Info-Abstract

This set of codes is used for the manuscript “An Unsupervised Bayesian Hierarchical Method for Medical Fraud Assessment” 
that is under review in “Statistical Analysis and Data Mining”. Please feel free to reach Greg Lakomski(gl1082@txstate.edu)
or Tahir Ekin (t_e18@txstate.edu, www.tahirekin.com) for more information.

Abstract

The increasing size and complexity of healthcare industry makes it attractive for fraudsters, therefore medical 
fraud assessment has gained more importance. Statistical methods can help medical auditors reveal fraud patterns 
within medical claims data. This paper proposes an unsupervised Bayesian hierarchical method as a pre-screening 
tool to aid in medical fraud assessment. The proposed hierarchical model helps the investigator group medical 
procedures and identify the hidden patterns among providers and medical procedures. Outlier detection and similarity 
assessment are conducted to analyze the billing differences among providers. We illustrate the utilization of the 
proposed method using U.S. Medicare Part B data and discuss the potential insights for medical audit decision making.