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
Open accessAbstract
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.
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Several studies have used AI to predict or manage burnout based on physiological indicators or to predict the degree of burnout.
“Several studies have used AI to predict or manage burnout based on physiological indicators or to predict the degree of burnout [61-64].”
From Development of an artificial intelligence-based tailored mobile intervention for nurse burnout: single-arm trial (2024) · cites this paper as [64]
No previous study has incorporated AI to provide tailored programs to users to reduce burnout.
“To the best of our knowledge, no previous study has incorporated AI to provide tailored programs to users to reduce burnout.”
From Development of an artificial intelligence-based tailored mobile intervention for nurse burnout: single-arm trial (2024) · cites this paper as [64]
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Cites
Mental health and the COVID-19 pandemic
Pfefferbaum, B., & North, C. S. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2008017 · Cited by 4,378 · see citing works
Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature
Kim, H.-G., Cheon, E.-J., Bai, D.-S., Lee, Y. H., & Koo, B.-H. (2018) · Psychiatry Investigation · doi:10.30773/pi.2017.08.17 · Cited by 2,308 · see citing works
Depression, anxiety, stress levels of physicians and associated factors in Covid-19 pandemics
Elbay, R. Y., Kurtulmuş, A., Arpacıoğlu, S., & Karadere, E. (2020) · Psychiatry Research · doi:10.1016/j.psychres.2020.113130 · Cited by 845 · see citing works
Morgantini, L. A., Naha, U., Wang, H., Francavilla, S., Acar, Ö., Flores, J. M., Crivellaro, S., Moreira, D., Abern, M. R., Eklund, M., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238217 · Cited by 767 · see citing works
Barello, S., Palamenghi, L., & Graffigna, G. (2020) · Psychiatry Research · doi:10.1016/j.psychres.2020.113129 · Cited by 713 · see citing works
the Society of General Internal Medicine (SGIM) Career Satisfaction Study Group (CSSG), Linzer, M., Konrad, T. R., Douglas, J., McMurray, J. E., Pathman, D. E., Williams, E. S., Schwartz, M. D., Gerrity, M., Scheckler, W. E., et al. (2000) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2000.05239.x · Cited by 411 · see citing works
Matsuo, T., Kobayashi, D., Taki, F., Sakamoto, F., Uehara, Y., Mori, N., & Fukui, T. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.17271 · Cited by 404 · see citing works
Worklife and Wellness in Academic General Internal Medicine: Results from a National Survey
Linzer, M., Poplau, S., Babbott, S. F., Collins, T. C., Guzman-Corrales, L. M., Menk, J. S., Murphy, M. L., & Ovington, K. (2016) · Journal of General Internal Medicine · doi:10.1007/s11606-016-3720-4 · Cited by 256 · see citing works
Wilson, W., Raj, J. P., Rao, S. R., Ghiya, M., Nedungalaparambil, N. M., Mundra, H., & Mathew, R. (2020) · Indian Journal of Psychological Medicine · doi:10.1177/0253717620933992 · Cited by 255 · see citing works
Olson, K., Sinsky, C., Rinne, S. T., Long, T., Vender, R., Mukherjee, S., Bennick, M., & Linzer, M. (2019) · Stress and Health · doi:10.1002/smi.2849 · Cited by 202 · see citing works
A Key Differential Diagnosis for Physicians—Major Depression or Burnout?
Oquendo, M. A., Bernstein, C. A., & Mayer, L. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.1332 · Cited by 93 · see citing works
Chandler, C., Foltz, P. W., & Elvevåg, B. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sbz105 · Cited by 61 · see citing works
ECG signatures of psychological stress
Lampert, R. J. (2015) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2015.08.005 · Cited by 55 · see citing works
The association between acute mental stress and abnormal left atrial electrophysiology
O’Neal, W. T., Hammadah, M., Sandesara, P. B., Almuwaqqat, Z., Samman‐Tahhan, A., Gafeer, M. M., Abdelhadi, N., Wilmot, K. A., Mheid, I. A., Bremner, J. D., et al. (2017) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.13295 · Cited by 18 · see citing works
Abnormal P-wave axis and myocardial ischemia development during mental stress
Almuwaqqat, Z., O’Neal, W. T., Hammadah, M., Lima, B. B., Bremner, J. D., Soliman, E. Z., Shah, A. J., Quyyumi, A. A., & Vaccarino, V. (2020) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2020.02.019 · Cited by 4 · see citing works
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Cho, A., Cha, C., & Baek, G. (2024) · Journal of Medical Internet Research · doi:10.2196/54029 · Cited by 29 · see citing works
Levin, C., Naimi, E., & Saban, M. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105566 · Cited by 17 · see citing works
- Authors
- Gupta, M. D., Bansal, A., Sarkar, P. G., Girish, M. P., Jha, M., Yusuf, J., Kumar, S., Kumar, S., Jain, A., Kathuria, S., Saijpaul, R., Mishra, A., Malhotra, V., Yadav, R., Ramakrishanan, S., Malhotra, R. K., Batra, V., Shetty, M. K., Sharma, N., Mukhopadhyay, S., Garg, S., & Gupta, A.
- Affiliations
- Rajendra Institute of Medical Sciences (IN)
- Icahn School of Medicine at Mount Sinai (US)
- Lok Nayak Jai Prakash Narayan Hospital (IN)
- Maulana Azad Medical College (IN)
- All India Institute of Medical Sciences (IN)
- Indraprastha Institute of Information Technology Delhi (IN)
- Corresponding author
- Gupta, Mohit D.
- ORCID
- Venue
- Indian Heart Journal
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 73
- Issue
- 1
- Pages
- 109-113
- ISSN
- 0019-4832 (ISSN-L)
- PMC
- PMC7683295
- OpenAlex
- W3106887551
- Cited by
- 25
- References
- 15
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 8 · 2023: 2 · 2022: 8 · 2021: 4
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Indian Heart Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Health Professions › General Health Professions › Healthcare professionals’ stress and burnout
- Health Sciences › Health Professions › Occupational Therapy › Occupational Health and Burnout
- Keywords
- burnout, healthcare workers, COVID-19 pandemic, electrocardiogram, artificial intelligence, convolutional neural networks, psychological stress, burnout prevalence
- MeSH terms
- COVID-19, SARS-CoV-2, Artificial Intelligence, Burnout, Professional, Cross-Sectional Studies, Electrocardiography, Female, Health Personnel, Humans, India, Male, Research Design, Prevalence
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