Using data mining to detect health care fraud and abuse: a review of literature.

Inappropriate payments by insurance organizations or third party payers occur because of errors, abuse and fraud. The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient.

Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study.

Background: We aimed to identify the indicators of healthcare fraud and abuse in general physicians' drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse.

Methods: We applied data mining approach to a major health insurance organization dataset of private sector general physicians' prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach.

The impact of rural health system reform on hospitalization rates in the Islamic Republic of Iran: an interrupted time series.

Objective: To assess the effects on hospital utilization rates of a major health system reform - a family physician programme and a social protection scheme - undertaken in rural areas of the Islamic Republic of Iran in 2005.

Methods: A "tracer" province that was not a patient referral hub was selected for the collection of monthly hospitalization data over a period of about 10 years, beginning two years before the rural health system reform (the "intervention") began. An interrupted time series analysis was conducted and segmented regression analysis was used to assess the immediate and gradual effects of the intervention on hospitalization rates in an intervention group composed of rural residents and a comparison group composed of urban residents primarily.