In July of 2022, the Department of Justice announced criminal charges related to “ghost patients” for more than $1.2 billion in alleged fraudulent telemedicine, cardiovascular and cancer genetic testing, and durable medical equipment schemes. Additionally, the Centers for Medicare & Medicaid Services, and Center for Program Integrity announced it took administrative actions against providers involved in similar schemes.
These schemes account for more than $1 billion of the total alleged intended losses associated with today’s enforcement action. Prescription fraud comes at an astronomical cost to physicians, hospitals, insurers, and taxpayers. Proper analysis can provide these insights and discoveries, however often times too late. With A.I. predictive analytics these schemes can be curbed before they are detrimental.
Using NEMESIS, analysts can see claim details with trends and scores to identify whether this claim has a high probability of being a medical ghost patient fraudulent case. Using the historical data of medical insurance claims, analysts can easily build a predictive model to foresee the next move of fraudsters.
This case requires the use of historical claim data, including geo-location information, time parameters, policy information, claim type, claim amount, insured person description, etc.