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Health care audits are crucial in managing the government insurance programs that are estimated to have losses amounting to billions of dollars every year. Statistical methods such as sampling have long been used to handle their size and complexity. Sampling from health care claims data can benefit from multi-stage approaches, especially when the evaluation of the tradeoffs between precision and cost is important. The use of decision models could facilitate health care auditors and policy makers make the best use of these sampling outputs. This paper proposes an integrated multi-stage sampling and decision-making framework that enables auditors address the tradeoffs between audit costs and expected overpayment recovery. We illustrate the framework and discuss insights utilizing a variety of overpayment scenarios for payment populations including U.S. Medicare Part B claims payment data.

Integrated statistical and decision models for multi-stage health care audit sampling