Theja Birur, Chief Technology Officer & Founder, 4L Data Intelligence
In 2020, the Department of Justice estimated that fraudulent, wasteful, and abusive (FWA) billing practices account for more than $100 billion of the nation’s healthcare expenditures.1 Today, the National Healthcare Anti-Fraud Association (NHCAA) conservatively estimates that healthcare FWA costs the nation about $68 billion annually, representing 3% of the nation’s $2.26 trillion in healthcare spending.2 FWA estimates from commercial health plans range as high as $230 billion annually, or 10% of total healthcare spending.3
This lost money is far from a concept or abstraction. Every dollar lost to fraudulent, wasteful or abusive billing hurts patients, honest providers, payors and governments. Third-party benefits providers often receive outsized blame for these costs, when in reality, fraud, waste, and abuse is extremely difficult to detect using conventional methods because providers submitting excessive or fraudulent billing claims are constantly changing their methods to avoid detection.
Fortunately, new advances in artificial intelligence (AI) technology provide our industry a clear path forward to lowering healthcare costs by reducing excessive or overbilling in a way that rewards good providers and returns more dollars to patient care. By helping healthcare payors detect and prevent fraudulent, wasteful and abusive billing practices in greater quantities and before payments are made, it is estimated that up to $1 trillion in fraudulent, wasteful, and abusive costs can be eliminated from U.S. healthcare by 2030. It’s time to stop blaming benefits providers for spiraling costs and start addressing the technology that powers their day-to-day healthcare claims editing, audit and review systems. Here are the key concepts to consider.
Static Claims Editing Systems Are Exploitable
Most healthcare benefit systems are based on a static, rules-based or use case-based technology that audit a very narrow set of criteria in determining whether a healthcare claim should be paid to the provider. While these systems do a good job of processing and paying billions of claims each year, their antiquated technology allows hundreds of billions of dollars in excessive bills or fraudulent bills to be paid. It’s not because the claims management companies don’t want to stop fraudulent and excessive billing, it’s because their technology can’t see the exploitation that’s occurring.
Technology Has To See Provider Behaviors, Relationships and Outliers
When cases are reviewed and adjudicated using traditional rules-based, use case-based and conventional AI methods, dynamic provider behaviors, relationships and outliers are hard to detect. You have to see a provider’s behavior around a claim and all claims and that provider’s relationships with other providers in order to detect fraudulent, wasteful and abusive billing at a significant level before claims are paid out. This means that this sophisticated, interdependent relationship between providers, a current claim form, historical claim forms, and all other providers in a network has to be able to be identified, analyzed and reported on in less than one second when a claim is submitted for payment.
The Promise of Artificial Intelligence
AI scares a lot of people, because it is hard to wrap your arms around what it is. Simply stated, one definition of artificial intelligence (AI) is technology that thinks and does what a human can do, but much faster. Even this simple description leaves out the benefit of unsupervised AI being able to identify an infinite number of ‘math problems’ that a human might not even know to look for in a data set.
Early AI, and much of the conventional AI used in healthcare FWA detection and payment integrity work today, is not much more than a really advanced Excel spreadsheet. Much of the conventional AI operates using structured machine learning. This means that a machine is trained to perform an algorithm or series of algorithms that take an “if-then” approach to analyzing data.
These structured machine-learning approaches are very helpful, but miss a lot of the dynamic trends, patterns and outliers that can be detected by advanced, unsupervised machine learning. To ‘see’ all of the FWA activity, you have to deploy unsupervised machine learning that identifies trends, patterns and outliers without being “told” specifically to go perform the task. This enables payors to see new fraud trends and patterns forming in near real-time that are indicators of behaviors and relationships that may be signs of excessive payments, over-payments, or even fraud. In short, you can see things and stop things from happening that you did not even know to tell your technology or staff to pursue.
The Reality Of Integr8 AI™ Technology In Stopping FWA
Integr8 AI technology is a new generation of artificial intelligence that is patented for the detection of operational threats. The first application of the technology is to enable healthcare payors – commercial health plans, TPAs, CMS programs, etc. – to take a dynamic, provider-centric approach to processing, auditing and paying healthcare claims. This technology has proven to increase FWA detection by 2X to 10X in initial commercial use, all because it can “see” FWA activity that conventional technology can’t see. And Integr8 AI can see it in a way that does not slow down the claims editing, review and payment process.
As one payment integrity executive said, “We need to be able to see the FWA activity that we all know is there. Current technology just doesn’t let us see the volume of FWA that next-generation Integr8 AI technology enables. The best part is that this type of technology operates on top of our current claims editing system. We don’t have to make new capital investments to make a big difference fast.”
The Bottom Line for Benefits Providers
The battle against spiraling healthcare costs has important implications for every stakeholder in the healthcare value chain, but third-party benefits providers stand to benefit the most when fraudulent, wasteful and abusive costs are controlled. Today, almost a third of all insured Americans receive their health coverage through a third-party provider. Removing fraudulent, wasteful, and abusive costs helps benefits providers lower the cost of benefits for customers and their employees, automate and streamline operations, and increase bottom-line profitability. Technology, like Integr8 AI, enables the benefits to be quantified quickly and recognized almost immediately – regardless of what claims editing and adjudication system is being used.
Now is the time for benefits providers to embrace sophisticated AI solutions for claims management, moving from a relatively static, claims-based model to a dynamic, provider-centric model. It’s time to take control in the fight against adaptable, malicious actors. That fight starts and ends with thinking about the technologies we have in place.
About Theja Birur
Theja Birur is the founder of 4L Data Intelligence and inventor of the patented Integr8 AI intelligence platform. She has 20 years of experience in analytics and artificial intelligence with most of that focused on solving payment and quality challenges for healthcare payers and public health agencies. Her career includes work in the government sector with the Ontario Ministry of Health in Canada, with IBM as a management consultant, and in the IBM Innovation Lab focused on analytics. Prior to founding 4L Data Intelligence, Theja worked as a consultant for the California State Compensation Insurance Fund where she was an Associate Director over Big Data and Data Warehouse functions.
