Abstract

In the United States, advances in technology and medical sciences continue to improve the general well-being of the population. With this continued progress, programs such as Medicare are needed to help manage the high costs associated with quality healthcare. Unfortunately, there are individuals who commit fraud for nefarious reasons and personal gain, limiting Medicare’s ability to effectively provide for the healthcare needs of the elderly and other qualifying people. To minimize fraudulent activities, the Centers for Medicare and Medicaid Services (CMS) released a number of “Big Data” datasets for different parts of the Medicare program. In this paper, we focus on the detection of Medicare fraud using the following CMS datasets: (1) Medicare Provider Utilization and Payment Data: Physician and Other Supplier (Part B), (2) Medicare Provider Utilization and Payment Data: Part D Prescriber (Part D), and (3) Medicare Provider Utilization and Payment Data: Referring Durable Medical Equipment, Prosthetics, Orthotics and Supplies (DMEPOS). Additionally, we create a fourth dataset which is a combination of the three primary datasets. We discuss data processing for all four datasets and the mapping of real-world provider fraud labels using the List of Excluded Individuals and Entities (LEIE) from the Office of the Inspector General. Our exploratory analysis on Medicare fraud detection involves building and assessing three learners on each dataset. Based on the Area under the Receiver Operating Characteristic (ROC) Curve performance metric, our results show that the Combined dataset with the Logistic Regression (LR) learner yielded the best overall score at 0.816, closely followed by the Part B dataset with LR at 0.805. Overall, the Combined and Part B datasets produced the best fraud detection performance with no statistical difference between these datasets, over all the learners.

Introduction

Healthcare in the United States (U.S.) is important in the lives of many citizens, but unfortunately the high costs of health-related services leave many patients with limited medical care. In response, the U.S. government has established and funded programs, such as Medicare [1], that provide financial assistance for qualifying people to receive needed medical services [2]. There are a number of issues facing healthcare and medical insurance systems, such as a growing population or bad actors (i.e. fraudulent or potentially fraudulent physicians/providers), which reduces allocated funds for these programs. The United States has experienced significant growth in the elderly population (65 or older), in part due to the improved quality of healthcare, increasing 28% from 2004 to 2015 compared to 6.5% for Americans under 65 [3]. Due, in part, to the increase in population, especially for the elderly demographic, as well as advancements in medical technology, U.S. healthcare spending increased, with an annualized growth rate between 1995 and 2015 of 4.0% (adjusted for inflation) [4]. Presumably, spending will continue to rise, thus increasing the need for an efficient and cost-effective healthcare system. A significant issue facing healthcare is fraud, waste and abuse, where even though there are efforts being made to reduce these [5], they are not significantly reducing the consequent financial strain [6]. In this study, we focus our attention on fraud, and use the word fraud in this paper to include the terms waste and abuse. The Federal Bureau of Investigation (FBI) estimates that fraud accounts for 3–۱۰% of healthcare costs [7], totaling between $19 billion and $65 billion in financial loss per year. Medicare accounts for 20% of all U.S. healthcare spending [8] with a total possible cost recovery (with the potential application of effective fraud detection methods) of $3.8 to $13 billion from Medicare alone. Note that Medicare is a federally subsidized medical insurance, and therefore is not a functioning health insurance market in the same way as private healthcare insurance companies [9].