Federal Leadership Needed to Fight Medicaid Fraud, Waste and Abuse
Data analytics offers unparalleled promise to stop fraud, waste and abuse in state-run Medicaid programs, but the lack of standards and federal leadership is holding states back, experts say.
Improper payments account for about 5 cents of every Medicaid dollar, according to federal estimates, or about $29.1 billion of the $547.7 billion program in 2015 alone. Those improper payments fall into three categories:
- Intentional deception or misrepresentation
- The overuse or inappropriate use of services and resources
- Provider practices that are inconsistent with sound fiscal, business or medical practices, such as providing medically uneccessary services, or beneficiary practices resulting in unnecessary Medicaid costs.
Spotting all three consistently is challenging. Investigators can dig through data and follow hunches, but the volume of claims is so great that without automation, agencies fight a losing battle. Advanced data analytics promise to improve detection, but a lack of standards and incentives has thus far left states on their own.
On the federal level, the Centers for Medicare and Medicaid Services (CMS) within the U.S. Health and Human Services Department has demonstrated success in combatting fraud, waste and abuse in Medicare. By contrast, Medicaid programs are state run, each unique in its own right and with its own data sets and formats.
That has state agencies looking for help.
“CMS has to be a partner with us in analytics and set emerging best practices and standards for us to reach,” says Ted Dallas, who heads Pennsylvania’s Department of Human Services. “The federal government should try to incentivize state agencies to use analytics by paying a greater percentage of Medicaid dollars to fund analytics programs.”
CMS uses its own Fraud Prevention System (FPS) to detect Medicare fraud, one similar to those used by banks and credit card companies to spot credit and debit card fraud. By applying analytics to bills as they come in, CMS can identify suspicious billing patterns. Since implementing FPS in 2011, CMS has generated $820 million in savings – a 10:1 return on investment in the program’s first three years alone.
That’s the kind of performance states would like to achieve in their individual Medicaid programs, says Pennsylvania’s Dallas.
His agency’s fraud, waste and abuse program relies today on people, rather than advanced analytics technology. The fraud team applies rules to review checks, detect claims anomalies and spot patterns. Last year, his department stopped $648 million in fraud waste and abuse through a combination of cost avoidance – catching errant payments before they were made – and cost recoveries, when the agency chases down errant payments and gets the money back. In all, the fraud program has saved the state about 5 percent of its $13 billion budget.
Yet Dallas believes he could reap even greater savings if he could leverage the expertise of CMS. He wants guidance on which data sets and formats are most useful and which tools will yield the best results. But he also understands that’s harder than it looks.
“If you’ve seen one Medicaid program, you’ve seen one Medicaid program,” Dallas says. Each state is unique. “To create one system and say it’s going to work for every state is not realistic.”
Jala Attia, senior program director of Program Integrity Solutions for General Dynamics Information Technology (GDIT), has spent years creating solutions to combat fraud, waste and abuse. “Today’s challenge is identifying fraud waste and abuse faster – before payments are made,” Attia says. “This responsibility is carried by every healthcare payer in the nation and impacts us all.”
Attia also believes that while strong analytics are a key factor in detection, the importance of prevention cannot be overstated. “Imagine if you could leverage all of the historical data gathered about inappropriate behavior and mitigate that risk moving forward,” she says. “Imagine the losses that can be prevented and put towards improvements of our healthcare programs.”
States are under the gun to process payments quickly to keep practitioners in the program. Participating doctors accept that Medicare may not pay them as much as other insurers, but they expect prompt and consistent payment in return. That argues for automation in pre-payment analytic work. But most states aren’t ready for that. Some don’t even have specialized fraud, waste and abuse teams, let alone automation tools.
“Right now, there is too much inconsistency in terms of what data must be reported and how to report it,” Attia says. “States need guidance and support in order to effectively measure the impact that their fraud programs have at the state level.”
Bill Fox, vice president of Healthcare and Life Sciences at MarkLogic, of Tysons Corner, Va., says CMS is moving toward setting standards, albeit slowly. He also emphasizes that the problem is bigger than simply analyzing bills. “The Holy Grail is to get as much of a diversified data set as you can,” Fox says, “including payment information, relationships between people, ownership connections, real estate records and other unstructured data.”
Those connections can turn up critical information, such as cases where investigators find fraud at one provider and later track its ownership to other providers that have been accused of the same or similar crimes.
Working against the fraud detectors are many of the same rules put in place to protect patients and providers, such as privacy rules and regulatory limitations. Another problem: legacy processing systems that use proprietary data formats or have cumbersome interfaces. These make integration both a structural problem and a technology challenge. While the technology is maturing, structural changes are always complex and time-consuming.
The Digital Accountability and Transparency Act of 2014, also called the DATA Act, could prove helpful. The measure standardized data formats for all federal billing, notes Linda Miller, who spent 10 years with the General Accountability Office (GAO) and is now a director at Grant Thornton LLP.
John Stultz, a Government Fraud Solutions Architect at database software specialist SAS, says another way CMS could show leadership in this area is by expanding existing programs.
“CMS has set up a lot of areas where they help agencies with fraud, waste and abuse,” Stultz says. “But there is more they could do in the area of establishing a clearing house, or secure information sharing environment, [containing] standard rule sets and alert scenarios that the states could apply to their own Program Integrity or payment systems.”
Stultz says states are eager for assistance and that rather than a financial incentive, what they really need is leadership. CMS could establish a “push” environment, he suggests, and the states would readily accept what they receive. “If CMS built out an environment for sharing those best practices,” he says, “it wouldn’t take much effort on the state side to add or enhance existing rule sets or payment edits.”
More standardization across state lines would also make it more difficult for fraudsters to escape detection by shutting down and setting up one state away.
Miller suggests states could help their own causes by reviewing the data they gather, making sure they are collecting it in a standard way and also requiring users to fill in all required data fields to do meaningful analysis.
“Once states have data that can be pooled together, they can use some basic tools, including Excel, to start to identify outliers and develop a common understanding of fraud indicators within a given program,” she says.
Much of the identification work will initially take place post-payment, says GDIT’s Attia, but that’s OK. “You can’t build an effective pre-payment program without an effective post-payment program,” she says. “Pre-payment and post-payment aren’t separate. They have to be built together to create an effective fraud, waste and abuse program where you learn from what has occurred in the past in order to prevent it from occurring again in the future.”