3 ways generative AI can level the field with health care fraudsters

Over the past year, generative AI has captured the public’s imagination. We have become familiar with concepts like the Singularity and the Paper Clip thought experiment. Fortunately, generative AI has not caused the world to be overrun by paper clips or surpassed human intelligence. However, it does have the potential to cause harm in various ways. Well-intentioned individuals can unintentionally harm others by lacking transparency, invading privacy, or introducing bias. In a previous article, we discussed how generative AI can be used to create false medical documentation. This can occur in clinical notes, medical imaging, and other healthcare-related areas. The good news is that these capabilities are not entirely new to healthcare payers. Bad actors can use generative AI to increase the efficiency and sophistication of existing fraudulent schemes. Health care payers need to respond to these challenges. Special investigation units (SIU) and payment integrity (PI) professionals are crucial in countering these threats. Instead of starting from scratch, generative AI can enhance the efficiency of these teams. Here are three examples of how generative AI can be used by SIU and PI teams. 1. A digital assistant for claims: Imagine having a digital assistant that can access claims data and allow non-technical users to retrieve the data they need. Instead of going back and forth with analysts, investigators can simply type in plain English what data they require. The digital assistant can even provide suggestions and alerts to ensure accuracy. This would save time and streamline the data retrieval process. 2. Synthetic data generation: Training health care fraud models can be challenging due to the lack of fraudulent examples. One way to address this is by creating synthetic data. Generative AI excels in synthetic data generation because it can create data that looks similar to fraudulent examples. This can help improve the accuracy of fraud detection models. 3. Case management assistant: Case management is an essential part of fraud investigation. A digital assistant trained for investigations can assist investigators and managers in their day-to-day tasks. It can provide case summaries, answer specific questions, recommend next steps, and mine case notes for additional information. This would enhance the efficiency of case management and support investigators. Generative AI is a powerful tool in the fight against fraud. It can help payers detect and prevent fraudulent activities more effectively. As technology continues to evolve, we can expect generative AI to play an even larger role in fraud prevention. Now is the time to leverage generative AI in the battle against fraud.



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