Why the study?
Current approaches to monitor burnout in health care professionals are reactive and require additional work such as survey completion.
Can unobtrusive sensors measuring physiological data proactively identify burnout in health care professionals?
Can unobtrusive sensors measuring physiological data proactively identify burnout in health care professionals?
Real-time physiological sensor data (HRV and EDA) can proactively identify burnout and workload in healthcare professionals.
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May enable proactive burnout monitoring in HCPs; hypothesis-generating pending validation in clinical workflows.
Health care professionals (HCPs) are frequently exposed to Human Factors/Ergonomics (HFE) issues that result in stress, adversely affecting their health and negatively impacting the quality of care. Chronic stress can result in burnout, with negative implications for individuals, health care organizations, and patients. Current approaches to monitor burnout are reactive and require additional work (e.g., survey completion). In this study, we pilot a methodology using unobtrusive sensors and advanced statistics to bridge this important gap. We collected two types of physiological data - heart rate variability (HRV) and electrodermal activity (EDA) - and measures of perceived workload and burnout from three HCPs in a COVID-19 Testing Laboratory. We identified meaningful relationships between physiological data, workload, and burnout, demonstrating that burnout can be identified proactively using real-time sensor data. Future work will expand the timeframe of data collection and include a larger sample with different types of HCPs.
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Leite et al. (2022) studied this question.
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