Monitoring Redeployment-Associated Burnout in Healthcare Workers: A Real-Time Approach Using Ecological Momentary Assessment

What are the main findings? Ecological momentary assessment (EMA) methods can capture dynamic trends in clinician burnout during a prolonged hospital crisis. Redeployment and higher caseloads increased burnout, with effects persisting after return to usual roles. What are the implications of the main findings? Redeployment is a commonly used strategy to handle increased needs for personnel during prolonged hospital crises. However, the data suggest that redeployment presents risks for persistent burnout under some conditions and for some personnel. Preventative efforts to reduce these risks are needed. This novel application of EMA used a low-intensity high-frequency surveillance approach to obtain information in real time about variations in the intensity of workplace demands and their association with employee burnout. This approach can be used to guide the deployment of burnout-mitigation efforts to high-risk groups during periods of high demands. Highlights: Background/Objectives: Ecological momentary assessment (EMA) is a methodology that offers a real-time approach to monitoring clinician well-being, but its utility during high-intensity operational periods remains underexplored. This study examines the feasibility and performance of an EMA-based system for tracking clinical responsibilities and burnout among healthcare workers during the first year of the COVID-19 pandemic. Methods: Utilizing an intensive longitudinal design, 398 healthcare workers, including physicians, physician assistants, nurses, and trainees, completed brief EMA surveys every five days from April 2020 to March 2021. Burnout was assessed with a validated single-item measure and analyzed in relation to redeployment status and hospital caseloads. Results: The EMA approach successfully captured meaningful temporal fluctuations in burnout. Redeployment was associated with higher burnout levels (b = 0.125; p = 0.01), and rising caseloads amplified this effect (interaction b = 0.169; p = 0.001). Nurses showed the strongest caseload-related increases in burnout (b = 0.359; p < 0.001). These patterns persisted even after individuals returned to their usual roles. Conclusions: This study demonstrates that EMA is a scalable and sensitive approach for continuous burnout surveillance, capable of detecting role-specific and context-dependent stress responses in real time. EMA-based monitoring can support early identification of at-risk groups, guide staffing and redeployment decisions, and inform timely organizational interventions during crises and other periods of operational strain.