Document Type
Article
Abstract
Objective: To evaluate an integrated machine learning model using routinely collected organizational data to identify nurses at increased risk for burnout.
Background: Health care worker burnout directly threatens patient safety, quality of care, and workforce stability. Although nurses experience significantly higher burnout rates than physicians and face distinct operational stressors (eg, staffing shortages, continuous bedside care demands), most predictive modeling efforts have focused on physicians. Current approaches to identifying burnout typically rely on voluntary surveys, but advances in data science and machine learning have created new opportunities to move beyond self-reported measures.
Methods: This retrospective observational study included 1,177 nurses from a large health system in the Midwest. We integrated multiple routinely collected administrative sources to create a comprehensive dataset that included information from the electronic health record, human resources, and annual health system surveys. We developed a machine learning model using the XGBoost (extreme gradient boosting) framework. The model was trained using 80% of the sample and evaluated in the remaining 20% of the test sample (n =236). Model performance was assessed using standard classification metrics. The target variable was self-reported burnout assessed using a validated 2-item instrument measuring emotional exhaustion and depersonalization.
Results: The machine learning model demonstrated strong performance in identifying nurses who met the criteria for burnout. The model achieved an area-under-the-receiver-operating-characteristic curve of 0.81, indicating good discrimination between nurses with and without burnout. The model identified 81.3% (bootstrap 95% CI = 71.3%–88.3%) of nurses were experiencing burnout (sensitivity) while maintaining a positive predictive value of 65.0% (bootstrap 95% CI = 55.3%–73.6%), and F1 score of 0.72 (bootstrap 95% CI = 0.64–0.79).
Conclusion: Using integrated data sources, we developed a model that demonstrated strong performance in prospectively identifying nurses who were experiencing burnout. This is the first step to providing health systems with an actionable foundation to implement targeted, system-level workforce interventions.
DOI
https://doi.org/10.52519/ACEQI.26.2.2.a2
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Davoudpour S, Vaidyanathan M, Beidas RS, Mulroe H, Brandon E, Lloyd M, Olson GM, Pandey M, Blumenthal D, Cheema B, Agarwal G. Validating a Predictive Model of Nurse Burnout Using Integrated Data Sources. Advances in Cancer Education and Quality Improvement. 2026; 2(2). doi: https://doi.org/10.52519/ACEQI.26.2.2.a2.
