A Machine Learning Approach to Management of Heart Failure Populations

Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies. This study sought to generate a strategy for managing populations of patients with heart failure by leveraging large clinical datasets and machine learning. Geisinger electronic health record data were used to train machine learning models to predict 1-year all-cause mortality in 26,971 patients with heart failure who underwent 276,819 clinical episodes. There were 26 clinical variables (demographics, laboratory test results, medications), 90 diagnostic codes, 41 electrocardiogram measurements and patterns, 44 echocardiographic measurements, and 8 evidence-based “care gaps”: flu vaccine, blood pressure of