An Integrated Decision Making Framework for Medical Audit Sampling

Abstract

The loss of three to ten percent of annual health care expenditures to fraudulent transactions makes medical audits paramount. In order to handle the size and complexity of medical claims, the use of analytical methods and information technology tools to aid in medical audits is necessary. In general, sampling frameworks are utilized to choose representative claims. However, these are not integrated within audit decision-making procedures. As a novelty, this paper presents an integrated decision-making framework for medical audit sampling. We propose a simple but effective optimization method that uses sampling output and enables auditors address the trade-offs between audit costs and expected overpayment recovery. We use U.S. Medicare Part B claims payment data to demonstrate the framework.