Graph analytics for healthcare fraud risk estimation

This paper presents a novel approach to estimating healthcare fraud (HCF) risk that applies network algorithms to graphs derived from open source datasets. One group of algorithms calculates behavioral similarity to known fraudulent and non-fraudulent healthcare providers with respect to measurable healthcare activities, such as medical procedures and drug prescriptions. Another set of algorithms estimates propagation of risk from fraudulent healthcare providers through geospatial collocation, i.e., shared practice locations or other addresses. The algorithms were evaluated with respect to their ability to predict a provider's presence on the Office of the Inspector General's list of providers excluded from participation in Medicare and other Federal healthcare programs ( exclusion ). In an empirical evaluation, a combination of 11 features achieved an f-score of 0.919 and a ROC area of 0.960 in exclusion prediction. An ablation analysis showed that most of this predictive accuracy was the result of features that measure risk propagation through geospatial collocation.