In the blink of an eye, AI and automation have transformed nearly every corner of revenue cycle management (RCM). And the pace shows no signs of slowing.
It’s like getting on a bullet train with no stops. Once you’re on board with AI, you’ve got to get accustomed to the breakneck pace. Miss it, and all you can do is stand on the platform watching everyone speed by.
Let’s remember, AI in RCM began as a set of rules-based tools to handle low-level, easily repeatable tasks. It’s come a long way since. Today, the most sophisticated versions of AI in RCM make important decisions, write letters and emails, and even gauge the emotions of inbound phone callers.
The question is no longer just about who’s using AI; it’s what they’re using it for and how well leaders are balancing the advanced capabilities with human expertise to strengthen revenue cycle performance.
All Gas, No Brakes
Clearly, we’ve moved well past the days when automation simply filled in fields or routed claims. The newest iteration can read, interpret, and act on information in ways that only humans have before.
While robotic process automation (RPA) still plays a key role in handling structured, repetitive work like billing and processing, more advanced versions of AI are expanding what’s possible. They can interpret unstructured data like emails, payer correspondence, and claim denial patterns, and turn it into actionable insights and next-step executions. In practical terms, this means AI can now draft its own denial appeal letters, analyze payer behavior patterns to guide submissions, and prioritize accounts based on the likelihood of payment recovery.
Instead of waiting for instructions, agentic AI systems can use judgment and make decisions within a set of boundaries to route tasks to the appropriate specialized AI agent.
Meduit’s virtual assistant, SARA (Supervised Autonomous Revenue Associate), handles 75 percent of incoming calls for Meduit’s bad debt clients and resolves 40 percent of those calls without human intervention. Without knowing the nature of an incoming call, SARA adapts in real time, deciphering caller intent and determining the best course of action. SARA can even measure caller emotion, so if there’s a frustrated patient on the other end, SARA will immediately transfer them to a live representative.
Finding the Right Balance
As AI becomes more capable, it’s allowing RCM professionals to operate at the top of their skill set. They no longer have to reach as far to pick up where AI leaves off. The handoff now happens closer to the high-value work, allowing RCM teams to focus on analysis, strategy, and forward-thinking rather than routine tasks.
Of course, as AI is involved in more areas of RCM operations, concerns about bias, data security, and governance will likely become more prominent. As AI autonomy increases, so too should the emphasis on establishing guardrails, transparency, and oversight.
Even as its capabilities evolve, AI remains an amplifier for RCM professionals, not a replacement. As always, the focus should be on finding the balance between AI and human expertise to build more resilient systems that are ready to move forward at unprecedented speed.
The AI train in healthcare RCM just keeps rolling on.