TY - JOUR
T1 - Detecting Burnout of Health Care Professionals in a COVID-19 Testing Laboratory
AU - Carvalho Manhaes Leite, Carolina
AU - Chronopoulou, Alexandra
AU - Irem Yuceel, Adviye
AU - Wooldridge, Abigail R.
PY - 2022/9
Y1 - 2022/9
N2 - 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.
AB - 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.
U2 - 10.1177/1071181322661066
DO - 10.1177/1071181322661066
M3 - Conference article
SN - 2169-5067
VL - 66
SP - 570
EP - 574
JO - Proceedings of the Human Factors and Ergonomics Society Annual Meeting
JF - Proceedings of the Human Factors and Ergonomics Society Annual Meeting
IS - 1
ER -