perform hand washing using liquid soap without any instructions (1st HW attempt). The camera installed above the hand-wash basins will take videos of both hands (no face nor other parts of the body) during this procedure. After washing hands, participants will need to scan fluorescent powder remained on their hands in a hand scanner (The Semmelweis Scanner™). The recorded hand washing videos will be used to train the AI algorithms for automatic image processing and assessments. Two IPC experts will judge the quality of hand hygiene in these videos, which will be adopted as ground truth in image processing. The percentage of fluorescent gel residual on hands shown in the hand scanner will be used as an objective assessment for efficacy of hand washing in individual participants.

Interventions groups

Group A (AI-based AR training system) Participants will use the AI-empowered AR training system to get individualized assessments of their hand wash performance. They will not be informed about the hand scanner results.

Group B (Hand scanner + Video training) Participants will use an existing education program that has been demonstrated effective in our previous study. In brief, the participants will be informed about their hand scanner results during the first HW attempt. They will also watch a training video about the 7 steps of hand washing.

Post-intervention assessment The participants will take the second HW attempt by putting fluorescent powder on both of their hands again and then perform hand wash with videos recorded. After washing hands, participants will scan fluorescent powder remained on their hands again in a hand scanner.

Participants will also be asked to fill in a questionnaire on knowledge and practice of hand hygiene as part of post-intervention assessment.

Randomization, allocation concealment, and blinding Participants will be randomly assigned to the intervention groups through a process of randomization performed by a statistician who will not be involved in subject recruitment. The participants will be blinded. The RAs involved in subject recruitment and the IPC experts who judge the quality of hand hygiene will be blinded to the groupings. The RAs and student assistants involved in hand hygiene video recording at the study site will not be blinded because they will be giving instructions to the participants.