Abuse detection in healthcare insurance with disease-treatment network embedding.

Abuse in healthcare insurance refers to a medical service or practice inconsistent with the generally accepted sound fiscal practices, such as overtreatment or overcharging. These types of abuses may lead to prescriptions that do not meet the criteria for medical stability. On the other hand, abuse may incur unnecessary costs by deliberately executing gratuitous treatments. In efforts to detect and prevent abuse, insurance companies hire medical professionals to manually examine the legitimacy of claim filings. It is, however, very costly in terms of labor and time to review all of the claims given the exploding amount of filings. In this light, there are growing interests for employing data mining techniques to automatically detect abusive claims or providers showing an abnormal billing pattern. Unfortunately, most of these models do not consider the disease-treatment information explicitly. In order for detection models to properly address the issues rising from individual drugs with similar efficacy, it is absolutely essential to account for the relationship between diseases and treatments during the learning process. In this paper, we propose a network-based approach which assesses the relationship between the diseases and treatments when detecting abuse from claim filings. Our proposed model consists of three stages. During the first stage, a disease-treatment network is constructed based on information extracted from the claim filings. Since the association between diseases and treatments is not explicitly expressed on these filings, we infer the disease-treatment relationship by computing the relative risk (RR). Second stage involves selecting the best graph embedding method from several candidates. We select the best method by comparing performances on link prediction. At the final stage, we solve a link prediction problem as a vehicle to detecting overtreatments. If our link prediction model predicts links to be nonexistent for all of the diseases and treatments listed in a given claim, then the claim is classified as an overtreatment case. We test the proposed model using the real-world claim data and showed that the proposed method classifies the treatment well which does not explicitly exist in the training network.