Using Explainable Supervised Machine Learning to Predict Burnout in Healthcare Professionals.

ABSTRACT

Burnout in healthcare professionals (HCPs) is a multi-factorial problem. There are limited studies utilizing machine learning approaches to predict HCPs' burnout during the COVID-19 pandemic. A survey consisting of demographic characteristics and work system factors was administered to 450 HCPs during the pandemic (participation rate 59.3%). The highest performing machine learning model had an area under the receiver operating curve of 0.81. The eight key features that best predicted burnout are excessive workload, inadequate staffing, administrative burden, professional relationships, organizational culture, values and expectations, intrinsic motivation, and work-life integration. These findings provide evidence for resource allocation and implementation of interventions to reduce HCPs' burnout and improve the quality of care.