Sample Design for Medicaid and Healthcare Audits

TL;DR

This paper develops new sample size and design tools for Medicaid and healthcare audits, accommodating partial claim errors and improving accuracy over previous all-or-nothing error models.

Contribution

It introduces a flexible error model for partial claim errors and derives variance estimates for sample size calculation under this model, expanding existing methodologies.

Findings

Partial error model improves audit sample design accuracy.

Ratio estimation outperforms simple expansion under all-or-nothing errors.

Optimal stratification is independent of error rate in ratio estimation.

Abstract

We develop several tools for the determination of sample size and design for Medicaid and healthcare audits. The goal of these audits is to examine a population of claims submitted by a healthcare provider for reimbursement by a third party payer to determine the total amount of money which is erroneously claimed. For large audit populations, conclusions about the total amount of reimbursement claimed erroneously are often based on sample data. Often, sample size determination must be made in the absence of pilot study data and existing methods for doing so typically rely on restrictive assumptions. This includes the `all-or-nothing errors' assumption which assumes the error in a claim is either the entire claim amount or none of it. Under the all-or-nothing errors assumption, Roberts (1978) has derived estimates of the variances needed for sample size calculations under simple expansion and ratio estimation. Some audit populations, however, will contain claims which are partially in error. We broaden existing methodology to handle this scenario by proposing an error model which allows for partial errors by modeling the line-item error mechanism. We use this model to derive estimates of the variances needed for sample size determination under simple expansion and ratio estimation in the presence of partial errors. In the absence of certain error-rate parameter estimates needed to implement our method, we show that conservative sample sizes can be determined using the claim data alone. We further show that, under all-or-nothing errors, ratio estimation will tend to outperform simple expansion and that optimal stratification is independent of the population error rate under ratio estimation. The proposed sample design methods are illustrated on three simulated audit populations.