The Application of Data Mining to Evaluate the Cost-Effectiveness of Alternative Treatment Modalities in a National Medicare Database

Abstract

This paper reports on a case study applying data mining techniques to large Medicare claims databases to discover insights related to the comparative cost effectiveness of two alternative treatment modalities for treating brain tumors in the elderly. The authors evaluate the cost effectiveness of open surgery, radiosurgery, or a combination of the two. The study applies data mining algorithms including data preparation, classification, association, and predictive modeling techniques to reveal insights into the costs of alternative health care practice patterns. The results suggest radiosurgery appears to be less costly compared to surgical resection in the Medicare-eligible population. The authors also identified the fact that African Americans comprised a smaller percentage of patients receiving radiosurgery. The study demonstrates that data mining methods can be applied to large complex Medicare claims files to identify and extract undiscovered knowledge to guide medical decision making and public policy.