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

Abdulaziz Alkattan1, Allison A Norful2, Cynthia X Pan3,4

  • 1Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.

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Summary
This summary is machine-generated.

Ecological momentary assessment (EMA) effectively tracked healthcare worker burnout during the COVID-19 pandemic. Redeployment and increased caseloads significantly elevated burnout, especially in nurses, highlighting EMA

Keywords:
COVID-19burnoutecological momentary assessmentprofessionalpsychologicalresilience

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Area of Science:

  • Occupational Health
  • Psychiatry
  • Public Health

Background:

  • Ecological momentary assessment (EMA) offers real-time monitoring of clinician well-being.
  • The utility of EMA during high-intensity operational periods, like the COVID-19 pandemic, is underexplored.
  • This study investigated EMA for tracking clinical responsibilities and burnout in healthcare workers.

Purpose of the Study:

  • To examine the feasibility and performance of an EMA-based system for monitoring healthcare worker burnout.
  • To analyze burnout in relation to redeployment status and hospital caseloads during the COVID-19 pandemic.
  • To identify role-specific and context-dependent stress responses in real time.

Main Methods:

  • An intensive longitudinal design was employed with 398 healthcare workers (physicians, physician assistants, nurses, trainees).
  • Participants completed brief EMA surveys every five days from April 2020 to March 2021.
  • Burnout was assessed using a single-item measure and analyzed against redeployment and caseload data.

Main Results:

  • EMA successfully captured temporal fluctuations in burnout.
  • Redeployment was linked to higher burnout (b = 0.125; p = 0.01), amplified by rising caseloads (interaction b = 0.169; p = 0.001).
  • Nurses experienced the most significant caseload-related burnout increases (b = 0.359; p < 0.001), persisting post-redeployment.

Conclusions:

  • EMA is a scalable and sensitive method for continuous burnout surveillance in healthcare.
  • The system detects real-time, role-specific, and context-dependent stress responses.
  • EMA can inform early identification of at-risk groups and guide organizational interventions during crises.