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When the novel The Hunt for Red October was first published, it did more than elevate Tom Clancy from obscure real estate agent to international best-selling author. It also introduced readers to the exciting, elusive world of submarine warfare, where the ability to identify patterns and spot anomalies is often the difference between returning to port safely and ending up at the bottom of the ocean.

Healthcare fraud, waste, and abuse

While the hunt for healthcare fraud, waste and abuse (FWA) may not have the drama of torpedoes in the water or emergency dives, it definitely shares some similarities to submarine warfare – including the continual need to learn and adjust as well as the potential for huge impacts on people’s health and pocketbooks.

Fraud, waste, and abuse 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 they 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 rules change frequently, and physicians do not receive billing training in school, leading to errors.  Also, the sophisticated players know that industry is watching and go to great lengths to obscure what they’re doing.

The system’s inherent structure of trust enables both simple billings 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 big data 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.

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. The story is different 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 copay, highlighting possible improper activity.

Another way payers can use advanced analytics to uncover FWA is by analyzing links between multiple providers. After all, why settle for sinking one enemy ship when you can potentially cripple the whole fleet?

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 will 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

No matter how advanced analytics are, however, FWA detection also requires experts who understand how to analyze 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 that 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 avoid further false positives.

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 will be for all involved.

Hunting in a large ocean for a single runaway nuclear submarine built specifically to be undetectable makes for thrilling reading. But it also offers a few lessons.

To keep up with the challenges of FWA, payers need to use all the data and analytics tools at their disposal to meet the growing threat. By thinking creatively, mining all available data sources with advanced analytic tools, and involving experts with specialized knowledge who can find the hidden clues, they can significantly reduce the impact of FWA on the healthcare industry.

This article was originally published on revcycleintelligence.com and can be viewed in full here

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