Mining Anomalies in Medicare Big Data Using Patient Rule Induction Method
The public health infrastructure delivers proper health care services as part of the basic needs of the general population. The health care system in the United States is rapidly changing in order to provide a better and convenient healthcare system to the public. Unfortunately, this comprehensive expand has also given rise to healthcare frauds in recent years where losses surge up to $1.8 billion in the country. Organizations such as the Center for Medicare Services (CMS) have started providing accesses to comprehensive medical big data to promote the identification of healthcare frauds as an important research topic. In this paper, we will use the Patient Rule Induction Method (PRIM) based bump hunting method to identify the spaces of higher modes and masses to indicate the peak anomalies in the CMS 2014 dataset. By applying our framework, we can find a way to observe anomalies, which can be attributed to frauds in legal medical practices or other interesting insights in the CMS dataset. This will enable us to characterize the attribute space and explain the events incurring losses to the medicare/medicaid program. The proposed framework is compared with several methods to illustrate the efficiency and effectiveness of the proposed framework for fraud detection.