Abstract
People rely on government-managed health insurance systems, private health insurance systems, or both to share the expensive healthcare costs. With such an intensive need for health insurances, however, health care service providers' fraudulent and abusive behavior has become a serious problem. In this research, we propose a data-mining framework that utilizes the concept of clinical pathways to facilitate automatic and systematic construction of an adaptable and extensible detection model. The proposed approaches have been evaluated objectively by a real-world data set gathered from the National Health Insurance (NHI) program in Taiwan. The empirical experiments show that our detection model is efficient and capable of identifying some fraudulent and abusive cases that are not detected by a manually constructed detection model.
| Original language | English |
|---|---|
| Pages (from-to) | 56-68 |
| Number of pages | 13 |
| Journal | Expert Systems with Applications |
| Volume | 31 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2006 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence
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A process-mining framework for the detection of healthcare fraud and abuse. / Yang, Wan-Shiou; Hwang, San Yih.
In: Expert Systems with Applications, Vol. 31, No. 1, 01.01.2006, p. 56-68.Research output: Contribution to journal › Article
TY - JOUR
T1 - A process-mining framework for the detection of healthcare fraud and abuse
AU - Yang, Wan-Shiou
AU - Hwang, San Yih
PY - 2006/1/1
Y1 - 2006/1/1
N2 - People rely on government-managed health insurance systems, private health insurance systems, or both to share the expensive healthcare costs. With such an intensive need for health insurances, however, health care service providers' fraudulent and abusive behavior has become a serious problem. In this research, we propose a data-mining framework that utilizes the concept of clinical pathways to facilitate automatic and systematic construction of an adaptable and extensible detection model. The proposed approaches have been evaluated objectively by a real-world data set gathered from the National Health Insurance (NHI) program in Taiwan. The empirical experiments show that our detection model is efficient and capable of identifying some fraudulent and abusive cases that are not detected by a manually constructed detection model.
AB - People rely on government-managed health insurance systems, private health insurance systems, or both to share the expensive healthcare costs. With such an intensive need for health insurances, however, health care service providers' fraudulent and abusive behavior has become a serious problem. In this research, we propose a data-mining framework that utilizes the concept of clinical pathways to facilitate automatic and systematic construction of an adaptable and extensible detection model. The proposed approaches have been evaluated objectively by a real-world data set gathered from the National Health Insurance (NHI) program in Taiwan. The empirical experiments show that our detection model is efficient and capable of identifying some fraudulent and abusive cases that are not detected by a manually constructed detection model.
UR - http://www.scopus.com/inward/record.url?scp=33644780888&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33644780888&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2005.09.003
DO - 10.1016/j.eswa.2005.09.003
M3 - Article
AN - SCOPUS:33644780888
VL - 31
SP - 56
EP - 68
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 1
ER -