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      A prescription fraud detection model

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      Author
      Aral, K. D.
      Güvenir H. A.
      Sabuncuoğlu, T.
      Akar, A. R.
      Date
      2012
      Source Title
      Computer Methods and Programs in Biomedicine
      Print ISSN
      0169-2607
      Publisher
      Elsevier Ireland Ltd.
      Volume
      106
      Issue
      1
      Pages
      37 - 46
      Language
      English
      Type
      Article
      Item Usage Stats
      147
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      555
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      Abstract
      Prescription fraud is a main problem that causes substantial monetary loss in health care systems. We aimed to develop a model for detecting cases of prescription fraud and test it on real world data from a large multi-center medical prescription database. Conventionally, prescription fraud detection is conducted on random samples by human experts. However, the samples might be misleading and manual detection is costly. We propose a novel distance based on data-mining approach for assessing the fraudulent risk of prescriptions regarding cross-features. Final tests have been conducted on adult cardiac surgery database. The results obtained from experiments reveal that the proposed model works considerably well with a true positive rate of 77.4% and a false positive rate of 6% for the fraudulent medical prescriptions. The proposed model has the potential advantages including on-line risk prediction for prescription fraud, off-line analysis of high-risk prescriptions by human experts, and self-learning ability by regular updates of the integrative data sets. We conclude that incorporating such a system in health authorities, social security agencies and insurance companies would improve efficiency of internal review to ensure compliance with the law, and radically decrease human-expert auditing costs. © 2011 Elsevier Ireland Ltd.
      Keywords
      Data mining
      Health care fraud
      Outlier detection
      Prescription fraud
      Computer interface
      Permalink
      http://hdl.handle.net/11693/21531
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.cmpb.2011.09.003
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      • Department of Industrial Engineering 677
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