Back in 2015, industry pundits speculated that the traditional fee-for-service (FFS) fee schedule would, at last, go extinct. At that time, the US Department of Health and Human Services had set a timeline to move 30 percent of Medicare payments away from FFS by 2016 and 50 percent by 2018. What was supposed to replace the legacy FFS fee schedule? Bundled-payment and/or value-based contract models, such as accountable care organizations, patient-centered medical homes and/or managed care organizations.
Since then, all these payment modalities have been instituted in one form or fashion across Medicare and across Medicaid states; however, these “value based” models have not replaced the FFS fee schedule. In most cases, providers are still paid according to the FFS fee schedule, and then receive bonuses on top of the FFS payment for meeting certain quality standards.
While it’s possible that these new payment models can lead to higher quality of care and improved experiences for patients and providers, a very important question remains.
Fraud numbers are on the rise because fraud rings are not only getting more effective at what they do, but because it’s easy for them to find soft targets across multiple government programs simultaneously.
Learn about how next-generation analytic tools from SAS cut across data and program silos and empower investigators to go on the offensive with fraud operators – without disrupting the efficient and timely delivery of benefits, services or tax refunds.
What is being done to curb the insidious Medicaid and benefit fraud, waste and abuse problem in the US?
The National Health Care Anti-Fraud Association estimates conservatively that health care fraud equals 3 percent of the nation's total health care expenditures. Other estimates, like the FBI, range as high as 10 percent of annual health care expenditures. So how much money are we really talking about when using words like insidious to define the problem?
In 2016, the US spent $3.4 trillion on health care expenditures. According to the percentages above, fraud, waste and abuse would range from $102 billion to $340 billion.
As you might imagine, Medicaid spending has been on the rise, totaling $363.8 billion, or roughly 10 percent of the total health care expenditures. Medicaid fraud, waste, and abuse has also remained on the rise, up from 6 percent in 2014 to 10 percent in 2017, or $36.7 billion in improper payments in 2017 alone.
Wait, it gets worse. Experts are projecting that health care expenditures will soar as high as $5.5 trillion by 2025. That would mean health care fraud could range between $165 billion and $550 billion, and Medicaid fraud could be as high as $60 billion if we don’t take a hard look at how to solve this problem.
So will these new payment models really help prevent Medicaid fraud and improper payments from occurring at the levels we are projecting for the year 2025?
Knowing fraud as we do at SAS, we think not – fraudsters will simply adapt to take advantage of the new types of incentives built into these “new” payment modalities. The challenge here for both government-sponsored programs and commercial health care plans (which now underwrite the majority of Medicaid plans across the country through managed care organizations) will be staying one step ahead of the traditional “FFS Bad Guy.”
The challenge here for both government-sponsored programs and commercial health care plans (which now underwrite the majority of Medicaid plans across the country through Managed Care (MCO)) will be staying one step ahead of the traditional “FFS Bad Guy.”
The new face of health care cost containment and payment integrity challenges
In the FFS realm of Medicaid (which is still 50 percent of Medicaid expenditures in most states), “creative billers” seemingly add extra services or supplies that were never rendered to their bills, up-code to show a higher level of service than was performed, and/or order unnecessary procedures. In the new world of value-based payments and bonuses, unscrupulous health care providers will falsify documentation in new ways to show that:
- They had more patient encounters than they actually had.
- Their patients showed better-quality outcomes than they actually had.
- Their mix of patients is higher-risk than is normal, which would typically entitle them to higher reimbursements.
Unfortunately, corrupt people and organizations will still be able to manipulate the facts in claims for reimbursement – and in ways that are difficult to detect using traditional IT resources and processes. The question is, how should government agencies (like CMS and Medicaid state agencies) and commercial plans prepare so they can detect and prevent Medicaid fraud and improper payments? What new solutions will they need to build a robust Medicaid or other benefit fraud and abuse detection system?
Responding to threats with data management and analysis
Medicaid agencies and their MCOs will need a data management infrastructure that provides access to data across programs, products and channels, as well as integrated analytical solutions to detect Medicaid fraud and improper payments hidden in this data. Despite the conventional wisdom, this does not require a database overhaul or a massive central data warehouse. Rather, states should invest in integrated analytical solutions that offer a data integration layer that can source from databases around the organization, business partner organizations, and external public or purchased data. These integrated analytical solutions should have the capability of performing data quality functions that support entity resolution, as unscrupulous providers and suppliers often intentionally provide inaccurate, incomplete or inconsistent information to prevent records matching across disparate systems.
To stay competitive, an analytical Medicaid and benefit fraud and abuse detection system must include the right data management, integration and quality infrastructure, plus a robust business analytics foundation. After all, success will be defined by one’s ability to gain insights from the data and reduce fraud and improper payments to the Medicaid program. To successfully manage and analyze vast amounts of detailed data, this infrastructure must be able to:
- Process large volumes of data within a narrow time window.
- Handle the wide variety of data involved, including tables, documents, email, web streams, videos and more.
- Manage the velocity of data, which is growing rapidly.
These data management capabilities are not only critical to everyday business functions, but also to detecting and preventing Medicaid and benefit fraud and improper payments. The volume, variety and velocity of data will keep growing, increasing the gap between relevant data and big data. And for organizations without these capabilities, an uncertain amount of information overload will undoubtedly make it even more difficult than it was in the legacy FFS environment to quickly detect fraud and take action.
To prevent increasingly insidious Medicaid and benefit fraud and improper payments from reaching $500 billion annually by 2025, Medicaid agencies and their commercial counterparts must either develop their own detection systems or partner with an organization with expertise in this area. Only an integrated data management and analytics system can provide the necessary capabilities to ensure success in the fight against Medicaid and benefit fraud and improper payments.
- Detect and prevent banking application fraudSince credit fraud often starts with a falsified application, it makes sense to have analytics-driven tools in place to detect fraud from the earliest point and across the life of the account.
- Stop contract and procurement fraudFraud affects an estimated 30 percent of organizations' procurement processes. Beyond business rules and anomaly detection, analytics can detect and prevent fraud and preserve the integrity of the procurement process.
- Rethink customer due diligenceTo streamline compliance and protect against financial and regulatory risk, re-examine your customer due diligence processes and technologies regularly. With new analytical tools, you can monitor customer transactions or personal information in real time, and accurately segment customers by the risk they represent.