AccScience Publishing / AIH / Online First / DOI: 10.36922/aih.5127
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ORIGINAL RESEARCH ARTICLE

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

Teray Johnson1* ,  Sameh Shamroukh2
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1 Department of Data Analytics, Faculty of Data Analytics, Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, United States of America
2 Department of Information Technology and Decision Sciences, G. Brint Ryan College of Business, University of North Texas, Denton, Texas, United States of America
AIH, 5127 https://doi.org/10.36922/aih.5127
Submitted: 12 October 2024 | Revised: 23 January 2025 | Accepted: 17 February 2025 | Published: 3 March 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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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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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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