A medical price prediction system using hierarchical decision trees

The health care costs constitute a significant fraction of the U.S. economy - nearly 20% of the Gross Domestic Product (GDP) is spent on health care. The health spending in the US is the highest among all developed nations in absolute numbers as well as a percentage of the economy. The U.S. government bears a large portion of seniors' health expenditure through its Medicare program. The growing health related expenses combined with the fact that the baby-boomer generation is retiring, and hence they will be eligible for Medicare, puts a great burden on the U.S. exchequer. Therefore, it is essential to contain health related payments through all means possible. In this paper, we develop a medical price prediction system using Medicare payment datasets. Such a system will aid in steering patients to cost effective providers and thereby curb health spending. The policymakers can also use the tool to better understand which providers are relatively expensive and take punitive actions if necessary. We develop a novel hierarchical decision tree approach to predict prices. We conduct extensive experiments to compare various machine learning algorithms, including hierarchical decision trees, RandomForests, linear regression, and Gradient-boosted trees. The experiments indicate that the price prediction system achieves high accuracy.