Between 3% and 10% of total U.S. healthcare spending each year amounts to fraud, according to the National Healthcare Anti-Fraud Association. Facing potential multimillion-dollar losses, insurers invest significant resources into weeding out fraud and protecting their financial interests.
Conducting a healthcare fraud investigation is a laborious process that can take weeks at the minimum. Most of that time is spent sifting through information from multiple sources, piecing it together like a jigsaw puzzle to reveal a complete picture of the provider-member relationship. Only a small proportion is dedicated to actual decision-making.
But it doesn’t have to be that way.
Dynamic problems vs. manual solutions
Healthcare fraud is a dynamic problem, with fraudsters constantly changing their tactics to evade detection. Yet many insurers are tackling this growing issue using only manual tools – multiple databases, pivot tables and Excel spreadsheets – which are difficult to adapt to new scenarios.
Unsurprisingly, this approach leaves insurers constantly playing catch-up with fraudsters, as investigators spend days manually looking into cases. In the current environment, some fraud is missed by insurers, while false positives are too frequent. Plus, when an insurance investigator does spot something fishy, they often lack the data to validate their findings.
Evidence suggests that the pandemic and the rise of telehealth led to an increase in insurance fraud – as stringent telehealth rules were relaxed overnight, enabling providers to bill insurers for an enormous range of medical services provided virtually.
So – in this increasingly digitized healthcare landscape – it’s clear that insurers must modernize their back office to protect against healthcare fraud. That’s where artificial intelligence (AI) and machine learning (ML) tools come in. These technologies enable insurers to create dynamic solutions to a dynamic problem. Here’s how.
How AI Helps
AI enables insurers to optimize employee time. Rather than spending tens of days on research, caseworkers can rely on AI to analyze relevant, contextualized data and generate alerts accordingly. This both increases the efficiency of insurance investigators and improves customer experience.
Consider the telehealth example. Imagine you’re an investigator working with rudimentary manual tools like pivot tables and Excel spreadsheets. You might spend hours assessing providers’ telehealth billing data before finding one that seems to be filing a suspiciously high number of claims. Bingo – you've spotted a fraudster! Not so fast. After embarking on a lengthy investigation, you find that this "discrepancy" can be attributed to the way the provider in question is coding a certain category of services. False alarm.
Though the insurance investigator in this scenario acted completely reasonably, they inadvertently wasted significant resources chasing this dead-end lead. If this investigator had been equipped with AI-powered analytical tools, the same billing data could have been processed and interpreted in minutes – rather than hours. What’s more, AI would’ve produced significantly fewer false alarms, enabling the investigator to dedicate their time to following up on genuine leads – which means even more time saved.
AI’s second superpower is uncovering trends or patterns that simply aren’t easily recognized – thereby revealing hidden fraud.
Let’s take the example of prescribing controlled substances via telehealth consultations. Rules governing this practice were significantly relaxed during COVID-19. As a result, this area of prescribing has become a fraud hotspot. This has both financial implications for insurers and potential public health ramifications – for example, if fraudulently prescribed opioids were to be sold on the streets.
AI is great at uncovering fraudulent prescriptions issued via telehealth. It can quickly spot suspicious prescribing patterns by aggregating all relevant data – including relationship analytics between members and providers. Has there been a spike in prescribing? Have these members been issued this prescription before? If not, why now?
What’s more, AI can analyze huge quantities of publicly available data from the internet – forums, social media, Google reviews and more. For instance, if a physician was at a conference or on vacation during a particular week, how could they possibly have completed several dozen telehealth consultations each day?
See Also: Can AI Solve Health Insurance Fraud?
Striking the right balance
This isn’t about replacing special investigations units with AI. Instead, it’s about enabling employees and AI tools to work together symbiotically.
There’s no doubt that AI can analyze data in a fraction of the time it takes a human analyst – and with a much higher degree of accuracy. However, healthcare is – and will always be – a people business. Complex, life changing decisions made each day by insurers on the behalf of members and providers will always need human input.
Let’s take the example of "fraud" perpetrated by healthcare providers. This falls broadly into two categories. The first category is composed of otherwise-well-meaning providers who might sometimes round up appointment times by 10 or 15 minutes and receive a higher insurance payout. The second category is made up of serious fraudsters, who consciously and consistently set out to defraud insurers.
With AI and human employees working in tandem, insurers can easily uncover both groups. But they can also retain the freedom to handle the issue with compassion – punishing genuine fraudsters, while educating providers who’ve committed minor abuse.
That’s why – without a hint of irony – it seems obvious that AI is exactly what’s needed to bring humanity back into healthcare fraud investigations.
