Predicting Healthcare Fraud in Medicaid: A Multidimensional Data Model and Analysis Techniques for Fraud Detection

Abstract

It is estimated that approximately $700 billion is lost due to fraud, waste, and abuse in the US healthcare system. Medicaid has been particularly susceptible target for fraud in recent years, with a distributed management model, limited cross- program communications, and a difficult-to-track patient population of low-income adults, their children, and people with certain disabilities. For effective fraud detection, one has to look at the data beyond the transaction-level. This paper builds upon Sparrow's fraud type classifications and the Medicaid environment and to develop a Medicaid multidimensional schema and provide a set of multidimensional data models and analysis techniques that help to predict the likelihood of fraudulent activities. These data views address the most prevalent known fraud types and should prove useful in discovering the unknown unknowns. The model is evaluated by functionally testing against known fraud case