Fraud, waste, and abuse cost healthcare organizations billions of dollars each year. AI-powered fraud detection and prevention tools can help healthcare organizations identify suspicious activity and prevent fraudulent claims from being paid. By leveraging machine learning algorithms, healthcare organizations can identify patterns of fraud and adjust their fraud detection strategies accordingly. This will result in reduced costs and improved quality of care, as resources can be focused on legitimate healthcare needs.

How GenHealth’s AI Addresses Fraud

Our V2 AI model has medical event data (including claims and clinical events) from patients, plans and most importantly providers across the US. We tag each of those providers and organizations with the metadata including whether or not their licenses were revoked for fraudulent activity. Using that approach our model is able to learn the patterns of individuals which have committed Medicare or other medical fraud in the past. Then when we apply this model to new medical event data including new providers our model includes a predicted event token which denotes “some probability of fraud.” By aggregating those events at the individual, provider, or organization level, our model is able to rank order the likelihood of fraud for those various levels. Your team can then take action on those events to investigate further.

The Future of Fraud Detection with Generative AI

The use of AI in fraud detection and prevention will continue to evolve, enabling healthcare organizations to more effectively identify and prevent fraudulent activity. By leveraging machine learning algorithms and real-time data, health plans and providers will be able to stay ahead of emerging threats and reduce the overall impact of fraud, waste, and abuse in the healthcare system.