US Patent Application 17828945. IDENTIFICATION OF FRAUDULENT HEALTHCARE PROVIDERS THROUGH MULTIPRONGED AI MODELING simplified abstract

IDENTIFICATION OF FRAUDULENT HEALTHCARE PROVIDERS THROUGH MULTIPRONGED AI MODELING

Organization Name

Mastercard International Incorporated

Inventor(s)

Athena Stacy-nieto of Somerville MA (US)

Alok Singh of Gurgaon (IN)

Nitish Kumar of Jamshedpur (IN)

Kaye Kirschner of New York NY (US)

Mahdi Jadaliha of Wildwood MO (US)

Yuanzheng Du of San Francisco CA (US)

Timothy Mcbride of Saratoga Springs UT (US)

IDENTIFICATION OF FRAUDULENT HEALTHCARE PROVIDERS THROUGH MULTIPRONGED AI MODELING - A simplified explanation of the abstract

This abstract first appeared for US patent application 17828945 titled 'IDENTIFICATION OF FRAUDULENT HEALTHCARE PROVIDERS THROUGH MULTIPRONGED AI MODELING

Simplified Explanation

The patent application describes a system and method for identifying fraudulent healthcare providers using raw claims data.

• The system receives raw claims data from various sources, which includes claims associated with a selected healthcare provider.
• The claims data consists of claim lines, which are analyzed using multiple models.
• The first model calculates a score for the healthcare provider based on the raw claims data.
• The second model also calculates a score for the healthcare provider using the same raw claims data.
• The third model determines a third score for the healthcare provider based on the raw claims data.
• Finally, a final provider-level risk score is determined for the healthcare provider by combining the scores from the first, second, and third models.

Original Abstract Submitted

A system and computer-implemented method for identifying fraudulent healthcare providers receives raw claims data from one or more data sources. The raw claims data includes claims associated with a selected healthcare provider. Each of the claims includes one or more claim lines. A first model is executed on the raw claims data. The first model determines a first score for the healthcare provider. A second model is executed on the raw claims data. The second model determines a second score for the healthcare provider. In addition, a third model is executed on the raw claims data. The third model determines a third score for the healthcare provider. A final provider-level risk score is determined for the healthcare provider based on the first, second, and third scores.