The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene

Awwad, S; Tarvade, S; Piccardi, M; Gattas, DJ

The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene

Awwad, S Tarvade, S Piccardi, M

Gattas, DJ

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Publication Type:: Journal Article
Citation:: International Journal for Quality in Health Care, 2019, 31 (1), pp. 36 - 42
Issue Date:: 2019-02-01

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Field	Value	Language
dc.contributor.author	Awwad, S	en_US
dc.contributor.author	Tarvade, S	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.contributor.author	Gattas, DJ	en_US
dc.date.available	2020-05-25T19:18:47Z
dc.date.issued	2019-02-01	en_US
dc.identifier.citation	International Journal for Quality in Health Care, 2019, 31 (1), pp. 36 - 42	en_US
dc.identifier.issn	1353-4505	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128996
dc.description.abstract	© 2018 The Author(s). 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.	en_US
dc.relation.ispartof	International Journal for Quality in Health Care	en_US
dc.relation.isbasedon	10.1093/intqhc/mzy099	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Health Policy & Services	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Cross Infection	en_US
dc.subject.mesh	Privacy	en_US
dc.subject.mesh	Patient Simulation	en_US
dc.subject.mesh	Image Processing, Computer-Assisted	en_US
dc.subject.mesh	Health Personnel	en_US
dc.subject.mesh	Quality of Health Care	en_US
dc.subject.mesh	Guideline Adherence	en_US
dc.subject.mesh	Hand Disinfection	en_US
dc.subject.mesh	Hand Sanitizers	en_US
dc.title	The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	31	en_US
utslib.for	11 Medical and Health Sciences	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access	*
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	31	en_US

Abstract:

© 2018 The Author(s). 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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http://hdl.handle.net/10453/128996