Author: Mohaimina Haque

The False Claims Act (FCA), 31 U.S.C. §§ 3729-3733, was enacted during the time of the Civil War to combat  fraud committed by the suppliers of  goods to the Union army. What made the False Claims Act different from other fraud and abuse laws was the inclusion of the qui tam provision that allowed private citizens/whistleblowers (“relators”) to bring a lawsuit against companies and individuals who were defrauding the government. Over the years, the FCA has had many changes, but more recently, the FCA has become the primary tool in combatting federal healthcare fraud and abuse.

Healthcare spending is skyrocketing in United States. As of the recent data, published by the Center of Medicare and Medicaid services on December 3, 2015, health spending accounted for 17.5 percent of the nation’s Gross Domestic Product (GDP). In 2014, U.S. health care spending grew 5.3 percent, reaching $3.0 trillion. It is no surprise that, with this gargantuan amount of healthcare cost, there is a renewed attention to expose health care fraud and abuse. False certification is an archetypal example of fraud and abuse perpetrated by healthcare providers.

False certification is when hospitals, physicians, and healthcare providers misrepresent to government health care programs through non-compliance with all the regulations and obligations under their contracts with government. For example, when providers misrepresent non-covered treatments as medically necessary for the purpose of obtaining payments from federal healthcare program.  However, in legal terms there are two categories of certification: 1) express false certification and 2) implied false certification. Express false certification theory applies when a government payee, “falsely certifies compliance with a particular statute, regulation or contractual term, where compliance is a prerequisite to payment.” United States. ex rel. Conner v. Salina Reg’l Health Ctr., Inc., 543 F.3d 1211, 1217 (10th Cir. 2008). While express false certification may not seem too hard to understand on its face, circuit courts across the country have been split on the application of the implied certification theory of liability. Government and qui tam plaintiffs have argued that just submitting a claim for reimbursement alone implies compliance with federal rules, and the implied false certification theory can be a basis for liability. On the other hand, defense counsels have argued that implied certification creates an excessive burden on defendants, especially when defendants have to pay treble damages for noncompliance of a contractual or regulatory terms as conditions of payment.

On June 16, 2016, the  Supreme Court in Universal Health Services v. United States ex. rel. Escobar, 136 S. Ct. 1989 (2016) unanimously held that implied certification theory, can be a basis for FCA liability,  thus resolving a  circuit split. Under the Universal Healthcare decision, “when a defendant submitting a claim makes specific representations about the goods or services provided, but fails to disclose noncompliance with material statutory, regulatory, or contractual requirements that make those representations misleading with respect to those goods or services” 136 S. Ct. at 1994.

However, the Supreme Court limited the wide application of the implied certification theory. First, when government payee submits a claim for government payment, the claim must not “merely request payment,” but also makes “specific representation about the goods or services provides.” Second, the Supreme Court applied the common-law rule to misrepresentation, where “half-truth representations that state the truth . . . . while omitting critical qualifying information- can be actionable misrepresentation.” 136 S. Ct. at 1994. In a footnote, Justice Thomas gave examples of tort law, contract law and securities law to demonstrate the example of common law misrepresentation, which is very much the same in understanding misrepresentation in the FCA context. Third, the Supreme Court pronounced that the materiality standard is demanding. The Court further went on to state that when assessing materiality under FCA, it’s not dispositive that every violation of express condition of payment can trigger liability. Id. at 2003. The Supreme Court identified additional situations on what can trigger materiality, and in sum, it is dependent on specific context.

The much-anticipated Universal Health Services decision resolved the circuit split, and at the same time would thwart attempts by plaintiffs and the government to bring cases for a “garden variety of breaches of contract or regulatory violation.” Overall, this decision is a win for plaintiffs and government because a healthcare provider can still be facing implied certification liabilities under FCA for making a fraudulent claim for payment from the federal healthcare programs, but at the same time defense counsels have an assurance in light of this decision that minor regulatory and contract violations would not result in huge liabilities. Lower courts will determine what lies ahead in the wake of this decision, whether qui tam plaintiffs will have difficulty pleading facts sufficient to prove the test outlined by the Universal Health Services court or whether the defense community have these new guardrails to shield them from unsubstantiated implied false certification liability.

A major push has been under way by medical service providers and their technology outsourcing companies to digitalize electronic records and the attendant Personal Health Information (PHI) that is generated every time we get medical care. In fact, digitalizing healthcare records is widely thought of as one of the best available avenues of obtaining savings and holding back the United States’ rapidly ballooning healthcare costs. However, beyond the immediate savings, digitalization of medical records holds out the promise of running analytics on those records, and thus uncovering precious new information on the trends hidden in our PHI.

One example of a domain where this effort is starting to bear fruit is analytic efforts regarding episodes of care. Let us say that State X analyzes all of the instances where its citizens were treated for a particular disease. Next, after isolating those cases, it normalizes for age, race, gender and other demographic variables, and then analyzes the data for cost per patient for the treatment of these diseases. Once that’s done, the state could then compile and rank all the medical providers providing medical treatment to its population, in order of least expensive to most expensive. It could then provide a financial reward to, say, the five medical providers who are providing this treatment at the lowest risk, while penalizing those medical providers who are costing their patients the most money for getting the same treatment. Or it could try to pick either the best or the worst medical providers, and try to find out why one group is performing so much differently than the other group.

Two recent trends in modern healthcare have accelerated the potential for deriving greater benefits from the PHI analytics. One is the emergence of Cloud technology. Due to the ever-tightening budgets, states are increasingly finding it more and more convenient to move their healthcare operation and applications from dedicated data centers to the Cloud environment. The more security-conscious of these entities are opting for some variation of private clouds. Either way, they are depending on the protection provided by the breach requirements of the Healthcare Insurance Portability and Accountability Act (HIPAA), which allows them to shift the liability of these breach incidents to the business associates rather than the covered entities.

The second trend worth paying attention to is the emergence of Hadoop. Hadoop is an open-source software language developed by the non-profit Apache Foundation. Due to its open-source nature, it can be used without having to incur any commercial licensing costs. Hadoop allows users to process very large data sets on a group of different computers, by splitting up the data, sending it to different computers to be processed, and then putting all the processed data back together in the correct order. This feature allows it to produce results similar to what would be achieved through a supercomputer but by using a group of less-powerful computers.

Entities using the Cloud are becoming increasingly comfortable with allowing their data to reside in external servers. This then allows this data to be analyzed by analytics suites running Hadoop. The result is increasing clarity and decreasing cost. This happy convergence of more powerful analytics and cloud computing should ultimately accrue greater benefit to the healthcare community and the patients they serve.