Influence of different types of real-time feedback on hand washing quality assessed with neural networks/simulated neural networks

Abstract

Background: Thousands of people die every day around the world from infections acquired in a hospital. Hands are the main pathways of germ transmission during healthcare. Hand hygiene monitoring can be performed using various methods. One of the latest techniques that can combine all is a neural network-based hand hygiene monitoring system. Methods/Design: Each participant performed 3 hand-washing trials, each time receiving different type of feedback. The order in which each participant of the study used the developed applications was strictly defined, thus each hand-washing study session started with performing hand washing using application A, B and C accordingly. All captured videos of hand-wash episodes were saved and later analysed with neural networks. In the end, both evaluation results were compared and evaluated. Results show that when the participants use Application Type A, they perform hand washing much faster, as well as in comparison of Application Type A versus application type C. However, the longest time spent for the hand washing was detected while using the application type B. Conclusion: Study shows that structured guidance provided during the real time hand washing could be associated with better overall performance. The Application C has confirmed its effectiveness. Proving its advantage among other applications, the Application C can be integrated into the clinical environment