Graph Analysis for Detecting Fraud, Waste, and Abuse in Health‐Care Data

Abstract

Detection of fraud, waste, and abuse (FWA) is an important yet challenging problem. In this article, we describe a system to detect suspicious activities in large health‐care data sets. Each healthcare data set is viewed as a heterogeneous network consisting of millions of patients, hundreds of thousands of doctors, tens of thousands of pharmacies, and other entities. Graph‐analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous network structure. The visualization interface, known as the network explorer, provides a good overview of data and enables users to filter, select, and zoom into network details on demand. The system has been deployed on multiple sites and data sets, both government and commercial, and identified many overpayments with a potential value of several million dollars per month.