Predicting High Need End-of-Life in Medicare Patients through Machine Learning

Abstract

Patients are often at risk of consuming significant medical resources during the last 6 to 12 months of life, without improving their life expectancy. We develop a model for predicting patients likely to die within the next 6 to 12 months based on administrative claims and demographic data. Standard statistical models as well as newer machine learning approaches are used to identify target patients. Patients identified through the model are candidates for palliative care of hospice care. Timely intervention with appropriate care has been shown to both improve the quality of life of such patients while also reducing resource consumption. We demonstrate the use of the model by incorporating in an economic model of an intervention program, showing that intervention in the highest predicted probability cohort can provide a positive return on investment, provided the program is targeted at the highest-risk patients.