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Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study
BACKGROUND: There is no large contemporary data from India to see the prevalence of burnout in HCWs in covid era. Burnout and mental stress is associated with electrocardiographic changes detectable by artificial intelligence (AI). OBJECTIVE: The present study aims to estimate the prevalence of burn...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683295/ https://www.ncbi.nlm.nih.gov/pubmed/33714394 http://dx.doi.org/10.1016/j.ihj.2020.11.145 |
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author | Gupta, Mohit D. Bansal, Ankit Sarkar, Prattay G. Girish, M.P. Jha, Manish Yusuf, Jamal Kumar, Suresh Kumar, Satish Jain, Ajeet Kathuria, Sanjeev Saijpaul, Rajni Mishra, Anurag Malhotra, Vikas Yadav, Rakesh Ramakrishanan, S. Malhotra, Rajeev K. Batra, Vishal Shetty, Manu Kumar Sharma, Nandini Mukhopadhyay, Saibal Garg, Sandeep Gupta, Anubha |
author_facet | Gupta, Mohit D. Bansal, Ankit Sarkar, Prattay G. Girish, M.P. Jha, Manish Yusuf, Jamal Kumar, Suresh Kumar, Satish Jain, Ajeet Kathuria, Sanjeev Saijpaul, Rajni Mishra, Anurag Malhotra, Vikas Yadav, Rakesh Ramakrishanan, S. Malhotra, Rajeev K. Batra, Vishal Shetty, Manu Kumar Sharma, Nandini Mukhopadhyay, Saibal Garg, Sandeep Gupta, Anubha |
author_sort | Gupta, Mohit D. |
collection | PubMed |
description | BACKGROUND: There is no large contemporary data from India to see the prevalence of burnout in HCWs in covid era. Burnout and mental stress is associated with electrocardiographic changes detectable by artificial intelligence (AI). OBJECTIVE: The present study aims to estimate the prevalence of burnout in HCWs in COVID-19 era using Mini Z-scale and to develop predictive AI model to detect burnout in HCWs in COVID-19 era. METHODS: This is an observational and cross-sectional study to evaluate the presence of burnout in HCWs in academic tertiary care centres of North India in the COVID-19 era. At least 900 participants will be enrolled in this study from four leading premier government-funded/public-private centres of North India. Each study centre will be asked to recruit HCWs by approaching them through various listed ways for participation in the study. Interested participants after initial screening and meeting the eligibility criteria, will be asked to fill the questionnaire (having demographic and work related with Mini Z questionnaire) to assess burnout. The healthcare workers will include physicians at all levels of training, nursing staff and paramedical staff who are involved directly or indirectly in COVID-19 care. The analysis of the raw electrocardiogram (ECG) data and development of algorithm using convolutional neural networks (CNN) will be done by experts. CONCLUSIONS: In Summary, we propose that ECG data generated from the people with burnout can be utilized to develop AI-enabled model to predict the presence of stress and burnout in HCWs in COVID-19 era. |
format | Online Article Text |
id | pubmed-7683295 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-76832952020-11-24 Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study Gupta, Mohit D. Bansal, Ankit Sarkar, Prattay G. Girish, M.P. Jha, Manish Yusuf, Jamal Kumar, Suresh Kumar, Satish Jain, Ajeet Kathuria, Sanjeev Saijpaul, Rajni Mishra, Anurag Malhotra, Vikas Yadav, Rakesh Ramakrishanan, S. Malhotra, Rajeev K. Batra, Vishal Shetty, Manu Kumar Sharma, Nandini Mukhopadhyay, Saibal Garg, Sandeep Gupta, Anubha Indian Heart J Original Article BACKGROUND: There is no large contemporary data from India to see the prevalence of burnout in HCWs in covid era. Burnout and mental stress is associated with electrocardiographic changes detectable by artificial intelligence (AI). OBJECTIVE: The present study aims to estimate the prevalence of burnout in HCWs in COVID-19 era using Mini Z-scale and to develop predictive AI model to detect burnout in HCWs in COVID-19 era. METHODS: This is an observational and cross-sectional study to evaluate the presence of burnout in HCWs in academic tertiary care centres of North India in the COVID-19 era. At least 900 participants will be enrolled in this study from four leading premier government-funded/public-private centres of North India. Each study centre will be asked to recruit HCWs by approaching them through various listed ways for participation in the study. Interested participants after initial screening and meeting the eligibility criteria, will be asked to fill the questionnaire (having demographic and work related with Mini Z questionnaire) to assess burnout. The healthcare workers will include physicians at all levels of training, nursing staff and paramedical staff who are involved directly or indirectly in COVID-19 care. The analysis of the raw electrocardiogram (ECG) data and development of algorithm using convolutional neural networks (CNN) will be done by experts. CONCLUSIONS: In Summary, we propose that ECG data generated from the people with burnout can be utilized to develop AI-enabled model to predict the presence of stress and burnout in HCWs in COVID-19 era. Elsevier 2021 2020-11-24 /pmc/articles/PMC7683295/ /pubmed/33714394 http://dx.doi.org/10.1016/j.ihj.2020.11.145 Text en © 2020 Cardiological Society of India. Published by Elsevier B.V. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Article Gupta, Mohit D. Bansal, Ankit Sarkar, Prattay G. Girish, M.P. Jha, Manish Yusuf, Jamal Kumar, Suresh Kumar, Satish Jain, Ajeet Kathuria, Sanjeev Saijpaul, Rajni Mishra, Anurag Malhotra, Vikas Yadav, Rakesh Ramakrishanan, S. Malhotra, Rajeev K. Batra, Vishal Shetty, Manu Kumar Sharma, Nandini Mukhopadhyay, Saibal Garg, Sandeep Gupta, Anubha Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study |
title | Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study |
title_full | Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study |
title_fullStr | Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study |
title_full_unstemmed | Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study |
title_short | Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study |
title_sort | design and rationale of an intelligent algorithm to detect burnout in healthcare workers in covid era using ecg and artificial intelligence: the brucee-li study |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683295/ https://www.ncbi.nlm.nih.gov/pubmed/33714394 http://dx.doi.org/10.1016/j.ihj.2020.11.145 |
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