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.

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

SUBMITTER: Gupta MD 

PROVIDER: S-EPMC7683295 | biostudies-literature | 2021 Jan-Feb

REPOSITORIES: biostudies-literature