Zero-paid claims are defined as any claims submitted by healthcare providers that are not paid. The problem is that when performing statistical extrapolations, auditors (ZPICs, or Zone Program Integrity Contractors, and others) routinely screen out zero-paid items when they extract the claims from a sample.
This is a violation of at least 12 parts of the Medicare Program Integrity Manual (MPIM), according to a recent administrative law judge (ALJ) decision that discarded statistical extrapolation. Section 220.127.116.11 says that removing zero-paid claims is a “direct violation” of Chapter 8 of the MPIM.
Screening out zero-paid claims is a crooked practice. It is hidden from the provider, because the auditors leave out the details from their “documentation” of the statistical work. Also, and more importantly, it biases the entire audit and extrapolation against the provider. Why? Because zero-paid claims may not be checked to see if they should have been paid. The result: the extrapolation number can only go up, and never down, in favor of the provider.
Mounting a Defense
In order to mount a defense against exclusion of zero-paid claims, your team must convince the ALJ that first, it is a violation of the MPIM, and second, that it really makes a difference in the extrapolation.
The harsh reality is that the MPIM provides a well-known section that gives the auditor a “Get Out of Jail Free” card. In § 18.104.22.168 it states that:
Failure by the contractor to follow one or more of the requirements contained herein . . . should not be construed as necessarily affecting the validity of the statistical sampling and/or the projection of the overpayment.
If I had a dollar for every time an auditor has been exposed with numerous MPIM violations, yet has pulled out this clause and convinced the ALJ to ignore its sloppy work, I would be able to afford that villa in Italy.
Here is what to do:
Step 1 – Detail All MPIM Violations
The first step is to document every single violation of the MPIM made by the auditor. Review every single paragraph of Chapter 8, “Statistical Sampling for Overpayment Estimation,” and see if the auditor either met or failed to meet the requirements.
In this process, if you apply the same level of scrutiny to the auditor’s actions as it has applied to your claims that were rejected, a large number of violations will be found. We have been doing this work since 2001, and have never even once seen a statistical extrapolation done in full compliance with the MPIM. Most violations likely will be found in shoddy documentation provided by the auditor.
Step 2 – Document All Zero-Paid Claim References in the MPIM
Next, the ALJ must be schooled on the zero-paid claims problem, and shown each of the 12 sections of the MPIM that have been violated. There are two parts to this discussion: first, elucidation of the relevant rules, and second, showing with specificity where in the auditor’s work the violations took place.
At the end, the ALJ must understand that there are a number of MPIM violations, that they are significant, and that the auditor has hidden the documentation showing how they made the omissions.
Step 3 – Verify All Zero-Paid Claims
The so-called “universe file” that the auditor will give you will not have the zero-paid claims. They will be hidden. Yet it is crucial – repeat, crucial – to document how many zero-paid claims there were.
After all, if you are making a defense against the extrapolation based on elimination of zero-paid claims, but it turns out that there were none, then the appeal will be laughed out of court. So as a precautionary measure, make sure there were actually zero-paid claims.
It has been our experience that the best way to get this information is from the provider’s own database of submitted claims, and not from the auditor. Two reasons: first, the provider’s database will be definitive, and second, there is no need to tip off the auditor to your defense strategy.
Step 4 – Model the Effect of Zero-Paid Claims on the Extrapolation
Next, you will need to hire a statistical expert who is capable of modeling the effect of zero-paid claims omission on the extrapolation.
Doing this requires sophisticated mathematical skills and experience with Medicare extrapolations. One complicating factor is that it is impossible to know which of the zero-paid claims might have been included in the sample – and of those, how many would have been found deserving of payment. What this modeling will answer is to what degree screening out zero-paid claims introduced error into the extrapolation.
Making Your Case
The result must be explained to the ALJ in order to show how unfair the practice was, and how it made the extrapolation so unreliable that it must be discarded.
The practice of screening out zero-paid claims has gone on for years. It is a relief to see that finally, ALJs are starting to recognize this problem, and are rightfully throwing out these crooked statistical extrapolations.