title: A prescription fraud detection model.
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.
authors.affiliation: INSEAD, Technology & Operations Management Area, Fontainebleau, France.
authors.forename: Karca Duru
authors.initials: KD
authors.lastname: Aral
authors.forename: Halil Altay
authors.initials: HA
authors.lastname: Güvenir
authors.forename: Ihsan
authors.initials: I
authors.lastname: Sabuncuoğlu
authors.forename: Ahmet Ruchan
authors.initials: AR
authors.lastname: Akar
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D057225:Data Mining; D016208:Databases, Factual; D005607:Fraud / Q000191:economics / Q000331:legislation & jurisprudence / Q000706:statistics & numerical data*; D006801:Humans; D055553:Prescription Drugs; D012306:Risk; D012985:Software Design; D014421:Turkey
chemical_list: D055553:Prescription Drugs