Fraud Analytics

Data-­‐driven/Artificial Intelligence for Fraud and Abuse Prevention

Medicare fraud and abuse is a serious problem requiring attention. Although there is no precise measure of  health care fraud and the majority of health care providers are honest and well-­-intentioned, a minority of providers who are intent on abusing the system can cost taxpayers billions of dollars and put beneficiaries’ health and welfare at risk. The impact of these losses and risks is magnified by the growing number of people served by Medicare and the increased strain on Federal and state budgets.

Government audits recently revealed huge uptick in fraud and abuse in Medicare coming from HER mis-­- use. Apparently, many HER vendors have built in coding optimization tools, which can be misused to increase reimbursement for various services.

Model simulation with scenario analysis can be used to predict fraudulent behavior. These models can quantify the impact of fraud and abuse on the system based on different policy models and help support policy change decisions. Big-­-data Analytics tools from xtLytics can be used to review large healthcare claims and billing information to target the following:

• Assess payment risk associated with each provider

• Over-­-utilization of services in very short-­-time windows

• Patients simultaneously enrolled in multiple states

• Geographic dispersion of patients and providers

• Patients traveling large distances for controlled substances

• Likelihood of certain types of billing errors

• Billing for “unlikely” services

• Pre-­-established code pair violation

• Up-­-coding claims to bill at higher rates