The OIG recently released a podcast highlighting and explaining use of statistical sampling in audits. Laboratories are under the microscope of OIG scrutiny with this year’s Work Plan generically listing independent clinical laboratory billing as an area of concern. So it’s wise to understand the OIG’s audit techniques.
What is Statistical Sampling?
Statistical sampling is an audit technique the OIG uses when it isn’t practical or feasible to review every claim in a universe of suspect claims. Sampling is a lot of times used when you have similarly situated claims with the same CPT Code or the same range of CPT codes for services provided in the same time period and “it would be administratively burdensome to review the records for tens of thousands of claims,” explains attorney Kevin R. Miserez, of Wachler & Associates, P.C. in Michigan. “The relevance of this podcast is that the OIG is highlighting a method used more and more frequently when a sustained error rate is found” in initial audits, he adds.
There must be grounds to implement the statistical sampling method, however. “They have to determine a sustained or high level of payment error to even proceed with statistical sampling,” Miserez advises. For example, if a probe audit of 40 claims reveals a 100% error rate within those 40 claims, statistical sampling is warranted to project the error rate to a universe of claims within a certain time period.
In the podcast, Lisa Wombles, an OIG senior auditor, interviewed Jared Smith, an office of audit statistician for the OIG, who explains how the sampling is tailored. The four key objectives for statistical sampling are:
- To yield a “statistically valid” sampling;
- Provide “efficient” review;
- Generate a sample “representative of the larger group”; and
- “[P]roduce a valid estimate of any overpayment.”
Medicare Contractors also use statistical sampling in audits or claim reviews. The Medicare Program Integrity Manual lays out requirements and guidelines for CMS contractors to follow in statistical sampling, says Miserez. He also explained the sampling doesn’t have to be perfect it just needs to be statistically valid. “More times than not, the sampling methodology used is determined to be appropriate,” he says. The OIG’s podcast indicates that to ensure fairness, the agency uses “an estimation method that gives the provider the benefit of the doubt for any uncertainty in the sampling process.” Miserez explains that this benefit of the doubt is generally facilitated with a confidence interval (generally 90%) with a high limit and a low limit and the OIG will select the low limit as the amount of overpayment to be demanded to account for the uncertainty in the sample design.
So the resulting overpayment estimate should be less than that which would be found if every claim were reviewed. Smith also explained in that podcast that the OIG also reduces an overpayment estimate to account for claims that might have been refunded, not erroneous or canceled. Unfortunately for providers, says Miserez, even with this benefit of the doubt and reduction in the estimate, “when you calculate the overpayment, whether its $1 million or $1.2 million, either number is significantly high anyways.”
Smith asserted that sampling benefits the provider by alleviating the need to produce all the supporting documentation for every claim under review.
Appealing Overpayment Estimates Based on Statistical Sampling
The podcast also referenced the right to appeal the results of the statistical sampling through the Medicare appeals process. Miserez explains the provider’s recourse following an OIG audit as follows:
First the provider can submit a rebuttal to the OIG’s draft audit findings before the OIG releases its final audit findings and provide arguments to refute those findings. Arguments that may be made to refute an audit finding could include that the sample selected wasn’t sufficiently random, or the sample selected is not representative of the universe, advises Miserez. When the OIG issues a final audit report and recommends to CMS a recoupment of an overpayment, Medicare appeal rights begin. The provider can seek a redetermination by appealing to the Medicare Administrative Contractor, then a reconsideration to a Qualified Independent Contractor and then a hearing before an administrative law judge (ALJ). Finally, an appeal can be made to the Medicare Appeals Council (MAC) followed by federal district court.
If a provider successfully challenges the statistical sampling at the reconsideration stage, CMS cannot appeal, says Miserez. While this doesn’t happen often, when it does, the provider is only liable for the amount of any overpayment relating to sample claims shown to be in error, rather than the much higher projected overpayment demand based on the statistical extrapolation.
After the reconsideration stage, CMS can appeal any decision finding a problem with the statistical sampling methodology. “The MAC doesn’t often throw out the statistical methodology,” says Miserez; “often times at the MAC and ALJ level, CMS is permitted to correct any errors found in the sampling methodology.” The good news is that when you appeal a statistically projected audit—you are appealing the sample claims reviewed and if you get some of those overturned (e.g. deemed medically necessary and appropriately billed), that reduces the error rate applied to the universe of claims. So for every individually sampled claim that you prove accurate, you are reducing the overpayment calculation by more than just the value of that single claim, he explains.
What You Should Do
Generally, such sampling methodology tends to be pretty representative, says Miserez. “Practitioners don’t tend to change their documentation habits overnight,” he explains. That means errors found in a small sampling of claims are usually a reasonable indicator that the same errors will be found in other claims as well.
In fact, the OIG recently issued an audit report using statistical sampling to review a Louisiana hospital’s claims. The OIG conducted an audit covering 1,078 claims considered at risk for billing errors. The audit involved a random sample of 158 claims from an audit period running from January 2011 through September 2012. Of those 158 claims, the OIG found 51 claims were not fully compliant. Using those results, the OIG estimated the hospital received over $1.6 million in overpayments for that audit period.
With significant overpayment liability at stake, it’s critical that laboratories and all providers are aware of documentation and billing requirements and rules for Medicare reimbursement to avoid an error rate that justifies statistical sampling. “Providers have to take it upon themselves to ensure their documentation and billing practices align with Medicare reimbursement requirements,” advises Miserez.
To listen to or review transcripts of this and other OIG podcasts, visit https://oig.hhs.gov/newsroom/podcasts/reports.asp
Medicare Appeal Rights: http://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/downloads/overpaymentbrochure508-09.pdf
Medicare Program Integrity Manual: http://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/Downloads/pim83c08.pdf
OIG Audit Report http://oig.hhs.gov/oas/reports/region6/61300042.pdf
Takeaway: The OIG’s podcast spotlighting its statistical sampling methodology is timely and should be of interest to labs given significant scrutiny of laboratory billing.