Wednesday, December 4, 2024
12/4/2024
Project Summary Emergency department clinicians (ECs) regularly experience among the highest rates of psychological distress. Manifesting in poor well-being and burnout, EC psychological distress can lead to adverse outcomes for patient care, institutions (e.g., costly attrition), and individual ECs (e.g., illness and death). On-going surveys conducted by the research team since 2020 have collected validated measures of an EC well-being index and burnout score for three types of ECs (attendings, Advanced Practice Clinicians, and residents) at seven emergency department locations at a large academic health system in upstate South Carolina. Locations span urban to rural settings. Our interdisciplinary team proposes to link key contextual factors of institutional stressors (including staffing strain and patient congestion) with coincident contextual factors (including external pandemic status and individual demographic characteristics) to measure and visualize well- being and burnout. Aim 1 will quantify the relationships between stressors and context with well-being and burnout. Generalized linear mixed effects models will consider contributors of each predictor on both outcomes, and factor analysis will compare the relative contribution of each predictor. Machine learning methods will be used to develop a predictive model to evaluate future burnout risk. In Aim 2, we will develop a visualization framework to display predictors and corresponding real-time predictions of well-being. Iterative refinement will improve the visualization channels, user performance, and support accessibility. Mixed-method validation with EC leadership will involve interviews to probe interpretation of data insights with the graphical framework and eye-tracking to evaluate performance and speed. Consistent with the exploratory nature of an R21, the visualization will be developed with EC-informed hypothetical values. A follow-on study will be proposed to integrate the work both aims and to implement and evaluate the use of the visualization to improve interpretation in practice. We anticipate that this research will both spur further work to develop decision- support tools to address modifiable factors to improve well-being and have immediate impact in understanding contributors to EC well-being and burnout. This work seeks to improve EC experience to interrupt the negative feedback loop that drives adverse outcomes in patient care, EC turnover, and institutional goals to ultimately improve EC well-being and patient care.
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