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Information-theoretic multistage sampling framework for medical audits

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The sampling resource allocation decisions for medical audits of outpatient procedures are crucial and challenging because of the large payment amounts and heterogeneity of the claims. A number of frameworks are utilized to help auditors address the trade-offs between efficiency and cost while having valid overpayment amount estimates. As a potential improvement, this paper presents a novel information-theoretic multistage sampling framework. In particular, we propose an iterative stratified sampling method that uses Lindley's entropy measure to evaluate the expected amount of information. We use US Medicare Part B claims outpatient payment data and investigate the versatility of the framework for different overpayment scenarios and resource allocation designs. The proposed method results in reasonable coverage and lower estimation errors for the proportion of overpaid claims and overpayment recovery amounts. Our sampling method is shown to outperform the current stratification method of practice, ie, Neyman allocation, for many scenarios. The framework also can be used to make probability statements on variables of interest, such as the number of overpaid claims.

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