A prescription fraud detection model

• Published in: Computer methods and programs in biomedicine Vol. 106; no. 1; pp. 37 - 46
• Main Authors: Aral, Karca Duru, Güvenir, Halil Altay, Sabuncuoğlu, İhsan, Akar, Ahmet Ruchan
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01.04.2012; Elsevier
• Subjects: Adult; Algorithms; Artificial Intelligence; Biological and medical sciences; Computer Simulation; Data Mining; Databases, Factual; Fraud - economics; Fraud - legislation & jurisprudence; Fraud - statistics & numerical data; Health care fraud; Humans; Internal Medicine; Medical sciences; Other; Outlier detection; Prescription Drugs; Prescription fraud; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Risk; Software Design; Technology. Biomaterials. Equipments. Material. Instrumentation; Turkey; Turkey; Outlier detection; Health care fraud; Data mining; Prescription fraud; Biomedical engineering
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2011.09.003

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.