Statistical Medical Fraud Assessment: Exposition to an Emerging Field

Abstract

Health care expenditures constitute a significant portion of governmental budgets. The percentage of fraud, waste and abuse within that spending has increased over years. This paper introduces the emerging area of statistical medical fraud assessment, which becomes crucial to handle the increasing size and complexity of the medical programmes. An overview of fraud types and detection is followed by the description of medical claims data. The utilisation of sampling, overpayment estimation and data mining methods in medical fraud assessment are presented. Recent unsupervised methods are illustrated with real world data. Finally, the paper introduces potential future research areas such as integrated decision making approaches and Bayesian methods and concludes with an overall discussion. The main goal of this exposition is to increase awareness about this important area among a broader audience of statisticians.

An unsupervised Bayesian hierarchical method for medical fraud assessment

“…Increasing availability and size of medical data generate the need for techniques and automated tools that can assist in transforming the vast amounts of data into useful information and knowledge . Li et al and Ekin et al present comprehensive reviews of the data mining methods that are used for medical fraud assessment. These are classified as supervised, unsupervised or hybrid approaches.…”

Fraud Risk Measurement of Basic Medical Insurance for Urban and Rural Residents in China

“…With the rapid development of medical insurance industry, health insurance fraud has caused losses of hundreds of millions of dollars in many countries every year (USGAO, 1992), thus, it poses a great threat to the safety of health insurance funds. Ekin et al (2018) point out that medical fraud has recently attracted more attention because of the increases in healthcare spending and overpayments. The total health care expenditures in the United States have reached $3.2 trillion, which corresponds to $9,990 per person in 2015 CMS (Bauder et al, 2017).…”

Topic Modelling for Medical Prescription Fraud and Abuse Detection

“…Lastly, Ekin et al . () present an overview that includes sampling and overpayment estimation methods and potential future research areas. However, the prescription fraud literature is relatively limited.…”

“…Bauder et al (2017) presented a survey on the state of healthcare upcoding fraud analysis and detection with an emphasis on data mining. Lastly, Ekin et al (2018) present an overview that includes sampling and overpayment estimation methods and potential future research areas. However, the prescription fraud literature is relatively limited.…”