The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene
© The Author(s) 2018. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:31 |
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| Enthalten in: |
International journal for quality in health care : journal of the International Society for Quality in Health Care - 31(2019), 1 vom: 01. Feb., Seite 36-42 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Awwad, Sari [VerfasserIn] |
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| Links: |
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| Themen: |
Computer-assisted [MeSH] |
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| Anmerkungen: |
Date Completed 02.07.2019 Date Revised 02.07.2019 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/intqhc/mzy099 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 4 | |a The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene |
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| 500 | |a Date Revised 02.07.2019 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2018. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com. | ||
| 520 | |a 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 | ||
| 520 | |a DESIGN: Observation of simulated hand hygiene encounters between a healthcare worker and a patient | ||
| 520 | |a SETTING: Computer laboratory in a university | ||
| 520 | |a PARTICIPANTS: Healthy volunteers | ||
| 520 | |a MAIN OUTCOME MEASURES: Sensitivity and specificity of automatic detection of the first moment of hand hygiene | ||
| 520 | |a 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 | ||
| 520 | |a 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%) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a computer-assisted [MeSH] | |
| 650 | 4 | |a cross infection [MeSH] | |
| 650 | 4 | |a hand hygiene [MeSH] | |
| 650 | 4 | |a healthcare [MeSH] | |
| 650 | 4 | |a image processing | |
| 650 | 4 | |a quality assurance | |
| 650 | 7 | |a Hand Sanitizers |2 NLM | |
| 700 | 1 | |a Tarvade, Sanjay |e verfasserin |4 aut | |
| 700 | 1 | |a Piccardi, Massimo |e verfasserin |4 aut | |
| 700 | 1 | |a Gattas, David J |e verfasserin |4 aut | |
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