HOSPITALS LOOKING FOR SOLUTIONS TO THE MEDICARE APPEAL BACKLOG CRISIS
Part IV — Finance Strategy for Hospitals to Cope with the Medicare Appeals Backlog
This is the fourth part of a series covering the Medicare appeal backlog. In Part I, we examined a few backlog statistics. We concluded that the Office of Medicare Hearings and Appeals (OMHA) does not have the capacity to handle this case load. It can process only around 72,000 appeals per year, which is less than one-fifth of the needed capacity. As of July 2014, the backlog had risen to over 800,000 appeals. Now it is said to be well over 1,000,000 appeals. (Does anyone really know?) Appeals are taking more than ten times longer than the statutory framework of 10 months to resolve. That is more than 10 years!
Figure 1 Medicare Appeals are Running Far Slower Than the Statutory Limit. This ties up hospital claims money for very long periods of time.
We suggested that one way to cut down the number of appeals would be to use audit contractors who make mistakes only 1-2% of the time, instead of 66% of the time, as is the case now. Although this would dramatically reduce the number of appeals, it seems as though we are asking too much.
Another option would be to charge the auditors a tax for each denied claim that is reversed on appeal, and hand that money over to the provider (not to the government). Or we could have the auditor be forced to refund all of the provider’s legal fees spent during the appeal. Even though this is a satisfying fantasy, none of it is going to happen.
In Part II we examined the proposal to insert a new actor into the appeals process. Under new proposals, Attorney Adjudicators (AAs) will take over part of the Administrative Law Judge’s (ALJ) work. We concluded that under the current proposals, even if they are adopted, it is unclear how this would help with the backlog except incrementally. In reality, it would take hiring a very large number of Administrative Law Judges to make substantial cuts in the current appeals backlog.
In Part III we examined proposals for bulk settlement through an alternative dispute resolution process called “Settlement Conference Facilitation” (SCF). We concluded that even if the program was doubled, it would amount to a solution for less than one-third of 1% of the backlog. This option is a form of “throwing in the towel”. That is, OMHA wants to have the appeals simply erased, and is willing to pay out around 66% of the amount in question, which happens to be the average rate for over-turned denials.
The problem with this approach is that it simply skips the carefully thought-out process of litigation. Since the claims themselves are not analyzed in this process, and no ruling is made on whether or not they are valid, this option would allow much fraud to slip through the system, and it would deprive the healthcare community of vital feedback information needed to take corrective actions in filing subsequent claims. It is a type of administrative ground hog day.
Today we will look at some of the financial aspects of the backlog. Here, we find that hospitals are well aware of their problem. A large amount of their money is being held up in the appeals backlog, and we have shown that at least two-thirds of this money eventually will come back because the auditors are doing such a poor and inaccurate job in their work.
So now lets look at some of the strategies available for hospitals to adjust to a situation in which a large amount of their claims money is improperly withheld from them, and for indeterminate amounts of time. Some hospitals keep these future denial reversals on the books as account receivables for a while, before they are retired in to the bad debt pile.
For hospitals, in 2016, we can estimate there will be around 1,600,000 claims available for appeal. At current rates, approximately 708,000 will be appealed.
Given that there are 77 ALJs available to handle all of this appeals work, this is a rate of around 9,200 claim appeals per ALJ per year, which of course it far too many, and does not take into consideration either the standing backlog or other provider appeals. So there will be continued delays. Indeed, we see that in the first quarter of 2016, 75% of appeals to the ALJ were taking longer than the 90 days provided for in the statute.
We know that in 2015 approximately $1.3 billion was paid to 1,900 hospitals and that represented 68% of the value of the claims under appeal. These payments were made providing the hospital would withdraw its appeal. There was an average of 158 claims per hospital in this tranche. These numbers define an approximate value of $6,375 dollars per claim appeal.
We know that there are 4,818 hospitals registered with Medicare. So using ratio analysis, we can estimate that in 2016 the value of these claims to be held will be approximately $4.8 billion dollars for around 761,250 claim appeals.
One option would be to finance this amount. Such a bridge loan might come into play when triggered by the appeals process exceeding the statutory time limit, combined with the expectation that they will be resolved either with a bulk settlement, or with an ALJ hearing.
Since the backlog is greatly expanded to more than 130 months, instead of the statutory 10, then it is reasonable to use a 10 year mortgage type calculation, similar to a rolling home equity loan. So at a 3.5% interest rate, the payments would be only $48,000 per month for carrying the $4.8 billion that would be in play. If the interest rate were only 5%, then still the carry payments would be only $52,000 per month. Mere pennies, considering that these interest payments could be shared between all hospitals taken as a whole.
This type of arrangement could be set up through a forward-looking financial institution. Alternatively, hospitals as a purchasing group could enter into a joint self-insurance arrangement so that each could draw upon the pool as needed. The interest payments, minus administrative expenses, would simply expand the amount of funds available to draw upon.
As soon as any settlement was paid out via a bulk negotiation, such as the 68% rule, or through an ALJ hearing, then the hospital would pay back the pool. In the meantime, for those many months that a hospital has its claims held, it will be able to make use of the money that it could expect, but at a small interest rate. For some hospitals, this might be well worth it.
This seems to be a reasonable opportunity for any financial intermediary who is interested in developing new products addressing new markets, particularly ones like Medicare appeals which seem to be rapidly expanding.
This type of financial solution will do nothing to relieve the appeals backlog, but it might help to make the financial pain more bearable for hospitals.
In Part V we will look at investments in IT as a strategy for many hospitals in building their capacities for both filing more acceptable claims, and also for better handling the information aspects of the claims appeals process when required. We will look at investments in Electronic Health Records (EHRs), patient portal software, e-prescribing and lab integration IT investments. For each of these massive investments, we will examine how it will have an impact on the backlog.
The number of Medicare audits is increasing. In the last 5 years, audits have grown by 936%. As reported previously in RACmonitor, this increase is overwhelming the appeals system. Less than three percent (3%) of appeal decisions are given on time within the statutory framework.
It is peculiar that the number of audits has grown rapidly, but without a corresponding growth in the number of employees for RACs. How can this be? Have the RAC workers become more than 900% more efficient? Well, in a way they have. They have learned to harness the power of Big Data.
Since 1986, the world’s ability to store digital data has grown from 0.02 exabytes to 500 exabytes today. An exabyte is one quintillion bytes or 10e+18 bytes. Every day the equivalent 30,000 Library of Congresses is put into storage. Lots of data.
Auditing by RACs has morphed into using computerized techniques to pick targets for audits. An entire industry has grown up that specializes in processing Medicare claims data and finding “sweet spots” on which the RACs may focus their attention. In a recent audit, the provider was told that a “Focused Provider Analysis Report” had been obtained from a subcontractor. Based on that report, the auditor was able to target the provider.
A number of hospitals have been hit with a slew of Diagnosis-Related Group (DRG) downgrades from Internal Hospital RAC Teams camping out in their offices, continually combing through their claims data. DRG is a system that classifies any inpatient stay into groups for purposes of payment.
The question then becomes: How is this work done? How is so much data analyzed? Obviously these audits are not manual. They are Cyber Audits. But how?
An examination of patent data begins to shed light on the answer. For example, Optum, Inc. of Minnesota (associated with United Healthcare) has applied for a patent on “Computer implemented systems and methods of health care claim analysis.” (Application Number 14/449,461, Feb. 5, 2015) These are complex processes, but what they do is analyze claims based on a Diagnosis-Related Group (DRG).
The information system envisaged in this patent appears to be specifically designed to downgrade codes. It works by running a simulation that switches out billed codes with cheaper codes, and then measures if the resulting code configuration is within the statistical range averaged from other claims.
If it is, then the DRG can be down-coded so that the revenue for the hospital correspondingly is reduced. This same algorithm can be applied to hundreds of thousands of claims in only minutes. And the same algorithm can be adjusted to work with different DRGs. This is only one of many patents in this area.
When this happens, the hospital may face many thousands of down-graded claims. If it doesn’t like it, then it must appeal.
Medicare Audits as Asymmetric “Warfare”
Here, there is a severe danger for the hospital. The problem is that the cost of the RAC running the audit is thousands of time less expensive that what the hospital must spend to refute the DRG coding downgrade.
This is the nature of asymmetric warfare. In military terms, the cost of your enemy’s offense is always much smaller than the cost of your defense. That is why guerrilla warfare is successful against nation states. That is why the Soviet Union and United States decided to stop building Anti-Ballistic Missile (ABM) systems — the cost of defense is disproportionately greater than the cost of offense.
Hospitals face the same problem. Their claims data files are a giant forest in which these big data algorithms can wander around down-coding and picking up a substantial revenue stream for the auditor.
By using Artificial Intelligence (advanced statistical) methods of reviewing Medicare claims, the RACs can bombard hospitals with so many DRG downgrades (or other claim rejections) that it quickly will overwhelm the provider’s defenses.
We should note that the use of these algorithms is not really an “audit”. It is a statistical analysis, but not done by any doctor or health care professional. The algorithm could just as well be counting how many bags of potato chips are sold with cans of beer. It doesn’t care.
If the patient is not an average patient, and the disease is not an average disease, and the treatment is not an average treatment, and if everything else is not “average”, then the algorithm will try to throw out the claim for the hospital to defend. This has everything to do with statistics and correlation of variables and very little to do with understanding whether the patient was treated properly.
And that is the essence of the problem with Big Data audits. They are not what they say they are because they substitute mathematical algorithms for medical judgment.
In Part II we will examine the changing appeals landscape and what Big Data will mean for defense against these audits. In Part III we will look at future scenarios for the auditing industry and the corresponding Public Policy agenda that will face lawmakers.
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.
In previous blogs we have explained the importance of using Statistical Extrapolation to win your RAC or MIC appeals.
Here are the top five things to avoid in order to overturn the statistical extrapolation.
DO NOT Base your extrapolation argument on a sample size that is too small
Although it is true that in most cases, the sample size used is too small, this argument alone generally will not prevail in the appeal.
What often happens is that the trier of fact will simply verify if the evidence that the statistical methodology used was generally consistent with the Medicare Program Integrity Manual (MPIM).
There is an unfortunate passage in the MPIM which states that any sample size can be valid as long as the underlying methodology is sound.
And the unfortunate result is that the widespread use of poor and inadequate samples in the extrapolation often leads to results not in your favor.
DO NOT Accept print-outs instead of real electronic spreadsheets
It’s common for contractors to send print-outs with tables of data to back up their statistical work. Don’t accept them for the extrapolation.
Try to get the original electronic spreadsheet because this allows you to verify the statistical work, and to see the details of how it was done.
It also will allow you to run various tests to determine the quality of the work.
Always use the “best business record” rule to argue for the original sheets.
DO NOT Neglect to obtain the full universe file
Audit Contractors generally select a “frame” from the larger universe of all of your claims. From this “frame”, they will select their sample for analysis.
Without the entire universe file containing all claims you have filed, it’s impossible for you to run several important statistical tests that will help uncover any flaws in the contractor’s statistical work.
DO NOT Accept the argument that poor precision works in your favor for the extrapolation
Poor precision in statistics is a sign of bad work. It is often argued that poor precision works in favor of the provider because the lower side of the confidence interval is chosen for the over payment demand number.
Using a confidence interval to adjust for poor precision is not good statistics practice.
Insist that the contractor meet the Federal precision standards that were published in the Federal Register.
DO NOT Ignore all aspects of the extrapolation’s statistical methodology
The statistical work is much more than simply the sample taking and calculation of the extrapolation. ALL of it has to be checked.
In addition, there are a number of PIM rules that must be complied with. Check what was done against each and every one of the PIM rules and that they were followed in every detail.
Breaking a single PIM rule is often is overlooked, but if there are a substantial number of violations, then it helps build the case that the statistical work is not to be trusted and the appeal will be decided in your favor.
RAC Medicare Audit Data From Senate Chairman Hatch
RAC Medicare Audits recovered over $3 billion
A large portion of the initial payment determinations are reversed on appeal. The Department of Health and Human Services Office of Inspector General reported that, of the 41,000 appeals made to Administrative Law Judges in FY 2012, over 60 percent were partially or fully favorable to the defendant.
In Fiscal Year 2014, Medicare covered health services for approximately 54 million elderly and disabled beneficiaries at a cost of $603 billion.
Of that figure, an estimated $60 billion, or approximately ten percent, were improperly paid, averaging more than $1,000 in improper payments for every Medicare beneficiary.
The Barraclough Blog features latest news on events and policies, as well as original Barraclough features and blogs about Litigation support for Medicare and Medicaid appeals and statistical overpayment extrapolations.
The report is full of statistics on the Medicare auditing program. It presents a picture of “profit”, that is, less money is spent by the government on running the auditing program than is recovered.
The report, however, does not address the discrepancies between states for recovery “claw back” of Medicare claims. The calculation is shown in the figure below.
When we chart the amount recovered and compare it to the number of persons living in the state, the difference is vast. In Maine, for example, there was $2 per state resident recovered. However, in North Dakota, there was $36 dollars recovered for each resident.
Does this mean that the health care providers in some states are being more strictly audited than in others? The CMS report does not give any clue to the answer.
So we thought we would reproduce the ruling here, but with only the portions that deal specifically with statistical sampling and extrapolation.
Hospital Insurance and Supplementary Medical Insurance Benefits (Parts A and B) Use of Statistical Sampling to Project Overpayments to Providers and Suppliers
Purpose: HCFA and its Medicare contractors may use statistical sampling to project overpayments to providers and suppliers when claims are voluminous and reflect a pattern of erroneous billing or overutilization and when a case-by-case review is not administratively feasible.
. . . As result of a subsequent audit of the provider’s Medicare claims, the intermediary discovered a large number of bills for medically unnecessary services. . . . The cost of identifying and calculating each individual overpayment itself would constitute a substantial portion of the amount the intermediary might reasonably be expected to recover. . . .
The intermediary notified the provider that, because of the volume of records and the costs of retrieving and reviewing all records for the period as discussed above, it intended to project the overpayment by reviewing a statistically valid sample of beneficiary records and that if it were determined that the provider had been overpaid for the sample cases, it would project the results (again using statistically valid methods) to the entire population of cases from which the sample had been drawn. This would result in a statistically accurate estimate of the total amount the provider had been overpaid for services to these beneficiaries.
[Next are the complaint items filed by the health care provider.]
The provider objected to the intermediary’s use of sampling to project the overpayment on the following grounds:
1. There is no legal authority in the Medicare statute or regulations for HCFA or its intermediaries to determine overpayments by projecting the findings of a sample of specific claims onto a universe of unspecified beneficiaries and claims.
2. Section 1879 of the Social Security Act, 42 U.S.C. 1395pp, contemplates that medical necessity and custodial care coverage determinations will be made only by means of a case-by-case review.
3. When sampling is used, providers are not able to bill individual beneficiaries not in the sample group for the services determined to be noncovered.
4. Use of a sampling procedure violates the rights of providers to appeal adverse determinations.
5. The use of sampling and extrapolation to determine overpayments deprives the provider of due process.
(The succeeding presentation of our decision and supporting facts is applicable also to the use of sampling to project overpayments to suppliers (including physicians) whose claims are processed by Medicare carriers when 100 percent readjudication would be excessively costly or impractical.)
The Supreme Court has long recognized that the Federal Government possesses an inherent right to recover monies illegally or erroneously paid out. . . . The Government’s common law right of recoupment, and its corollary power of recovery by offset, are based on strong considerations of public policy. . . . The common law right to recover Federal funds has been specifically recognized as being fully applicable to the Medicare program. . . . Congress has affirmed the Government’s right to recover Medicare Trust Funds by reasonable means from those who have no right to retain them. . . .
[Next, the ruling makes the “administrative burden” argument.]
Since HCFA’s contractors process vast numbers of Medicare claims . . . A case-by-case review could require a significant diversion of staff from the ongoing claims process, and the cost of determining the amount of an overpayment would be prohibitively high unless a sampling method were used. . . .
We also do not believe that the statutory provisions limiting provider or beneficiary liability preclude the use of sampling.. . .
The use of sampling to determine overpayments for medically unnecessary services or custodial care does not deprive a provider of its right to bill those beneficiaries who knew or should have known that they were receiving these services. . . .
[There are also “public policy” used to justify sampling.]
As between the provider and the Government, strong considerations of public policy favor recovery.. . .
[Next, the famous shifting of the burden of proof is explained.]
Sampling does not deprive a provider of its rights to challenge the sample, nor of its rights to procedural due process. Sampling only creates a presumption of validity as to the amount of an overpayment which may be used as the basis for recoupment. The burden then shifts to the provider to take the next step. The provider could attack the statistical validity of the sample, or it could challenge the correctness of the determination in specific cases identified by the sample (including waiver of liability where medical necessity or custodial care is at issue). . . . If certain individual cases within the sample are determined to be decided erroneously, the amount of overpayment projected to the universe of claims can be modified. If the statistical basis upon which the projection was based is successfully challenged, the overpayment determination can be corrected.
The provisions of the statutes and regulations provide a constitutionally sufficient means by which the provider may challenge an overpayment determination. In cases of denials made through sampling which are based on medical necessity or custodial care, section 1879 of the Act, 42 U.S.C. 1395pp, permits the provider to assert the same appeal rights that an individual has under the statute when the individual does not exercise his rights to appeal. Under Part A, these rights include an opportunity for reconsideration (42 CFR 405.710- 405.716), an oral evidentiary hearing by an administrative law judge (42 CFR 405.720-405.722), Appeals Council review (42 CFR 405.701(c) and 405.724), and finally judicial review if the amount in controversy is $1,000 or more (42 CFR 405.730; 42 U.S.C. 139 5ff (b)(2)). In cases that do not involve medical necessity or custodial care, 42 CFR 405.370, et seq. sets out the applicable procedures through which current payments may be suspended (offset) to recover an overpayment under the Medicare program. . . .
In summary, the use of sampling is a reasonable and cost effective method of projecting overpayments under Medicare. It is not unfair to a provider or supplier to hold it accountable for the receipt of Medicare funds to which it is not entitled under the statute. . . .
Ruling: Accordingly, it is held that the use of statistical sampling to project an overpayment is consistent with the Government’s common law right to recover overpayments, the Medicare statute, and the Department’s regulations, and does not deny a provider or supplier due process. Neither the statute nor regulations require that a case-by-case review be conducted in order to determine that a provider or supplier has been overpaid and to determine the amount of overpayment.
Effective date: This Ruling is effective February 20, 1986.
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.
Source: Memorandum Opinion re: Parties’ Cross-Motions for Summary Judgment, files 12/28/2012, and Barraclough analysis.
A number of arguments were made that established clearly that the statistical work was faulty, and from a scientific point of view was completely invalid.
Arthur J. Schwab the United States District Judge wrote in his opinion “Balko is not entitled to the best possible statistical sample of claims that it submitted . . . Instead, Balko is only entitled to a statistically valid random sample.” (Memorandum Opinion, p. 23.)
Question: Is a “statistical valid random sample” one that is so poor that it lacks any scientific credibility?
What has happened in this case does not bode well for health care providers. Here, statistical work that is demonstrably faulty and inferior and definitely not scientifically valid has been signed off on by the Medicare Appeals Council (MAC), and by the Federal Court that reviewed the case.
This type of sloppy scientific work never would be accepted in any other type of case before a Federal Court in which scientific evidence is evaluated in conformity with Rule 702 “Testimony by Expert Witnesses” of the Federal Rules of Evidence. The question is why is this type of poor and inadequate scientific work OK for audits of health care providers but not OK anywhere else?
Barraclough NY LLC supplies experts for litigation support in Medicare and Medicaid appeals cases.
John Balko & Associates d/b/a Senior Healthcare Associates, Plaintiff, v. Kathleen Sebelius Secretary U.S. Department of Health and Human Services, defendant. United States District Court for the Western District of Pennsylvania. Case 2:12-cv-00572-AJS.