||With the intensive need for health insurances, health care service providers’ fraud and abuse have become a serious problem. The practices, such as billing services that were never rendered, performing medically unnecessary services, and misrepresenting non-covered treatments as medically necessary covered treatments, etc, not only contribute to the problem of rising health care expenditure but also affect the health of patients. We are therefore motivated to investigate the detection of service providers’ fraudulent and abusive behavior. |
In this research, we introduce the concept of clinical pathways and thereby propose a framework that facilitates automatic and systematic construction of adaptable and extensible detection systems. For the purposes of building such detection systems, we study the problems of mining frequent patterns from clinical instances, selecting features that have more discriminating power and revising detection model to have higher accuracy with less labeled instances.
The performance of the proposed approaches has been evaluated objectively by synthetic data set and real-world data set. Using the real-world data set gathered from the National Health Insurance (NHI) program in Taiwan, the experiments show that our detection model has fairly good prediction power. Comparing to traditional expense driven approach, more importantly, our detection model tends to capture different fraudulent scenarios.