A representative sampling plan for auditing health insurance claims

TL;DR

This paper proposes a stratified sampling plan based on claim dollar amounts for auditing health insurance claims, ensuring representative samples with high probability that sample averages closely estimate population averages.

Contribution

It introduces a novel stratified sampling method tailored for health insurance claims, emphasizing representativeness and efficiency in estimating average claim amounts.

Findings

The sampling plan achieves small sample sizes while maintaining accuracy.

Three estimators and confidence bounds are evaluated for over/under payment estimation.

The method ensures high-probability closeness between sample and population averages.

Abstract

A stratified sampling plan to audit health insurance claims is offered. The stratification is by dollar amount of the claim. The plan is representative in the sense that with high probability for each stratum, the difference in the average dollar amount of the claim in the sample and the average dollar amount in the population, is ``small.'' Several notions of ``small'' are presented. The plan then yields a relatively small total sample size with the property that the overall average dollar amount in the sample is close to the average dollar amount in the population. Three different estimators and corresponding lower confidence bounds for over (under) payments are studied.