Hossein Joudaki1, Arash Rashidian2, Behrouz Minaei-Bidgoli3, Mahmood Mahmoodi4, Bijan Geraili5, Mahdi Nasiri3, Mohammad Arab2. 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.
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.
Authors: A Garjani; M Salimnejad; M Shamsmohamadi; V Baghchevan; R G Vahidi; N Maleki-Dijazi; H Rezazadeh Journal: East Mediterr Health J Date: 2009 Mar-Apr Impact factor: 1.628
Authors: Melissa M Garrido; David K Jones; Alexander Woodruff; Kiersten Strombotne; Sivagaminathan Palani; Sarah Zahakos; Michael Adelberg; Steven D Pizer; Austin B Frakt Journal: Health Serv Res Date: 2022-08-19 Impact factor: 3.734
Authors: Kevin B Johnson; Wei-Qi Wei; Dilhan Weeraratne; Mark E Frisse; Karl Misulis; Kyu Rhee; Juan Zhao; Jane L Snowdon Journal: Clin Transl Sci Date: 2020-10-12 Impact factor: 4.689