Healthcare compliance just got a lot more complicated.
We’re not dealing with traditional whistleblowers anymore – the ones who needed inside access to spot problems. Today’s relators are running sophisticated artificial intelligence (AI) algorithms against public datasets, flagging statistical anomalies that could signal fraud.
The U.S. Department of Justice (DOJ) logged 979 qui tam actions in the 2024 fiscal year (FY), the second-highest number of False Claims Act (FCA) cases in history. Many started not with insider tips, but with mathematical outliers.
This changes everything about how we think about compliance. If someone can train an AI model on decades of FCA cases and point it at your billing data, you need to be running the same analysis first. Advanced predictive analytics systems now offer healthcare organizations the same types of analytical tools being used to hunt for fraud, allowing them to find and fix problems before they become investigations.
The False Claims Act made sense when catching fraud required human witnesses with direct knowledge. But that world doesn’t exist anymore. Now you’ve got relators who work more like data scientists than traditional informants. They’re pulling Medicare utilization data, building predictive models, and flagging providers whose patterns look unusual compared to their peers.
Here’s what they’re working with:
appeal involving a $1. 6 billion judgment against Johnson & Johnson unit Janssen as well as the constitutionality of the FCA's "qui tam" whistleblower provisions, arguing that the act's lawfuln...