Texas Attorney General Greg Abbott recently announced the recovery of more than $ 1 billion dollars in Medicaid overpayments since 2002, a sizable recovery and certainly welcome news for the Texas taxpayers who foot the bill.
In fact, Texas Medicaid expenditures total almost $ 30 billion dollars each year.
Having litigated, negotiated, adjusted, and adjudicated in one form or another within the vast insurance claims tree, don’t the wonders rarely cease? Encounters with processors and adjusters carrying large checks a provider or a policyholder had returned to the state claiming they had already been paid can make a jaw hit the floor quickly in skeptical wonder. In one memorable instance with a particularly large check, the question begged asking: “How can this be?” After all, there were system edits, process audits, supervisory controls, and re-pricing software (in the case of medical claims), all of which theoretically functioned to prevent the very thing that happened from happening.
Events like this one led to a lifelong journey to understand the source, the nature and the degree of payment error that seems inherent in medical claims payments in particular.
As an example, the federal government admits to a six percent payment error rate in the Medicare program; do the math on that and there’s enough money to fund several federal agencies.
With Texas Medicaid now comprising about 25 percent of the state budget, the Affordable Care Act (AKA “Obamacare”) swelling the ranks of recipients of taxpayer funded medical care, and the health care inflation rate consistently outpacing all others, Texas leaders are beginning to take notice and take action.
With this upward pressure on Medicaid costs, what are the tools at the disposal of government to bring those costs down? Here are a few examples and what distinguishes each:
(1) Fraud identification: Currently a hot topic, fraud identification is typically a predictive-based approach typically driven by a software package that attempts to profile or score payment transactions for the possibility of fraud, misrepresentation, or abuse. Once a suspect emerges, additional investigation determines the presence of illicit activity, triggering the involvement of law enforcement. Because of the substantial cost of a fraud investigation to determine the presence or absence of fraudulent activity, fraud programs are usually limited to large dollar scenarios, generally starting at around $100,000 as the minimum level for investigative involvement.
(2) Pre-payment: Currently the darling of Federal Medicare, the idea here is to eliminate payment error before it happens. Applying a series of computerized rules, payment would be delayed – the term of art is “pended” – while the paying entity examines the suspected inaccuracy. Pre-payment, if perfected, is truly the magic bullet of payment accuracy. The challenges however are daunting. First, for pre-payment to work, the underlying factual data surrounding a claim must be perfectly accurate in real time or at least at the time a claim is processed and the payment determination is made. To take a simple example, a pre-payment process should, in theory, know that a potential beneficiary is actually still benefit eligible at the time the claim is processed, or at least that they are still alive. This, however, is often not the case. Enrollment updates are not generally managed in real time. The same is true with fee schedules, network affiliations, bill cycles, benefit schedules, bill accuracy, demographic data and more. No data associated with these exist in real time and therefore the prepayment system will suffer the same perils as the current claims processing environment we live in today.
Second, for prepayment to work, the bill must then be digitally re-adjudicated for accuracy, begging the question, “Why didn’t the process work the first time?”
Third, payment will be delayed. Indeed many states mandate the prompt payment of medical claims. While prepayment may be applicable in certain specific situations – duplicate payments come to mind – the challenges mitigate against its ultimate place in the cost control armory.
Prepayment is useful in some instances but not necessarily the magic bullet it’s been touted to be.
(3) Pay and Chase: Unfortunately, this term is simplistic to a degree that it makes the concept sound at best foolish and at worst sneaky. However when properly understood the concept avoids both the resource limitations of fraud investigation and the data instability of pre-payment. A better way to describe “pay and chase” would be “post payment error identification,” since it allows for the claims cycle to run its normal course: bills get submitted, processed, adjudicated and ultimately resolved but then circles back to review what happened and make sure it was done right. There are several advantages to this approach. Since the bill was paid, searching for error has time on its side. Error searches can, for example, go back in time for months to find leakage. The data instability issue is gone. The data underlying a claim being reviewed has had a chance to “settle down” so to speak. The post payment examiner can now say for certain who was eligible and who wasn’t, determine the correct schedule to apply, how many payments contained services that should not have been covered (or covered at a different rate) and so on. There are no limits to scope of inquiry and literally every aspect of every payment is or can be reviewed. Finally, a computer can do the work to a high degree of specificity because the nature of the search is linear rather than predictive, based on yes/no, if/then type logic rather than the profiling or scoring of the predictive approach. Finally, there is no limit to the size of the claim. A ten dollar claim gets the same scrutiny as a $10 million claim and the determination of the presence of error is a matter of fact, not estimation.
When a state spends $30 billion each year as Texas does, every dollar counts.
While fraud identification has its place, and indeed is a necessary weapon in the battle against rising costs, its scope is necessarily limited. Next, until a point is reached in social development when the world stops changing the inevitable, data lag will seriously limit prepayment efficacy.
In the end, post payment error identification remains the most potent tool in the arsenal to recover lost Medicaid dollars.
Jim Del Vecchio is the founder and Chief Executive Officer of Asset Protection Recovery Group (APRG), a Texas-based industry leader in data mining and overpayment recovery.
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