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

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.

[1]  M K Sparrow.  Health care fraud control: understanding the challenge. , 1996, Journal of insurance medicine.

[2]  N. Maleki-Dizaji,et al.  Can rational prescribing be improved by an outcome‐based educational approach? A randomized trial completed in Iran , 2010, The Journal of continuing education in the health professions.

[3]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[4]  John F. Roddick,et al.  Database Issues in Knowledge Discovery and Data Mining , 1999, Australas. J. Inf. Syst..

[5]  I. Durand-zaleski,et al.  Effects of financial incentives on medical practice: results from a systematic review of the literature and methodological issues. , 2000, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[6]  W. van Lerberghe,et al.  The weakest link: competence and prestige as constraints to referral by isolated nurses in rural Niger , 2004, Human resources for health.

[7]  Hyunjung Shin,et al.  A scoring model to detect abusive billing patterns in health insurance claims , 2012, Expert Syst. Appl..

[8]  Jionghua Jin,et al.  A survey on statistical methods for health care fraud detection , 2008, Health care management science.

[9]  B. Minaei-Bidgoli,et al.  Using Data Mining to Detect Health Care Fraud and Abuse: A Review of Literature , 2014, Global journal of health science.

[10]  A. Ali,et al.  EVALUATING THE CASES OF VIOLATING THE REGULATIONS OF MEDICAL SERVICES INSURANCE ORGANIZATION IN ISFAHAN PROVINCE, IRAN , 2012 .

[11]  Maurice M. Tatsuoka,et al.  9: Multivariate Analysis in Educational Research , 1973 .

[12]  H. Joudaki,et al.  ASSESSING MEDICAL MISCONDUCT AND COMPLAINTS IN IRANIAN HEALTH SYSTEM: A SYSTEMATIC REVIEW OF THE LITERATURE , 2010 .

[13]  David J. Hand,et al.  Statistical fraud detection: A review , 2002 .

[14]  Yin Shan,et al.  Mining Medical Specialist Billing Patterns for Health Service Management , 2008, AusDM.

[15]  San-Yih Hwang,et al.  A process-mining framework for the detection of healthcare fraud and abuse , 2006, Expert Syst. Appl..

[16]  Ihsan Sabuncuoglu,et al.  A prescription fraud detection model , 2012, Comput. Methods Programs Biomed..

[17]  Ying-Chan Tang,et al.  Detecting hospital fraud and claim abuse through diabetic outpatient services , 2008, Health care management science.

[18]  Yin Shan,et al.  Discovering inappropriate billings with local density based outlier detection method , 2009, AusDM.

[19]  H. Rezazadeh,et al.  Effect of interactive group discussion among physicians to promote rational prescribing. , 2009, Eastern Mediterranean health journal = La revue de sante de la Mediterranee orientale = al-Majallah al-sihhiyah li-sharq al-mutawassit.

[20]  P E Kalb,et al.  Health care fraud and abuse. , 1999, JAMA.

[21]  Graham J. Williams,et al.  Mining the Knowledge Mine: The Hot Spots Methodology for Mining Large Real World Databases , 1997, Australian Joint Conference on Artificial Intelligence.

[22]  Rassoul Dinarvand,et al.  CHALLENGES AND ACHIEVEMENTS OF PROMOTING RATIONAL USE OF DRUGS IN IRAN , 2009 .

[23]  Pedro A. Ortega,et al.  A Medical Claim Fraud/Abuse Detection System based on Data Mining: A Case Study in Chile , 2006, DMIN.

[24]  T. Vian,et al.  No Evidence of the Effect of the Interventions to Combat Health Care Fraud and Abuse: A Systematic Review of Literature , 2012, PloS one.

[25]  Jim Gee,et al.  The Financial Cost of Healthcare Fraud , 2012 .

[26]  Chinho Lin,et al.  Intelligent physician segmentation and management based on KDD approach , 2008, Expert Syst. Appl..

[27]  P. Ferrinho,et al.  Dual practice in the health sector: review of the evidence. , 2004, Human resources for health.