A prescription fraud detection model

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Aral K. D., Guvenir H. A., Sabuncuoglu I., Akar A. R.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.106, no.1, pp.37-46, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 106 Issue: 1
  • Publication Date: 2012
  • Doi Number: 10.1016/j.cmpb.2011.09.003
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.37-46
  • Abdullah Gül University Affiliated: Yes


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. (C) 2011 Elsevier Ireland Ltd. All rights reserved.