Hossein Joudaki - Health Economics Group, Social Security Organization, Tehran, Iran
Arash Rashidian - Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Behrouz Minaei-Bidgoli - School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Mahmood Mahmoodi - Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Bijan Geraili - Department of Education Management, School of Psychology and Education, University of Tehran, Tehran, Iran
Mahdi Nasiri - School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Mohammad Arab - Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

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 ۵ 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 (۵۴%) were ‘suspects’ of conducting abusive behavior. The results also identified ۲% 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 (۹۸%) and abuse (۸۵%) 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 physicians.

Healthcare, Fraud, Abuse, Insurance, Data Mining, General Physician