title: Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Health Economics Group, Social Security Organization, Tehran, Iran.
authors.forename: Hossein
authors.initials: H
authors.lastname: Joudaki
authors.affiliation: Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Arash
authors.initials: A
authors.lastname: Rashidian
authors.affiliation: School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.
authors.forename: Behrouz
authors.initials: B
authors.lastname: Minaei-Bidgoli
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Mahmood
authors.initials: M
authors.lastname: Mahmoodi
authors.affiliation: Department of Education Management, School of Psychology and Education, University of Tehran, Tehran, Iran.
authors.forename: Bijan
authors.initials: B
authors.lastname: Geraili
authors.affiliation: School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.
authors.forename: Mahdi
authors.initials: M
authors.lastname: Nasiri
authors.affiliation: Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Arab
mesh_terms: D057225:Data Mining / Q000379:methods*; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D005260:Female; D005607:Fraud / Q000706:statistics & numerical data*; D058005:General Practitioners / Q000706:statistics & numerical data*; D006801:Humans; D007348:Insurance, Health / Q000706:statistics & numerical data*; D007492:Iran / Q000453:epidemiology; D008297:Male; D011331:Private Practice; D028743:Professional Misconduct / Q000706:statistics & numerical data*
keywords: Abuse; Data Mining; Fraud; General Physician; Healthcare; Insurance