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Using Computer Vision and Depth Sensing to Measure Healthcare Worker-Patient Contacts and Personal Protective Equipment Adherence Within Hospital Rooms
Background. We determined the feasibility of using computer vision and depth sensing to detect healthcare worker (HCW)-patient contacts to estimate both hand hygiene (HH) opportunities and personal protective equipment (PPE) adherence. Methods. We used multiple Microsoft Kinects to track the 3-dimen...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4757761/ https://www.ncbi.nlm.nih.gov/pubmed/26949712 http://dx.doi.org/10.1093/ofid/ofv200 |
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author | Chen, Junyang Cremer, James F. Zarei, Kasra Segre, Alberto M. Polgreen, Philip M. |
author_facet | Chen, Junyang Cremer, James F. Zarei, Kasra Segre, Alberto M. Polgreen, Philip M. |
author_sort | Chen, Junyang |
collection | PubMed |
description | Background. We determined the feasibility of using computer vision and depth sensing to detect healthcare worker (HCW)-patient contacts to estimate both hand hygiene (HH) opportunities and personal protective equipment (PPE) adherence. Methods. We used multiple Microsoft Kinects to track the 3-dimensional movement of HCWs and their hands within hospital rooms. We applied computer vision techniques to recognize and determine the position of fiducial markers attached to the patient's bed to determine the location of the HCW's hands with respect to the bed. To measure our system's ability to detect HCW-patient contacts, we counted each time a HCW's hands entered a virtual rectangular box aligned with a patient bed. To measure PPE adherence, we identified the hands, torso, and face of each HCW on room entry, determined the color of each body area, and compared it with the color of gloves, gowns, and face masks. We independently examined a ground truth video recording and compared it with our system's results. Results. Overall, for touch detection, the sensitivity was 99.7%, with a positive predictive value of 98.7%. For gowned entrances, sensitivity was 100.0% and specificity was 98.15%. For masked entrances, sensitivity was 100.0% and specificity was 98.75%; for gloved entrances, the sensitivity was 86.21% and specificity was 98.28%. Conclusions. Using computer vision and depth sensing, we can estimate potential HH opportunities at the bedside and also estimate adherence to PPE. Our fine-grained estimates of how and how often HCWs interact directly with patients can inform a wide range of patient-safety research. |
format | Online Article Text |
id | pubmed-4757761 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-47577612016-03-04 Using Computer Vision and Depth Sensing to Measure Healthcare Worker-Patient Contacts and Personal Protective Equipment Adherence Within Hospital Rooms Chen, Junyang Cremer, James F. Zarei, Kasra Segre, Alberto M. Polgreen, Philip M. Open Forum Infect Dis Major Articles Background. We determined the feasibility of using computer vision and depth sensing to detect healthcare worker (HCW)-patient contacts to estimate both hand hygiene (HH) opportunities and personal protective equipment (PPE) adherence. Methods. We used multiple Microsoft Kinects to track the 3-dimensional movement of HCWs and their hands within hospital rooms. We applied computer vision techniques to recognize and determine the position of fiducial markers attached to the patient's bed to determine the location of the HCW's hands with respect to the bed. To measure our system's ability to detect HCW-patient contacts, we counted each time a HCW's hands entered a virtual rectangular box aligned with a patient bed. To measure PPE adherence, we identified the hands, torso, and face of each HCW on room entry, determined the color of each body area, and compared it with the color of gloves, gowns, and face masks. We independently examined a ground truth video recording and compared it with our system's results. Results. Overall, for touch detection, the sensitivity was 99.7%, with a positive predictive value of 98.7%. For gowned entrances, sensitivity was 100.0% and specificity was 98.15%. For masked entrances, sensitivity was 100.0% and specificity was 98.75%; for gloved entrances, the sensitivity was 86.21% and specificity was 98.28%. Conclusions. Using computer vision and depth sensing, we can estimate potential HH opportunities at the bedside and also estimate adherence to PPE. Our fine-grained estimates of how and how often HCWs interact directly with patients can inform a wide range of patient-safety research. Oxford University Press 2015-12-28 /pmc/articles/PMC4757761/ /pubmed/26949712 http://dx.doi.org/10.1093/ofid/ofv200 Text en © The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com. |
spellingShingle | Major Articles Chen, Junyang Cremer, James F. Zarei, Kasra Segre, Alberto M. Polgreen, Philip M. Using Computer Vision and Depth Sensing to Measure Healthcare Worker-Patient Contacts and Personal Protective Equipment Adherence Within Hospital Rooms |
title | Using Computer Vision and Depth Sensing to Measure Healthcare Worker-Patient Contacts and Personal Protective Equipment Adherence Within Hospital Rooms |
title_full | Using Computer Vision and Depth Sensing to Measure Healthcare Worker-Patient Contacts and Personal Protective Equipment Adherence Within Hospital Rooms |
title_fullStr | Using Computer Vision and Depth Sensing to Measure Healthcare Worker-Patient Contacts and Personal Protective Equipment Adherence Within Hospital Rooms |
title_full_unstemmed | Using Computer Vision and Depth Sensing to Measure Healthcare Worker-Patient Contacts and Personal Protective Equipment Adherence Within Hospital Rooms |
title_short | Using Computer Vision and Depth Sensing to Measure Healthcare Worker-Patient Contacts and Personal Protective Equipment Adherence Within Hospital Rooms |
title_sort | using computer vision and depth sensing to measure healthcare worker-patient contacts and personal protective equipment adherence within hospital rooms |
topic | Major Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4757761/ https://www.ncbi.nlm.nih.gov/pubmed/26949712 http://dx.doi.org/10.1093/ofid/ofv200 |
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