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 Congress continues to try to fix Medicare’s arduous healthcare audit procedures, as the RAC audit process and healthcare providers continue to be locked into a claims remediation nightmare. The Audit and Appeal Fairness, Integrity, and Reforms in Medicare, or AFIRM, Act of 2015, was introduced on June 3, 2015.
Senator’s Wyden statement about the Finance Committee Markup of this bipartisan effort is that it “will streamline the appeals and audits process so cases are resolved quickly and at the earliest possible step.” The legislation provides for:
More HHS personnel resources pick up the pace in order “to keep up with the enormous increase in appeals.” The Office of Medicare Hearings and Appeals can currently adjudicate 77,000 appeals in a year, far below the 474,000 appeals OMHA received in 2014.
HHS can use its resources more efficiently and process more appeals because of a new track for lower-cost, less-complex cases to be considered by a different set of hearing officers than other cases.
Requiring CMS to better coordinate provider audits “to ensure the entire process is more transparent and efficient, including the creation of an independent Ombudsman position at CMS” in order to assist those considering appeals. Providers who consistently bill correctly are exempted for burdensome audits, as a reward for their business practices.
Although this markup provides some improvement by separating high value from low value cases, Barraclough LLC is dubious about the additional number of people on the CMS payroll to deal with the appeals backlog and the overall impact of the Audit and Appeals Ombudsman which has yet to be fully explained. RAC Audit Appeals would be better served with more data transparency, a change in RAC auditors contingency fee payments, and the quality of initial determinations.
For the full text of Senator Wyden’s statement, click here.
As the AFIRM legislation progress, Barraclough LLC will continue to analyze the impacts and make recommendations for the best course of action.
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. It is “cost effective” to use government parlance. (This calculation does not account for the costs born by the health care providers.)
One would think that from state to state, the amount recovered “clawed back” by the Recovery Audit Contractors (RACs) would be about the same, but it is not. See the Figure below.
By taking the amount recovered in a state and dividing it by the number of doctors in the same state, we can see that in Mississippi the recovery per doctor was around $18,000 dollars. But in Maryland, it was around $1,000 dollars – an eighteen times difference!
It would be interesting to learn more about why this occurs.
Fox Rehabilitation, a health care provider located in Cherry Hill, New Jersey, “provides physical, occupational and speech therapy services – whether in a house, apartment, senior living community, adult medical day care, or outpatient clinic.”
Its CEO, Tim Fox, has reported on the costs of Medicare audits. Here are some of the observations:
Fox Rehabilitation employs 905 persons, with 705 clinicians “who visit patients in their homes in eight states”.
Win rate on audit appeals: 85% are won at the third level of appeal.
Expected revenue 2014 is $90 million.
One of the big problems is compliance with the massive amount of document requests from audits. The cost is so high that Fox Rehabilitation was forced to cut staff in other areas of the company. The increased audits with a reduction of -15% in Medicare payments drove the company to lay off 62 employees. IT also had to reduce the payments to its therapists.
What does “automated review” mean? In practice, it means the use of secretive and proprietary large data mining of records to discover patterns, leading to targeting of health care providers for audits. It is important to keep in mind that this is not really auditing, it is merely finding targets based on patterns of data. So, for example, if a physician or practice works overtime, and on the weekends, and therefore produces more billing that other practices that work a “normal” amount of time, then they will be targeted.
Data mining works on the assumption that if the billing records are out of the ordinary, then there is something wrong, and so the practice should be audited. There are two sides to this. On the one side, deviation from the norm might be a problem; on the other side, it might indicate honest health-care service providers who are working as hard as possible serving a disadvantaged market. In that latter case, an audit based on nothing other than data mining, simply burdens the health care system, and cuts off deserving patients from the health care they are entitled to. This is a non-incentive for the hard workers in the health care space.
The use of big data mining to control health care costs is the world’s greatest pressure towards average performance, that it, towards mediocrity.
The use of big data mining should be abolished, or at a minimum big data mining and the issue of presumption of guilt should be investigated.
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.
It has been our experience at Barraclough that contractors almost always skip the step of taking a probe sample when calculating the required sample size. Even though they do this, they frequently rely on RAT-STATS to make the sample size calculations. The inputs into RAT-STATS requires the variation in the variable being estimated, that is, RAT-STATS requires as one of its crucial inputs the variation (e.g., the mean and standard deviation) of the overpayments (which is the variable being estimated). Since the contractors skip taking a probe sample, they plug the wrong data into the RAT-STATS program, and then make their calculation of sample size by using the variation of the payments instead of the underpayments. This almost always results in RAT-STATS claiming that a smaller sample size is adequate. In the MPIM, Chapter 3, Section 3.10.2, we see a sketch of what a “properly executed” sample design is. In includes:
(1) defining the universe, (2) [defining] the frame, (3) [specifying] the sampling units, (4) using proper randomization, (5) accurately measuring the variables of interest, and (5) using the correct formulas for estimation
It can be argued that taking a probe sample so as to be able to plug the correct (and required) data into the RAT-STATS program falls under the fifth category “accurately measuring the variables of interest”. It follows that if the probe sample is not taken, then according to the MPIM, proper procedures have not been used. Note: RAT-STATS is a free statistical software package that providers can download to assist in a claims review. The package, created by OIG in the late 1970s, is also the primary statistical tool for OIG‘s Office of Audit Services.
“Failure by the [contractor] to follow one or more of the requirements contained herein does not necessarily affect the validity of the statistical sampling that was conducted or the projection of the overpayment.
An appeal challenging the validity of the sampling methodology must be predicated on the actual statistical validity of the sample as drawn and conducted.
Failure by the [contractor] to follow one or more requirements . . . should not be construed as
necessarily affecting the validity of the statistical
sampling and/or the projection of the overpayment.”
The language quoted from the MPIM seems to indicate that no appeal may be based on the sampling methodology.
No matter how the contractor arrives at their sample, it does not seem to be reviewable.
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.”