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Smarter Solutions

Smarter Solutions: AI’s Role in Combating Waste and Abuse

When we talk about challenges in healthcare, fraud, waste, and abuse (FWA) often fly under the radar—but they shouldn’t. It is estimated that fraud, waste, and abuse (FWA) account for approximately 3% of overall healthcare spending. However, some government and law enforcement sources suggest that these losses could climb as high as 10% of the total annual healthcare budget.

These losses strain the entire system, increasing costs for everyone while siphoning resources from patient care.

Fortunately, Artificial Intelligence (AI) is stepping in to change the narrative. With its ability to analyze data, detect patterns, and flag potential issues, AI is becoming a key player in combating FWA.

Whether you’re a payer, provider, or part of a health system, understanding how AI works—and how it can help—is more important than ever.

What Is Fraud, Waste, and Abuse in Healthcare?

To put it simply:

  • Fraud involves intentional deception, such as billing for services that were never provided.
  • Waste refers to unnecessary spending, often caused by overuse or inefficiencies, like redundant tests or procedures.
  • Abuse falls into a gray area—it often involves unintentional misuse of billing codes or services that don’t align with best practices.

Here’s an example: A provider bills for a more expensive service than what was performed (a practice called “upcoding”), or a hospital orders unnecessary tests, racking up costs without improving outcomes. Together, FWA costs the healthcare system about 3-10% of total healthcare spending.

AI to the Rescue: Smarter Solutions for Complex Problems

AI’s ability to process vast amounts of data quickly and accurately makes it a natural fit for tackling FWA. Here’s how it works:

AI can analyze years of claims data to identify patterns that might signal fraud or waste. For example:

  • Providers who submit claims at unusually high frequencies compared to their peers.
  • Duplicate billing, where the same service is charged multiple times.

In a 2024 study, AI models used by healthcare payers detected upcoding in 25% more cases than traditional methods, helping recover millions of dollars.

NLP technology enables AI to review unstructured data, like doctor’s notes and medical records, to spot inconsistencies. For instance, if a patient’s medical record shows a routine check-up but the claim includes a higher-cost procedure, the system flags it for review.

A pilot program highlighted in Seton Hall University research found that NLP tools reduced errors in medical coding by 30%, saving both time and money for providers and insurers.

Unlike manual audits that happen after the fact, AI can monitor claims as they’re submitted. By comparing incoming claims to historical data, AI systems can instantly flag anything unusual.

For example, during the rollout of an AI-powered monitoring system, one payer discovered $10 million in fraudulent claims in just six months—claims that would have taken much longer to catch manually.

AI doesn’t just find anomalies; it ranks them by risk level. This allows healthcare organizations to focus their resources on the most pressing issues.

In a case study by Forvis Mazars, an AI system applied risk scores to flagged claims, reducing investigation times by 40% and increasing recovery rates.

One of the most time-consuming parts of managing FWA is the claims review process. AI automates this, cross-referencing claims with coding guidelines and regulatory standards. The result? Faster claim approvals for legitimate cases and fewer bottlenecks for providers.

The Bigger Picture: Why This Matters for Healthcare Organizations

FWA isn’t just about numbers—it’s about trust. Every dollar lost to fraud or waste is a dollar that could be used to:

  • Improve patient care.

  • Invest in new technology.

  • Lower premiums and out-of-pocket costs.

AI helps healthcare organizations safeguard their resources while improving efficiency. And the impact is tangible: Payers and providers using AI have reported a 15-25% reduction in wasteful spending, according to industry analyses.

Challenges to Keep in Mind

As promising as AI is, it’s not without hurdles:

  • Data Privacy and Security: AI systems require access to sensitive information, so compliance with regulations like HIPAA is critical.

  • Integration with Legacy Systems: Many hospitals and clinics still rely on older technology, which can make it tricky to roll out AI solutions.

  • Algorithm Bias: AI systems are only as good as the data they’re trained on. If the data is incomplete or biased, it can lead to inaccurate results.

The key is to implement AI thoughtfully, ensuring it enhances existing processes without compromising patient trust or privacy.

Final Thoughts: AI as a Game-Changer

AI is no longer a futuristic concept—it’s here, and it’s already making a difference in healthcare. By spotting patterns, automating tedious tasks, and prioritizing high-risk cases, AI offers a smarter, more efficient way to combat fraud, waste, and abuse.

For healthcare organizations, this isn’t just about saving money—it’s about building a system that works better for everyone. Whether you’re part of a payer network, a provider group, or a hospital system, now is the time to explore how AI can help you protect your resources and focus on what matters most: delivering excellent care.

References

  1. National Health Care Anti-Fraud Association. The challenge of health care fraud. NHCAA. Accessed January 9, 2025. https://www.nhcaa.org/tools-insights/about-health-care-fraud/the-challenge-of-health-care-fraud/

  2. Forvis Mazars. AI strategies to help combat fraud, waste, and abuse in healthcare. Forvis Mazars. Published December 2024. Accessed January 9, 2025. https://www.forvismazars.us/forsights/2024/12/ai-strategies-to-help-combat-fraud-waste-abuse-in-healthcare?mkt_tok=OTMyLUJBQy03MDAAAAGX5noQuR8dUtcSDcA55QeQ6-l929QGcSTsO-8zgeqvHpn4xeyCDb4Va3N2MzVlTpTn7AupNZduiQa4TcET92HVa9-GcDplsPStWxJyp8Wm

  3. Sadeghi K. A review of healthcare fraud and abuse in the United States. Seton Hall University Student Scholarship. Accessed January 9, 2025. https://scholarship.shu.edu/cgi/viewcontent.cgi?article=2491&context=student_scholarship

  4. Baker E. Combating healthcare fraud: The role of AI in reducing waste and abuse. Seton Hall University Student Scholarship. Accessed January 9, 2025. https://scholarship.shu.edu/cgi/viewcontent.cgi?article=2640&context=student_scholarship

  5. Berwick DM, Hackbarth AD. Eliminating waste in US healthcare. JAMA. 2012;307(14):1513-1516. doi:10.1001/jama.2012.362

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