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Identifying Healthcare Fraud with Open Data

Xuan Zhang, Virginia Tech
Long Xia, Virginia Tech
Brandon Fan, Blacksburg High School
Alan Wang, Virginia Tech
Min Zhang, Tianjin University
Weiguo Fan, University of Iowa

Description

Health care fraud is a serious problem that impacts every patient and consumer. This fraudulent behavior causes excessive financial losses every year and causes significant patient harm. Healthcare fraud includes health insurance fraud, fraudulent billing of insurers for services not provided, and exaggeration of medical services, etc. To identify healthcare fraud thus becomes an urgent task to avoid the abuse and waste of public funds. Existing methods in this research field usually use classified data from governments, which greatly compromises the generalizability and scope of application. This paper introduces a methodology to use publicly available data sources to identify potentially fraudulent behavior among physicians. The research involved data pairing of multiple datasets, selection of useful features, comparisons of classification models, and analysis of useful predictors. Our performance evaluation results clearly demonstrate the efficacy of the proposed method.

 
Aug 16th, 12:00 AM

Identifying Healthcare Fraud with Open Data

Health care fraud is a serious problem that impacts every patient and consumer. This fraudulent behavior causes excessive financial losses every year and causes significant patient harm. Healthcare fraud includes health insurance fraud, fraudulent billing of insurers for services not provided, and exaggeration of medical services, etc. To identify healthcare fraud thus becomes an urgent task to avoid the abuse and waste of public funds. Existing methods in this research field usually use classified data from governments, which greatly compromises the generalizability and scope of application. This paper introduces a methodology to use publicly available data sources to identify potentially fraudulent behavior among physicians. The research involved data pairing of multiple datasets, selection of useful features, comparisons of classification models, and analysis of useful predictors. Our performance evaluation results clearly demonstrate the efficacy of the proposed method.