Assessing the predictive influence of organizational culture on employee burnout within health systems: Insights and strategic implications

Abstract

Organizational culture (OC) affects every workplace, yet few studies have explored the relationship between OC and burnout using machine learning methods, which could provide new insights. This exploratory study employed a random forest algorithm to examine the relationship between OC and burnout among employees in health systems, aiming to determine whether OC can predict employee burnout. A 57-question survey assessing perceptions of OC and burnout was administered to employees across various health systems in the United States, yielding 67 responses. These survey results were used to train and test the random forest model. The findings indicated that several aspects of OC, such as job interference with home life, are predictive of burnout. Based on these preliminary results, employers should be aware of their organization’s culture and actively work to improve it to alleviate employee burnout. Leaders should implement strategies, such as allowing flexible work schedules to promote work-life balance and providing employees with the necessary resources to excel in their roles. The model also highlights the significant impact of OC on burnout, suggesting that a variety of burnout symptoms may signal the need for improvements in OC. This study serves as a starting point for future research to further explore how OC predicts burnout, while emphasizing the importance of cultivating a positive OC.

Keywords

Burnout / Health systems / Employees / Organizational culture / COVID-19 pandemic / Random forest / Machine learning

Funding

None.

Conflict of interest

The authors declare that they have no competing interests.

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