Using data mining to find fraud in HCFA health care claims.
Data mining can be/used to detect health care fraud and abuse through visualization of very large data sets to isolate new and unusual patterns of activity. Data mining has allowed better direction and use of health care fraud detection and investigative resources by recognizing and quantifying the underlying indicators of fraudulent claims, fraudulent providers, and fraudulent beneficiaries. A large amount of work must be performed prior to the actual data mining. These precursory tasks include: customer discussions, data extraction and cleaning, transformation of the database, and auditing (basic statistics and visualization of the information) of the data. This paper describes the tasks performed in support of a project for HCFA (Health Care Financing Administration).
Sokol
L
L
Veridian Corporation, Fairfax, Virginia, USA.
Garcia
B
B
Rodriguez
J
J
West
M
M
Johnson
K
K
D014487:Centers for Medicare and Medicaid Services, U.S.; D003625:Data Collection; D016208:Databases, Factual / Q000706:statistics & numerical data*; D005260:Female; D005607:Fraud / Q000331:legislation & jurisprudence*; D006801:Humans; D016247:Information Storage and Retrieval; D007344:Insurance Claim Reporting / Q000145:classification / Q000331:legislation & jurisprudence*; D008327:Mammography / Q000145:classification* / Q000191:economics / Q000706:statistics & numerical data; D008485:Medical Audit; D014481:United States