Predicting 30 Day Risk and Cost of "All Cause" Hospital Readmissions
The hospital readmission rate of patients within 30 days after discharge is broadly accepted as a healthcare quality measure and cost driver in the United States. The ability to estimate hospitalization costs alongside 30 day risk stratification for such readmissions provides additional benefit for accountable care, now a global issue and foundation for the U.S.~government mandate under the Affordable Care Act. Recent data mining efforts either predict healthcare costs or risk of hospital readmission, but not both. In this paper we present a dual predictive modeling effort that utilizes healthcare data to predict the risk and cost of any hospital readmission `all cause' For this purpose, we explore machine learning algorithms to do accurate predictions of healthcare costs and risk of 30 day readmission.Results on risk prediction for `all cause' readmission compared to the standardized readmission tool (LACE) are promising, and the proposed techniques for cost prediction consistently outperform baseline models and demonstrate substantially lower mean absolute error (MAE)
AAAI Workshop: Expanding the Boundaries of Health Informatics Using AI