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

Document Type : Original Article

Authors

1 Health Economics Group, Social Security Organization, Tehran, Iran

2 Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

3 School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

4 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

5 Department of Education Management, School of Psychology and Education, University of Tehran, Tehran, Iran

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.
 
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.

Keywords

Main Subjects

References

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History

  • Receive Date: 24 February 2015
  • Revise Date: 22 June 2015
  • Accept Date: 27 October 2015
  • First Publish Date: 01 March 2016

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