Machine Learning

Bottom line: The federal rule that would have scrubbed medical debt from credit reports and barred lenders from using it in credit decisions was vacated by a federal court on July 11, 2025. That means the pre-existing national framework (FDCPA + FCRA + IRS 501(r) for nonprofit hospitals) is back to being the operative baseline—layered with a fast-moving patchwork of new state laws that (in some places) ban reporting, cap interest, and even prohibit selling medical debt to third-party buyers. If you collect or sell healthcare receivables, you’ll want to recalibrate policies now to the states you operate in, tighten 501(r) compliance, and build controls for “surprise billing” and dispute resolution.

The relationship between healthcare providers and insurance payers is at a breaking point. What was once a shared mission to deliver quality patient...

PayJourney™ is the healthcare industry’s first end-to-end concierge-service designed to improve the patient and provider financial experience – all...

In the rapidly evolving world of healthcare revenue cycle management (RCM), technology plays an essential role in improving efficiency, reducing errors, and enhancing customer interactions. Two of the most transformative technologies making an impact are Artificial Intelligence (AI) and Machine Learning (ML). These innovations are helping streamline processes, predict outcomes, and enable data-driven decision-making. But understanding the differences between AI, ML, and another related technology, Robotic Process Automation (RPA), can be a challenge. At Frost-Arnett, we are actively utilizing these technologies to improve our operations and customer service. In this blog post, we’ll break down these concepts and explain how they fit into the healthcare RCM landscape.