A new report(*) by the U.S. Government Accountability Office (GAO), shows that the Medicare Appeals system is crashing because the number of appeals filed exceeds the capacity of the Administrative Law system. The number of cases filed has exploded, but there has been inadequate improvement in capacity.
For the period 2010 -2014, there has been a substantial growth in appeals. Here is the data:
Level 1 +62%
Level 2 +238%
Level 3 +936% <— look at that number!
Level 4 +267%
The greatest increase in appeals has taken place in that place where the appeal is the most complicated: Appeals to Administrative Law Judges (ALJs) increased by almost 1,000 percent.
This data indicates that providers increasing are dis-satisfied with the results of their audit. They are more likely to appeal. Also, they are considerably less satisfied with the decisions of the QICs.
So this places an incredible burden at the ALJ level. The +936% increase at Level 3 (ALJ) represents a change from 41,733 appeals in 2010 to 432,534 appeals filed in 2014.
An Administrative Law Judge (ALJ) gets the same benefits as other Federal Employees. Each year they get 26 vacation days, and 10 holidays. This leaves 329 working days per year for them to do their work.
The maximum number of cases recommended per month for an ALJ is 60, but the average is much lower.
At 60 hearings/month, taking into account the number of holidays, that is approximately 2.2 hearings per day for an ALJ.
There are 77 ALJs and this should lead to a total of 168 hearings/day; and 55,474 hearings/year.
If the number of appeals has risen to 432,534 hearings per year, and each gets a hearing, and the ALJs are working at the unrealistic maximum rate of 60 hearings per month, then in order to meet this new load, a minimum of 599 ALJs need to be on the job.
That is ten times the number of ALJs needed.
But this number assumes a sustained rate of 60 hearings per month, and that is unrealistic. A better number is 45 hearings per month.
If this number is used, then 821 ALJs are needed, based only only the 2014 data, which already is obsolete, as the number is increasing.
(*) See U.S. Government Accountability Office, Medicare Fee-for-Service: Opportunities Remain to Improve Appeals Process, May, 2016, 88 pps., Document number GAO-16-366.
After receiving an enormous demand for reimbursement based on a statistical extrapolation, it may be possible to get the extrapolation thrown out by the Administrative Law Judge (ALJ) for a Medicare claim reversal.
If this occurs, you won’t have to pay the large extrapolated amount, but may need to pay only for any individual claims that have been ruled to be invalid, an amount that usually is much smaller.
How do you get this situation to be reversed in your favor? Barraclough Health does it by using an accurate statistical methodologyrather than let the results of the inaccurate methodology used by RACs stand.
In this Barraclough Health Medicare Claims Reversal Case Study, we show how using our statistical methodology saved the client $1,297,700 dollars.
Medicare ZPIC Claims Demand
Dr. X received a Medicare reimbursement demand for approximately $1,300,000 dollars.
But the auditor (the ZPIC) had examined only 35 of the thousands of Dr. X’s claims.
Of these 35 claims, they had rejected 17 of them.
The value of those claims was approximately $2,300 dollars.
Dr. X then contacted Barraclough LLC.
Barraclough’s VALID Statistical Methodology
Barraclough’s expert team completed an extensive analysis of the 4 statistical methodology used by the ZPIC, a formidable task.
The Barraclough team checked:
all of the calculations that were made
the details of how the sample was taken
the formulas that were used in picking the sample size
By doing this extrapolation, the general pattern of decisions was revealed.
The Barraclough team found that the contractor had made many errors in their work, including:
Manufactured data was an essential part of the calculations used in determining the needed sample size.
Picking sample sizes is not an arbitrary act – there should be a method behind it.
One of the formulas that is needed to determine sample size requires an input representing the underlying variation in the variable being estimated.
For an over payment analysis, this would mean that it is necessary to understand the underlying variation in the over payments.
The only way to get this information is to analyze a number of claims to make a measurement.
But the contractor had skipped this step entirely, and had simply manufactured this number, plugged it into the formulas, and decided to use a sample size of 35.
When the Barraclough Team double-checked the calculations, we put the correct number into the same formulas and found that the required sample size was more than 100 times larger.
During the hearing the contractor admitted that they had failed to make the required measurement.
After finding many other problems with the statistical work, it was possible to conclude that the contractor had failed to use an acceptable statistical methodology.
ZPIC Work Falls Short of Standard
Since the MPIM (Medicare Program Integrity Manual) requires that a valid statistical methodology must be used, we were able to show that the work of the ZPIC had fallen short of the standard.
Result: Medicare Claim Reduced Significantly
The extrapolation was thrown out by the ALJ.
Instead of having to pay the extrapolated amount of approximately $1,300,000 dollars, Dr. X. ended up paying approximately $2,300 dollars, a difference of $1,297,700.
Generally, it is after the first level of appeal. This rule is about to change, so that the RACs will get their fee only after the matter is completely decided, after the second appeal, or perhaps beyond.
What is the timing involved? Generally, a first level appeal takes 100 days. This means that the RAC gets their fee in about three months. But if the new rules come into effect, then the RAC will need to wait to see if their audit withstands the second-level appeal, but that is an average of 400 days long — more than one year!
So obviously this would have an effect on the cash flow for the RACs.
This “second appeal” proposed rule appears to be a compromise because these matters often are decided at the Administrative Law Judge (ALJ) level at a hearing. When does that take place? The data is fuzzy, but it appears to be frequently more than 750 days, that is, more than two years later.
If the health care providers had their way, then the RAC would not get paid until a final decision is made. Yes, the RAC would have to wait to get its money, it would not have the “free use” of money that it can hold until a final decision.
This is important because many ALJ decisions over-turn RAC audits.
Two RACs evidently have protested this new rule. The protestors include HMS Holdings (HealthDataInsights) and CGI Federal (which was fired after it botched the roll-out of the Obamacare enrollment website). This new rule, after all, would disturb their financial model because they would not get paid until they actually earn their money.
What is the right answer to this?
There is no answer that is acceptable to everyone, but lets look at the dynamics. Under the current “100-day” system, the RAC has every incentive to rush as quickly as possible through the first level of appeal. In that way, it gets its money as fast as possible. But after the first level appeal, the RAC has zero incentive to do anything but drag its feet and slow down the process. It is incentivized to delay the process, because the longer the delay, the more it can hold on to money that later might be taken away should the final decision go in favor of the health care provider.
This delay behavior frequently is done through the standard practice of taking every day of available time to fulfill even the smallest request. Even if something takes 10 minutes of work, if the RAC has 60 days to do it, then it will complete the 10 minutes of work on the 60th day. The longer the RAC delays, the longer it holds on to its money.
The health care provider, however, suffers immensely from this practice. It is guaranteed that the RAC will work for as long as possible and using every possible tactic to lengthen the appeal time. There is little if any attention given to the difficulties faced by the health care provider.
So under the proposed system, the RAC will have have incentives to operate efficiently up through the second level appeal, then it can continue to drag its heels beyond that.
It is a compromise.
Barraclough has been in dozens of cases involving the statistical extrapolation part of Medicare appeals. It always has amazed us how long it takes for the RACs to respond to even the most basic information. This lack of responsiveness slows down the process, harms the health care provider, and perverts the course of justice. Anything that can be done to curb these practices is a good thing.
So the question that arises is this: Are contractors free to employ any accuracy they wish in their work, or are there standards that have been suggested or published by the Federal Government?
As it turns out, there appears to be some guidance from two sources.
In the May 5, 2010, report by the Acting Administrator and Chief Operating Officer of the Centers for Medicare & Medicaid Services (CMS) On page 3 of that report, the section titled “Precision-level requirements” states:
“[Office of Management and Budget] OMBCircular A-123, Appendix C, states that Federal agencies must produce a statistically valid error estimate that meets precision levels of plus or minus 2.5 percentage points with a 90-percent confidence interval or plus or minus 3 percentage points with a 95-percent confidence interval.”
There is a note in the document: Under these assumptions, the minimum sample size needed to meet the precision requirements can be approximated by the following formula, which is used in the examples:
Where n is the required minimum sample size and P is the estimated percentage of improper payments (Note: This sample size formula is derived from Sampling of Populations: Methods and Applications (3rd edition); Levy, P. S. & Lemeshow, S. (1999); New York: John Wiley & Sons; at page 74. The constant 2.706 is 1.645 squared.
In the CMS-issued Federal Register, 72 Fed. Reg. 50490, 50495 (Aug. 31, 2007), the error estimate should meet precision levels of plus or minus 2.5 percentage points with a 90-percent confidence interval, and the State error estimates should meet precision levels of plus or minus 3 percentage points with a 95-percent confidence interval.”
So it appears that these standards, which are fairly good, have been twice promulgated by the Federal Government.
This is a case in which the extrapolation was thrown out.
Here, the Medicare Appeals Council agreed with the “determination that the sampling was sufficiently flawed to preclude calculation of an overpayment by extrapolation”.
Although the appellant had made a number of arguments attacking the statistical extrapolation, the MAC relied on two errors in throwing out the extrapolation:
“The errors are: 1) the PSC provided the independent statistical expert with sample data which assigned some claims to the wrong stratum; and 2) the PSC provided the independent expert with a second CD containing an Excel set of sample data with significant discrepancies from the first set of data, and the PSC was unable to clarify the discrepancies, to identify which set of data was applicable, or to explain the significance of the second set of data.”
This provides at least three check points when providing litigation support to a health care provider:
First, always have the statistical expert carefully check that all claims in any strata strictly fit the definition of the strata;
Second, look for any instance in which inconsistent records have been handed over by the contractor; and
Third, demand detailed explanations from the contractor for each and every inconsistency found in the data.
Unfortunately, it has been our experience at Barraclough that these arguments do not always result in an extrapolation being thrown out. Rulings are inconsistent with each other – sometimes this argument works, sometimes it does not.
Health Care Providers have been unsuccessful in getting judicial review of the complex algorithms used in these automated reviews. For example, it is impossible to determine how fair is the targeting process. When auditors are asked to provide this information, health care providers are told that this information is a “trade secret“, and in any case is not reviewable by the Administrative Law Judge (ALJ).
How successful are automated reviews? The data shows that 91% of the claw-backs from health care providers were the result of so-called “complex reviews”, not dependent upon the semi-automatic review process.
In writing about semi-automated reviews, CMS states:
“The first part is the identification of a billing aberrancy through an automated review using claims data. This aberrancy has a high index of suspicion to be an improper payment. The second part includes a Notification Letter that is sent to the provider explaining the potential billing error that was identified. The letter also indicates that the provider has 45 days to submit documentation to support the original billing.”