Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data
Abstract
Detection of fraud, waste, and abuse (FWA) is an important yet difficult problem. In this paper, we describe a system to detect suspicious activities in large healthcare claims datasets. Each healthcare dataset is viewed as a heterogeneous network of patients, doctors, pharmacies, and other entities. These networks can be large, with millions of patients, hundreds of thousands of doctors, and tens of thousands of pharmacies, for example. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous networks within the overall graph structure. The system has been deployed on multiple sites and data sets, both government and commercial, to facilitate the work of FWA investigation analysts.
Cite
Text
Liu et al. "Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I2.19047Markdown
[Liu et al. "Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/liu2015aaai-graph/) doi:10.1609/AAAI.V29I2.19047BibTeX
@inproceedings{liu2015aaai-graph,
title = {{Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data}},
author = {Liu, Juan and Bier, Eric and Wilson, Aaron and Honda, Tomonori and Sricharan, Kumar and Gilpin, Leilani and Gómez, John Alexis Guerra and Davies, Daniel},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2015},
pages = {3912-3919},
doi = {10.1609/AAAI.V29I2.19047},
url = {https://mlanthology.org/aaai/2015/liu2015aaai-graph/}
}