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.
Results: Thirteen indicators were developed in total. Over half of the general physicians (54%) were ‘suspects’ of
conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis
suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect
of perpetrating fraud (98%) and abuse (85%) in a new sample of data.
Conclusion: Our data mining approach will help health insurance organizations in low-and middle-income
countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing
of all physician