Open AccessGraph Analysis for Detecting Fraud, Waste, and Abuse in Health‐Care Data
Author(s) -
Liu Juan,
Bier Eric,
Wilson Aaron,
GuerraGomez John Alexis,
Honda Tomonori,
Sricharan Kumar,
Gilpin Leilani,
Davies Daniel
Publication year - 2016
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v37i2.2630
Subject(s) - geospatial analysis , computer science , zoom , visualization , graph , data science , data set , data mining , health care , set (abstract data type) , cartography , engineering , artificial intelligence , geography , theoretical computer science , economic growth , petroleum engineering , economics , lens (geology) , programming language
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.