A survey on the state of healthcare upcoding fraud analysis and detection

• Published in: Health services and outcomes research methodology 2017-03, Vol.17 (1), p.31-55
• Main Authors: Bauder, Richard, Khoshgoftaar, Taghi M., Seliya, Naeem
• Format: Article
• Language: English
• Subjects: Baby boomers; Big Data; Cost recovery; Criminal investigations; Data analysis; Data mining; Economic models; Economic statistics; Economic theory; Economics; Education; Electronic health records; Fraud; Fraud prevention; Group insurance; Health Administration; Health care expenditures; Health care policy; Health economics; Health insurance; Insurance claims; Libraries; Medicaid; Medical coding; Medical referrals; Medicare fraud; Medicine; Medicine & Public Health; Methodology of the Social Sciences; Older people; Prospective payment systems; Public Health; Reimbursement; Statistics; Studies; Trends; Websites
• ISSN: 1387-3741; 1572-9400
• DOI: 10.1007/s10742-016-0154-8

From its infancy in the 1910s, healthcare group insurance continues to increase, creating a consistently rising burden on the government and taxpayers. The growing number of people enrolled in healthcare programs such as Medicare, along with the enormous volume of money in the healthcare industry, increases the appeal for and risk of fraudulent activities. One such fraud, known as upcoding, is a means by which a provider can obtain additional reimbursement by coding a certain provided service as a more expensive service than what was actually performed. With the proliferation of data mining techniques and the recent and continued availability of public healthcare data, the application of these techniques towards fraud detection, using this increasing cache of data, has the potential to greatly reduce healthcare costs through a more robust detection of upcoding fraud. Presently, there is a sizable body of healthcare fraud detection research available but upcoding fraud studies are limited. Audit data can be difficult to obtain, limiting the usefulness of supervised learning; therefore, other data mining techniques, such as unsupervised learning, must be explored using mostly unlabeled records in order to detect upcoding fraud. This paper is specific to reviewing upcoding fraud analysis and detection research providing an overview of healthcare, upcoding, and a review of the current data mining techniques used therein.