Detecting Healthcare Fraud through Patient Sharing Schemes

  • Aryya Gangopadhyay
  • Song Chen
  • Yelena Yesha
Part of the Communications in Computer and Information Science book series (CCIS, volume 285)

Abstract

The United States loses at least $60 billion in health-care fraud every year, and some estimates put the cost as high as 10% of the nation’s total health-care spending, which exceeds $2 trillion. The federal government is putting tremendous efforts in combating health frauds and safeguard the two largest government sponsored programs: Medicare and Medicaid. Using data analysis techniques to discover and prevent health care frauds is an important focus in all of the efforts. In this paper, we propose a new method for identifying patient sharing schemes that are prevalent in many parts of this country. Our proposed method is based on the PageRank algorithm that has been used by Google’s Web search engine. We describe our approach, discuss the similarities and differences with PageRank, and demonstrate the applicability of this method by applying it to datasets simulated from real-life scenarios.

Keywords

Medicare Part False Claim PageRank Algorithm Dangling Node Medicare Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Berry, M.W., Browne, M.: Understanding Search Engines: Mathematical Modeling and text retrieval. SIAM, Philadelphia (2005)CrossRefMATHGoogle Scholar
  2. 2.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Seventh International World-Wide Web Conference, WWW 1998 (1998)Google Scholar
  3. 3.
    Moler, C.B.: Cleve’s corner: The world’s largest matrix computation: Google’s pagerank is an eigenvector of a matrix of order 2.7 billion. Technical note (October 2002)Google Scholar
  4. 4.
    Mostafa, J.: Seeking better web searches. Scientific American 292, 66–73 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Aryya Gangopadhyay
    • 1
  • Song Chen
    • 1
  • Yelena Yesha
    • 1
  1. 1.University of Maryland Baltimore County (UMBC)BaltimoreUSA

Personalised recommendations