The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene.
- Publication Type:
- Journal Article
- Int J Qual Health Care, 2019, 31 (1), pp. 36 - 42
- Issue Date:
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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.
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