Did you know that out of an estimated $3.6 trillion expended annually on healthcare in the U.S., nearly 10% is lost to fraud? That’s where innovative solutions like Artificial Intelligence (AI) step in. Discussing “5 Real-World Examples of AI Detecting Fraud in Healthcare,” this blog post will survey just how AI is revolutionizing fraud prevention.
Background
Healthcare fraud includes a range of deceptive practice, from double billing and phantom billing to upcoding and unbundling. These fraudulent activities threaten to undermine not only the financial viability of healthcare systems but the quality of care, and most importantly, patient’s trust. Fraud prevention, until recently, often involved laborious manual detection, reactive solutions, and high false-positive rates. The game-changer? Artificial Intelligence (AI).
AI Solutions
AI is rewriting the rules of healthcare fraud prevention by fighting fire with fire. Learning from past data patterns and implementing predictive algorithms, AI carries the potential to anticipate fraudulent activities before they occur
Let’s delve into the “5 Real-World Examples of AI Detecting Fraud in Healthcare.”
1. Jvion’s Cognitive Machine:
Specializing in predictive analytics, Jvion’s AI solution was utilized by Shared Health, Tennessee’s largest Medicaid health plan, to identify high-risk claims. Their cognitive machine software identified significant patterns of fraud saving them nearly 50% in investigation time.
2. Change Healthcare’s AI:
Implemented across various healthcare organizations, this AI solution predicts future fraudulent activities by learning from past billing patterns. An example was when it identified the trend of repetitive billing from certain geographical areas. Investigations confirmed the trend had a high probability of being fraudulent.
3. Optum:
Optum’s Advanced Analytics employs AI in preventing fraud in healthcare payers’ space, looking for anomalies in patient claims. Their robust software feeds off historical data, accurately pinpointing fraud based on pattern recognition.
4. IBM Watson:
The IBM’s Watson supercomputer, when paired with AI, can sift through vast amounts of patient data. It was able to detect specific cases in which upcoding was rampant, thereby preventing additional costs.
5. FraudScope AI:
Developed by researchers from Georgia Tech, FraudScope AI focuses on identifying fraudulent patterns and anomalies in claims data. A case in point is spotting suspicious patterns like the trend of unusually high anesthesia billings.
Future Trends
AI is changing the dynamics of fraud detection. In the future, AI is likely to further improve in predictive accuracy, minimize false-positives, and significantly expedite detection. This means healthcare professionals will have better, quicker tools to outwit increasingly complex fraudulent activities.
Actionable Takeaways
Healthcare professionals need to understand AI’s role in fraud prevention and invest in AI tools. Keep abreast of new developments, constantly train your team, and understand that AI doesn’t eliminate the need for human judgement but rather enhances it.
Rounding out the “5 Real-World Examples of AI Detecting Fraud in Healthcare” conversation, it’s clear that AI has opened up brave new possibilities in combating healthcare fraud. It’s time to harness this powerful tool and arm our healthcare system against fraud. Ready to learn more about leveraging AI in Healthcare? Get in touch with our team today.
