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Activity Number: 176 - Modeling
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #329822 Presentation
Title: Topic Models for Medical Prescription Fraud and Abuse Detection
Author(s): Tahir Ekin* and Babak Zafari
Companies: Texas State University and Babson College
Keywords: Bayesian; prescription; fraud detection; data mining; fraud analytics; Medicare Part D

Medical prescription fraud and abuse has been a pressing issue in the U.S. resulting in large financial losses and adverse effects on human health. The size and complexity of the healthcare systems as well as the cost of medical audits make use of statistical methods necessary to generate investigative leads in prescription audits. In this manuscript, using the real world Medicare Part D prescription data from New Hampshire, we take advantage of the hierarchical nature of the prescription data to analyze prescriber-drug associations. In particular, we propose the use of Bayesian topic models to group drugs with respect to the billing patterns and exhibit the potential aberrant behaviors. The prescription patterns of the providers are retrieved with an emphasis on opioids, and aggregated into distance based measures which are visualized by concentration functions. This output can enable medical auditors to identify leads for audits of providers prescribing medically unnecessary drugs.

Authors who are presenting talks have a * after their name.

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