7/1/2024
Florida Atlantic: Boosting Medicare Fraud Detection
Medicare insurance fraud topped an estimated $100 billion in 2023, according to the National Health Care Anti-Fraud Association. However, this number is likely much higher due to the number of fraudulent claims that go undetected each year.
Traditional methods of detecting Medicare fraud are time consuming and tedious, with investigators manually inspecting thousands of claims. They have limited time to look for the specific patterns that indicate suspicious behaviors in patient and provider records, and there are not enough investigators to keep up with the various Medicare fraud schemes. Even with technology like machine learning, handling the staggeringly large volume of data remains a significant challenge.
New research from Florida Atlantic University’s College of Engineering and Computer Science addresses this challenge by using artificial intelligence to pinpoint fraudulent activity in the vast sea of Medicare data. And since identification of fraud is the first step in stopping it, this novel technique could conserve substantial resources for the Medicare system.
For the study, which was published in Journal of Big Data, researchers tested two Medicare datasets, Part B and Part D. Part B involves Medicare’s coverage of medical services like doctor’s visits, outpatient care and other medical services not covered under hospitalization. Part D relates to Medicare’s prescription drug benefit and covers medication costs. For both datasets, researchers used a potent data sampling technique and conducted experiments in five scenarios to improve results. The process quickly and successfully identified potentially fraudulent claims.
“Given the enormous financial implications of Medicare fraud, findings from this important study not only offer computational advantages but also significantly enhance the effectiveness of fraud detection systems,” said Stella Batalama, Ph.D., dean of the College of Engineering and Computer Science. “These methods, if properly applied to detect and stop Medicare insurance fraud, could substantially elevate the standard of health care service by reducing costs related to fraud.”
Medicare insurance fraud topped an estimated $100 billion in 2023, according to the National Health Care Anti-Fraud Association. However, this number is likely much higher due to the number of fraudulent claims that go undetected each year.
Traditional methods of detecting Medicare fraud are time consuming and tedious, with investigators manually inspecting thousands of claims. They have limited time to look for the specific patterns that indicate suspicious behaviors in patient and provider records, and there are not enough investigators to keep up with the various Medicare fraud schemes. Even with technology like machine learning, handling the staggeringly large volume of data remains a significant challenge.
New research from Florida Atlantic University’s College of Engineering and Computer Science addresses this challenge by using artificial intelligence to pinpoint fraudulent activity in the vast sea of Medicare data. And since identification of fraud is the first step in stopping it, this novel technique could conserve substantial resources for the Medicare system.
For the study, which was published in Journal of Big Data, researchers tested two Medicare datasets, Part B and Part D. Part B involves Medicare’s coverage of medical services like doctor’s visits, outpatient care and other medical services not covered under hospitalization. Part D relates to Medicare’s prescription drug benefit and covers medication costs. For both datasets, researchers used a potent data sampling technique and conducted experiments in five scenarios to improve results. The process quickly and successfully identified potentially fraudulent claims.
“Given the enormous financial implications of Medicare fraud, findings from this important study not only offer computational advantages but also significantly enhance the effectiveness of fraud detection systems,” said Stella Batalama, Ph.D., dean of the College of Engineering and Computer Science. “These methods, if properly applied to detect and stop Medicare insurance fraud, could substantially elevate the standard of health care service by reducing costs related to fraud.”
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