TY - JOUR
T1 - Predicting Healthcare Fraud in Medicaid
T2 - A Multidimensional Data Model and Analysis Techniques for Fraud Detection
AU - Thornton, Dallas
AU - Mueller, Roland
AU - Schoutsen, Paulus
AU - van Hillegersberg, Jos
N1 - CENTERIS 2013 - Conference on ENTERprise Information Systems / ProjMAN 2013 - International Conference on Project MANagement/ HCIST 2013 - International Conference on Health and Social Care Information Systems and Technologies
PY - 2013
Y1 - 2013
N2 - 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 cases
AB - 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 cases
U2 - 10.1016/j.protcy.2013.12.140
DO - 10.1016/j.protcy.2013.12.140
M3 - Article
SN - 2212-0173
VL - 9
SP - 1252
EP - 1264
JO - Procedia technology
JF - Procedia technology
ER -