This study conducted by researchers at the Icahn School of Medicine and the University of Michigan assessed the impact of implementing predictive models on the subsequent performance of those and other models. The findings suggest that using the models to adjust how care is delivered can alter the baseline assumptions that the models were “trained” on, often for worse. The study simulated critical care scenarios at two major health care institutions, the Mount Sinai Health System in New York and Beth Israel Deaconess Medical Center in Boston, analyzing 130,000 critical care admissions. The researchers investigated three key scenarios: model retraining after initial use, model performance in a new environment, and model performance in a new population.
Related Posts
