Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data

Juan Liu, Eric Bier, Aaron Wilson, John Alexis Guerra-Gomez, Tomonori Honda, Kumar Sricharan, Leilani Gilpin, Daniel Davies

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 healthcare datasets. Each healthcare dataset 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 datasets, both government and commercial, and identified many overpayments with a potential value of several million dollars per month.

Full Text:

PDF


DOI: https://doi.org/10.1609/aimag.v37i2.2630

Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.