AI-Driven Solutions for Predicting and Managing Burnout in Healthcare Professionals

AI-Driven Solutions for Predicting and Managing Burnout in Healthcare Professionals

Wednesday, March 5, 2025 12:00 PM to 1:00 PM · 1 hr. (US/Pacific)
Venetian | Level 1 | Casanova 501
Workforce ConneXtions
Workforce

Information

Note: This poster will be on display at Workforce ConneXtions on Wednesday, March 5, 2025 from 10 am - 4 pm. Meet the author(s) from 12 pm - 1 pm.


Post-pandemic, burnout among healthcare professionals has reached critical levels, impacting both their well-being and the quality of care. This poster presents how AI-driven models can forecast burnout in real-time by analyzing work patterns, patient loads, and mental stress. Leveraging machine learning and predictive analytics, AI provides tailored interventions like workload adjustments and dynamic scheduling. By addressing burnout proactively, healthcare systems can improve staff wellness, reduce turnover, elevate patient care, and foster long-term organizational resilience, while supporting value-based care initiatives.

Sub-Topic Category
Staffing, Retention, and Employee Wellness
Target Audience
Allied Health ProfessionalCNIO/CNOEarly Careerist
Level
Introductory
CEU Type
CAHIMSCPDHTSCPHIMS
Format
60-Minute Best Practice
Learning Objective #1
Identify the main factors contributing to burnout among healthcare professionals, including work patterns, patient loads, and mental stress, through data analysis
Learning Objective #2
Recognize how real-time burnout forecasting and prompt interventions may be achieved by AI-driven models to avoid aggravation
Learning Objective #3
Explain the role of machine learning and predictive analytics in designing tailored interventions such as workload redistribution and dynamic scheduling
Learning Objective #4
Evaluate the effects of AI-driven burnout management on raising patient care standards, decreasing employee attrition, and promoting staff wellness
Learning Objective #5
Explore strategies for integrating AI-powered burnout prevention tools into healthcare workflows while supporting value-based care models
Session #
WFC-17