DocumentCode :
2507024
Title :
Using support vector machines to detect medical fraud and abuse
Author :
Francis, Charles ; Pepper, Noah ; Strong, Homer
Author_Institution :
Qmedtrix, Portland, OR, USA
fYear :
2011
fDate :
Aug. 30 2011-Sept. 3 2011
Firstpage :
8291
Lastpage :
8294
Abstract :
This paper examines the architecture and efficacy of Quash, an automated medical bill processing system capable of bill routing and abuse detection. Quash is designed to be used in conjunction with human auditors and a standard bill review software platform to provide a complete cost containment solution for medical claims. The primary contribution of Quash is to provide a real world speed up for medical fraud detection experts in their work. There will be a discussion of implementation details and preliminary experimental results. In this paper we are entirely focused on medical data and billing patterns that occur within the United States, though these results should be applicable to any financial transaction environment in which structured coding data can be mined.
Keywords :
fraud; medical administrative data processing; support vector machines; Quash; automated medical bill processing system; bill routing detection; medical abuse detection; medical fraud detection; support vector machines; Accuracy; Encoding; Hospitals; Humans; Machine learning; Medical diagnostic imaging; Training; Artificial Intelligence; Fraud; Health Services Misuse; Humans; Insurance Claim Reporting; Support Vector Machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Conference_Location :
Boston, MA
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4121-1
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2011.6092044
Filename :
6092044
Link To Document :
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