UltraCon: Machine learning predicts sonographer burnout

AUSTIN, TX -- A machine-learning algorithm can predict burnout among sonographers, though established burnout indices may be enough, according to research presented April 9 at UltraCon 2024. In her talk, Jennifer Bagley from the University of Oklahoma highlighted her team’s results, which show that a random forest AI model outperformed a neural network in predicting burnout and intention to leave the field and that burnout inventory scores are the most predictive variables. “[Intention to leave the field] is something we can’t really afford to have at this day and time in our workforce, especially in the sonographer workforce,” Bagley said. Jennifer Bagley from the University of Oklahoma presents findings at UltraCon on the performance of two machine learning approaches in predicting burnout among sonographers.Amerigo AllegrettoBurnout continues to be a challenge for medical imaging as those who experience symptoms have an intention to leave the field. Previous reports indicate that sonographers have a burnout rate as high as 90%. So, how can imaging leaders know who is most at risk for burnout? Bagley and colleagues employed two types of models, a neural network and a random forest model. The researchers issued a 43-question survey to study participants with questions about the following: workplace culture, workload, intention to leave the field, work-related musculoskeletal disease disorders, and the Oldenburg Burnout Inventory. The latter is a validated invent...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Ultrasound Artificial Intelligence Radiologic Technologist Source Type: news