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

Mohit D. Gupta, Ankit Bansal, Prattay G. Sarkar, M. P. Girish, Manish Jha, Jamal Yusuf, Suresh Kumar, Satish Kumar, Ajeet Jain, Sanjeev Kathuria, Rajni Saijpaul, Anurag Mishra, Vikas Malhotra, Rakesh Yadav, S. Ramakrishanan, Rajeev K. Malhotra, Vishal Batra, Manu Kumar Shetty, Nandini Sharma, Saibal MukhopadhyaySandeep Garg, Anubha Gupta

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)109-113
Number of pages5
JournalIndian heart journal
Volume73
Issue number1
DOIs
StatePublished - Jan 1 2021
Externally publishedYes

Keywords

  • Artificial intelligence
  • Burnout
  • Covid-19
  • Electrocardiogram
  • Stress

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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