A prescription fraud detection model
Author
Aral, K. D.
Güvenir H. A.
Sabuncuoğlu, T.
Akar, A. R.
Date
2012Source 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
ArticleItem Usage Stats
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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.