%0 Journal Article
%T How to Extract and Explore Big Data for Fraud Detection in the Healthcare Sector: The EOPYY Case Study.
%A Georgakopoulos S
%A Gallos P
%A Plagianakos V
%J Stud Health Technol Inform
%V 270
%N 0
%D Jun 2020 16
%M 32570632
暂无%R 10.3233/SHTI200415
%X Big Data technologies can contribute to medical fraud detection. The aim of this paper is to present the methodological approach of the Hellenic National Organization for the Provision of Health Services (EOPYY) in data analysis to detect financial or medical fraud. To analyze the data for fraud detection, a selection of prescription data from the year 2018 were examined. The Local Correlation Integral algorithm was applied to detect any outliers on the dataset. The results revealed that 7 out of 879 cases could be characterized as outliers. These outliers must be further investigated to determine if they have been associated with fraud. According to the results of this study, this outliers' detection approach can support and help the fraud detection process conducted by the auditing services in Healthcare sector.