A scoring model to detect abusive medical institutions based on patient classification system: Diagnosis-related group and ambulatory patient group.

The detection of medical abuse is essential because medical abuse imposes extra payments on individual insurance fees and increases unnecessary social costs. To reduce the costs due to medical abuse, insurance companies hire medical experts who examine claims, suspected to arise as a result of overtreatment from institutions, and review the suitability of claimed treatments. Owing to the limited number of reviewers and mounting volume of claims, there is need for a comprehensive method to detect medical abuse that uses a scoring model that selects a few institutions to be investigated. Numerous studies for detecting medical abuse have focused on institution-level variables such as the average values of hospitalization period and medical expenses to find the abuse score and selected institutions based on it. However, these studies use simple variables to construct a model that has poor performance with regard to detecting complex abuse billing patterns. Institution-level variables could easily represent the characteristics of institutions but loss of information is inevitable. Hence, it is possible to reduce information loss by using the finest granularity of data with treatment-level variables. In this study, we develop a scoring model by using treatment-level information and it is first of its kind to use a patient classification system (PCS) to improve the detection performance of medical abuse. PCS is a system that classifies patients in terms of clinical significance and consumption of medical resources. Because PCS is based on diagnosis, the patients grouped according to PCS tend to suffer from similar diseases. Claim data segmented by PCS is composed of patients with fewer types of diseases; hence, the data distribution by PCS is more homogeneous than data classified with respect to medical departments. We define an abusive institution as an institution having numerous number of abused treatments and containing their large sum of the abuse amounts, and the main idea of our model is that the abuse score of an institution is approximated as the sum of abuse scores for all treatments claimed from the institution. The proposed method consists of two steps: training a binary classification model to predict the abusiveness of each treatment and yielding an abuse score for each institution by aggregating the predicted abusiveness. The resulting abuse score is used to prioritize institutions to investigate. We tested the performance of our model against the scoring model employed by the insurance review agency in South Korea, making use of the real world claim data submitted to the agency. We compared these models with efficiency which represents the extent to which the model may detect the abused amounts per treatment. Experimental results show that the proposed model has efficiency up to 3.57 times higher than the model employed by the agency. In addition, we put forward an efficient and realistic reviewing process when the proposed scoring model is applied to the existing process. The proposed process has efficiency up to 2.17 times higher than the existing process.