標題: | Detecting hospital fraud and claim abuse through diabetic outpatient services |
作者: | Liou, Fen-May Tang, Ying-Chan Chen, Jean-Yi 經營管理研究所 Institute of Business and Management |
關鍵字: | Medical insurance fraud;National health insurance;Diabetes mellitus;Data mining;Logistic regression;Neural networks;Classification trees |
公開日期: | 1-十二月-2008 |
摘要: | Hospitals and health care providers tend to get involved in exaggerated and fraudulent medical claims initiated by national insurance schemes. The present study applies data mining techniques to detect fraudulent or abusive reporting by healthcare providers using their invoices for diabetic outpatient services. This research is pursued in the context of Taiwan's National Health Insurance system. We compare the identification accuracy of three algorithms: logistic regression, neural network, and classification trees. While all three are quite accurate, the classification tree model performs the best with an overall correct identification rate of 99%. It is followed by the neural network (96%) and the logistic regression model (92%). |
URI: | http://dx.doi.org/10.1007/s10729-008-9054-y http://hdl.handle.net/11536/14213 |
ISSN: | 1386-9620 |
DOI: | 10.1007/s10729-008-9054-y |
期刊: | HEALTH CARE MANAGEMENT SCIENCE |
Volume: | 11 |
Issue: | 4 |
起始頁: | 353 |
結束頁: | 358 |
顯示於類別: | 期刊論文 |