CMS launched a competition to identify AI and machine learning solutions that detect anomalies and trends in Medicare Fee-for-Service claims and translate them into fraud indicators. The competition prioritizes innovative, data-driven approaches and explainable AI/ML capable of analyzing large datasets to uncover unusual patterns that may signal fraud. The program emphasizes understanding underlying factors behind anomalies to develop evidence-based indicators useful for proactively flagging schemes and improving program integrity. Solutions should focus on broader patterns and systemic vulnerabilities rather than only individual providers. The competition runs in two phases, with proposals due Sept. 19 and ten finalists advancing to receive SAF LDS data access.
CMS announced the start of the "Crushing Fraud Chili Cook-Off Competition" on Aug. 19, calling it "a market-based research challenge" to identify emerging technologies that can "detect anomalies and trends in Medicare Fee-for-Service (FFS) claims data that can be translated into novel indicators of fraud." The agency said it is "prioritizing the use of innovative, data-driven approaches, including explainable AI/ML" that can analyze large datasets to "uncover unusual patterns, anomalies, or trends that may signal fraudulent activity."
"However, pattern detection alone is not sufficient to determine, let alone prove, fraudulent behavior, especially in legal or enforcement contexts," CMS added in its competition overview. "That's why it is critical to understand the underlying factors driving these anomalies. This deeper insight enables the development of clear, evidence-based indicators of fraud. These indicators can then be used to proactively flag similar fraud schemes across Medicare claims data and enhance the efficiency of program integrity efforts."
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