Healthcare Fraud: Analytics — The radiation badge for healthcare FWA
By Rodger Smith
While it has been a problem since the beginning of modern healthcare, in the past few years public focus on fraud, waste and abuse (FWA) in healthcare has grown substantially, as has the amount of money lost in the U.S. healthcare system as a result. Recent testimony to Congress by Ann Maxwell, assistant inspector general for evaluation and inspections at the Office of Inspector General (OIG), U.S. Department of Health and Human Services (HHS), reported “the estimated improper payments for Medicare and Medicaid to be approximately $88.8 billion” in FY2015.
Think of all the healthcare that could be purchased for $88 billion. Then consider that while the Centers for Medicare and Medicaid Services (CMS) is the country’s single largest health payer, it’s still just one. That $88.8 billion figure doesn’t take into account all of the dollars lost by private payers and their clients.
What makes it particularly frustrating (and attractive to those who do it intentionally) is that FWA can be very difficult to detect, but very impactful. Think of it like a small radiation leak.
With radiation there are no obvious signs; often there is nothing to see, hear, taste, touch or smell. But if it’s not found and stopped, over time the effects can be deadly. That’s why employees in facilities that handle radioactive material wear badges that constantly check and display cumulative radiation exposure. These badges can quickly uncover the presence of excessive radiation, either now or in the recent past, to determine whether there is an issue with a nuclear reactor or if there is a problem with equipment using radiation (such as X-ray machines).
The badges help people avoid the harmful effects of excessive radiation.
The healthcare FWA equivalent to the radioactive detecting badge is analytics. An advanced data analytics package can constantly monitor expenditures for healthcare services, using large volumes of data drawn from a multitude of sources, and detect subtle patterns or anomalies indicating possible FWA that would not be obvious to a human using manual means of data inspection. At the same time, advanced data analytics can also recognize certain mitigating events and reduce the frequency of false positives so health payers can deploy their resources most effectively.
The Many Faces of FWA
FWA runs the gamut, from honest mistakes that result in one-off overpayments to highly sophisticated criminal enterprises stealing millions of dollars. Payers must have strategies to address each of these. Advanced data analytics that incorporate diverse data sources are a key component of modern approaches and give payers a needed edge.
Typically, payers hire experts and build rules and processes around known and suspected issues. There are several problems with this approach.
First, the schemes and errors change frequently. Physicians, practice managers and medical billers do not receive billing training in school. Similarly, codes and coding rules change as new services come to market and treatment protocols change. The result is varied and changing errors. Meanwhile, sophisticated players know that the industry is watching and go to great lengths to obscure what they’re doing. The system’s inherent structure of trust enables both simple billing errors and illicit actors to hide in the shadows of the murky deep as overpayments quietly siphon money away from legitimate care.
Of course, fraud involves a misrepresentation of some key fact or event. When repeated misrepresentations are made, they create patterns that can be detected when compared to legitimate claims. Similarly, erroneous claims do not look exactly like valid ones, even within legitimate clinical variations. Advanced analytics that digest many different data sources give payers the means to look at benchmark patterns and results, and identify claims and patterns of billing sufficiently different to merit review. The best analytics are also adept at eliminating false positives so provider audit groups and special investigation units can focus their efforts where they are most likely to yield results.
Expanding Data Sources
Traditional analysis has primarily employed claims data, since it already includes substantial detail. However, claims data can be incorrect or paint a misleading picture because it is incomplete.
For example, suppose claims data shows a general surgeon in upstate New York is filing claims for more complex procedures than other surgeons in her region. A simple analysis might flag that provider. A different conclusion is reached, however, if by incorporating credentialing and geographical data into the analytics, the payer discovers she is the only hand surgeon in a 100-mile radius. Since she receives cases that are typically more complex than other surgeons, the higher intensity of claims makes sense.
Similarly, if a provider wants to prescribe a high-cost drug to a patient in a low-income area to make a substantially higher-than-appropriate profit, he or she may waive the member’s cost share. The provider’s actions undermine the incentive for using the alternative drug and lead to a substantial inappropriate payment. By only using claims data, this subterfuge may go undiscovered. But by analyzing demographic, geographic and other data about the member, the payer will realize it is virtually impossible for any member treated by that provider to pay the cost share, highlighting possible improper activity.
Another way payers can use advanced analytics to uncover FWA is by analyzing links between multiple providers. After all, if one nuclear reactor is having a radiation issue, it’s prudent to check all the others that were manufactured at that time by the same company.
If Provider A is involved in improper billing, it is not uncommon for other providers with which they associate to also be engaged in bad behavior. Thus, many payers work to analyze connected providers. Information on corporate ownership, billing and management companies, social media interactions of physicians and staff can reveal whether other physicians, pharmacies, radiology centers, home infusion agencies, etc., are engaged in a broader pattern of referral and collusion.
Rather than relying on the current or known state, advanced analytics can look at patterns and behaviors that vary from industry benchmarks, or Office of the Inspector General standards, or even what other providers in the payer’s network are doing. The key is to build innovative algorithms and data models around known issues, using as many data sources as possible, and train them with known patterns and issues.
Human Expertise Still Required
No matter how advanced analytics are, however, FWA detection also requires experts who understand how to work through the analytic output. Vital to this process are nurses who can see what others miss in medical records, former law enforcement officers who understand criminal behavior, claims adjustors who can see how bills twist CPT and HCPCs codes to their advantage, and actuaries and others who can look at mountains of statistics and see things that don’t look right. The insight offered by these individuals must then be fed back into the analytics to reduce false positives further.
One other factor is worth mentioning. Given the sums and effort involved for post-payment audits and reviews, it is critical to detect and address as many improper claims as possible before payment. Going after an erroneous payment due to a coding error months after reimbursement is expensive for payers, and creates a great deal of abrasion with providers. The more payers can avoid “pay and chase” scenarios, the better it is for all involved.
Reducing the Cost of FWA
As the OIG testimony points out, healthcare FWA is a large problem in the United States. Ensuring that the trillions of dollars being invested in healthcare are spent properly is a critical step toward making healthcare truly affordable for all.
To keep up with the challenges of detecting (and stopping) FWA, payers need to use all the data and analytics tools at their disposal to meet the threat. By thinking creatively, mining all available data sources with advanced analytic tools, and involving experts with specialized knowledge who can recognize even the subtlest anomalies, they can significantly reduce the impact of FWA on the healthcare industry. And keep us all safe from the life-threatening effect of radiation exposure.
Rodger Smith is the senior vice president for payment integrity at SCIO Health Analytics, an organization dedicated to using healthcare analytics to improve clinical outcomes, operational performance and business results. He can be reached at firstname.lastname@example.org.