Understanding and Estimating Fraudulent Activities in Healthcare
In this paper, we propose a predictive modeling approach to develop methodologies for healthcare fraud management. Our methodologies are based on the following economic framework: fraud is a rational response by certain providers to misrepresent their effort on behalf of patients in order to reap monetary incentives when the probability of detection is low. Given this framework, we describe how methodologies can be used in combination with previously identified fraud predictors. We then use the Medicare 5% sample file in order to show how these methodologies can be applied with identified fraud predictors and discuss the design of empirical analysis using healthcare claims data. We also explore the implications of our study for healthcare practitioners and public policy makers in terms of reducing fraud in public programs. We conclude by commenting on the validation of fraud detection methodologies given the fact that fraud is rarely, if ever, observed and reported in a standardized manner.