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

Document Type : Original Article


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


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.
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.
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.
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.


Main Subjects

  1. Health Financing. World Health Organization website. Accessed October 13, 2013.
  2. Gee J, Button M, Brooks G, Vincke P. The financial cost of healthcare fraud. Portsmouth: University of Portsmouth, MacIntyre Hudson, Milton Keynes. Accessed December 20, 2010.
  3. What is Fraud and Abuse? Department of Finance and Administration website. Accessed October 13, 2013.
  4. Torras H. Health care fraud and abuse: a physician’s guide to compliance. American Medical Association Press; 2006.
  5. Rashidian A, Joudaki H, Vian T. No evidence of the effect of the interventions to combat health care fraud and abuse: a systematic review of literature. PLoS One. 2012;7(8):e41988. doi:10.1371/journal.pone.0041988
  6. Li J, Huang KY, Jin J, Shi J. A survey on statistical methods for health care fraud detection. Health Care Manage Sci. 2008;11:275-287. doi:10.1007/s10729-007-9045-4
  7. Aral KD, Güvenir HA, Sabuncuoğlu İ, Akar AR. A prescription fraud detection model. Comput Methods Programs Biomed. 2012;106:37-46. doi:10.1016/j.cmpb.2011.09.003
  8. Ortega PA, Figueroa CJ, Ruz GA. A medical claim fraud/abuse detection system based on data mining: a case study in chile. Paper presented at: International Conference on Data Mining; 2006, Las Vegas, Nevada. Accessed October 13, 2013.
  9. Chaix-Couturier C, Durand-Zaleski I, Jolly D, Durieux P. Effects of financial incentives on medical practice: results from a systematic review of the literature and methodological issues. Int J Qual Health Care. 2000;12(2):133-142. doi:10.1093/intqhc/12.2.133
  10. Kalb PE. Health care fraud and abuse. JAMA. 1999;282(12):1163-1168. doi:10.1001/jama.282.12.1163
  11. Maimon OZ, Rokach L, eds. Data mining and knowledge discovery handbook. New York: Springer; 2005. ‏ doi:10.1007/978-0-387-09823-4
  12. Joudaki H, Rashidian A, Minaei-Bidgoli B, et al. Using data mining to detect health care fraud and abuse: a review of literature. Glob J Health Sci. 2014;7(1):194-202. doi:10.5539/gjhs.v7n1p194
  13. Bolton RJ, Hand DJ. Statistical fraud detection: a review. Stat Sci. 2002;17(3):235-249. doi:10.1214/ss/1042727940
  14. Liou FM, Tang YC, Chen JY. Detecting hospital fraud and claim abuse through diabetic outpatient services. Health Care Manage Sci. 2008;1:353-358. doi:10.1007/s10729-008-9054-y
  15. Yang WS, Hwang SY. A process-mining framework for the detection of healthcare fraud and abuse. Expert Syst Appl. 2006;31:56-68. doi:10.1016/j.eswa.2005.09.003
  16. Lin C, Lin CM, Li ST, Kuo SC. Intelligent physician segmentation and management based on KDD approach. Expert Syst Appl. 2008;34:1963-1973. doi:10.1016/j.eswa.2007.02.038
  17. Shin H, Park H, Lee J, Jhee WC. A scoring model to detect abusive billing patterns in health insurance claims. Expert Syst Appl. 2012;39:7441-7450. doi:10.1016/j.eswa.2012.01.105
  18. Shan Y, Murray DW, Sutinen A. Discovering inappropriate billings with local density based outlier detection method. Paper presented at: The Eighth Australasian Data Mining Conference; 2009; Melbourne, Australia. Accessed October 13, 2013.
  19. Shan Y, Jeacocke D, Murray DW, Sutinen A. Mining medical specialist billing patterns for health service management. Paper presented at: The 7th Conferences in Research and Practice in Information Technology; 2008; Australia. Accessed October 13, 2013.
  20. Sparrow MK. Health care fraud control understanding the challenge. J Insur Med. 1996;28(2):86-96.
  21. Williams G, Huang Z. Mining the knowledge mine: The Hot Spots methodology for mining large real world databases. Lect Notes Comput Sci. 1997;1342:340-348.
  22. Rashidian A, Joudaki H. Assessing medical misconduct and complaints in Iran health system: a systematic review of literature. Sci J Forensic Med. 2010;15(4):234-243. [In Persian].
  23. Rashidian A, Khosravi A, Khabiri R, et al. Islamic Republic of Iran's Multiple Indicator Demographic and Health Survey (IrMIDHS) 2010. Tehran: Ministry of Health and Medical Education, 2012. [In Persian].
  24. Social Security Organization (SSO). Annual Report for 2012. The Bureau of Statistics and Socio-economic Measurement, Social Security Organization; 2013. [In Persian].
  25. Tatsuoka MM. Multivariate analysis in educational research. Rev Res Educ. 1973;1:273-319.
  26. Soleymani F, Valadkhani M, Dinarvand R. Challenges and achievements of promoting rational use of drug in Iran. Iran J Public Health. 2009;38(suppl 1):166-168.
  27. Esmaily HM, Silver I, Shiva S, et al. Can rational prescribing be improved by an outcome-based educational approach? A randomized trial completed in Iran. J Contin Educ Health Prof. 2010;30(1):11-18. doi:10.1002/chp.20051
  28. Garjani A, Salimnejad M, Shamsmohamadi M, et al. Effect of interactive group discussion among physicians to promote rational prescribing. East Mediterr Health J. 2009;15(2):408-415.
  29. Ghodoosi A, Abedi HA, Mansouri A, Riaziat A. Evaluating the cases of violating the regulations of medical services insurance organization in Isfahan province, Iran. Health Inf Manag. 2012;9(3):339-347. [In Persian].
  30. Rainsford C, Roddick J. Database issues in knowledge discovery and data mining. Australasian J Info Syst. 1999;6(2):101-128.
  31. Ferrinho P, Van Lerberghe W, Fronteira I, Hipólito F, Biscaia A. Dual practice in the health sector: review of the evidence. Hum Resour Health. 2004;2(1):14. doi:10.1186/1478-4491-2-1