Sari Awwad1, Sanjay Tarvade2, Massimo Piccardi1, David J Gattas2. 1. Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia. 2. Intensive Care Unit, Royal Prince Alfred Hospital, University of Sydney, Camperdown, NSW, Australia.
Abstract
OBJECTIVES: (i) To demonstrate the feasibility of automated, direct observation and collection of hand hygiene data, (ii) to develop computer visual methods capable of reporting compliance with moment 1 (the performance of hand hygiene before touching a patient) and (iii) to report the diagnostic accuracy of automated, direct observation of moment 1. DESIGN: Observation of simulated hand hygiene encounters between a healthcare worker and a patient. SETTING: Computer laboratory in a university. PARTICIPANTS: Healthy volunteers. MAIN OUTCOME MEASURES: Sensitivity and specificity of automatic detection of the first moment of hand hygiene. METHODS: We captured video and depth images using a Kinect camera and developed computer visual methods to automatically detect the use of alcohol-based hand rub (ABHR), rubbing together of hands and subsequent contact of the patient by the healthcare worker using depth imagery. RESULTS: We acquired images from 18 different simulated hand hygiene encounters where the healthcare worker complied with the first moment of hand hygiene, and 8 encounters where they did not. The diagnostic accuracy of determining that ABHR was dispensed and that the patient was touched was excellent (sensitivity 100%, specificity 100%). The diagnostic accuracy of determining that the hands were rubbed together after dispensing ABHR was good (sensitivity 83%, specificity 88%). CONCLUSIONS: We have demonstrated that it is possible to automate the direct observation of hand hygiene performance in a simulated clinical setting. We used cheap, widely available consumer technology and depth imagery which potentially increases clinical application and decreases privacy concerns.
OBJECTIVES: (i) To demonstrate the feasibility of automated, direct observation and collection of hand hygiene data, (ii) to develop computer visual methods capable of reporting compliance with moment 1 (the performance of hand hygiene before touching a patient) and (iii) to report the diagnostic accuracy of automated, direct observation of moment 1. DESIGN: Observation of simulated hand hygiene encounters between a healthcare worker and a patient. SETTING: Computer laboratory in a university. PARTICIPANTS: Healthy volunteers. MAIN OUTCOME MEASURES: Sensitivity and specificity of automatic detection of the first moment of hand hygiene. METHODS: We captured video and depth images using a Kinect camera and developed computer visual methods to automatically detect the use of alcohol-based hand rub (ABHR), rubbing together of hands and subsequent contact of the patient by the healthcare worker using depth imagery. RESULTS: We acquired images from 18 different simulated hand hygiene encounters where the healthcare worker complied with the first moment of hand hygiene, and 8 encounters where they did not. The diagnostic accuracy of determining that ABHR was dispensed and that the patient was touched was excellent (sensitivity 100%, specificity 100%). The diagnostic accuracy of determining that the hands were rubbed together after dispensing ABHR was good (sensitivity 83%, specificity 88%). CONCLUSIONS: We have demonstrated that it is possible to automate the direct observation of hand hygiene performance in a simulated clinical setting. We used cheap, widely available consumer technology and depth imagery which potentially increases clinical application and decreases privacy concerns.
Authors: Amit Singh; Albert Haque; Alexandre Alahi; Serena Yeung; Michelle Guo; Jill R Glassman; William Beninati; Terry Platchek; Li Fei-Fei; Arnold Milstein Journal: J Am Med Inform Assoc Date: 2020-08-01 Impact factor: 4.497