AI is transforming healthcare RCM, but adoption remains a high-stakes challenge, especially for standalone and mid-sized facilities. While the impact of AI on revenue cycle performance is clear, for hospitals of a certain size, technology is only part of the equation.
Cost is an obvious factor, specifically paying a lot up front for what could be a not-so-immediate return. Then there’s the question of internal capabilities. Who’s going to own the ongoing updates and maintenance when the IT team is already working at capacity? Governance might be the most difficult area of all to reconcile. Larger hospitals are forming in-house oversight teams, but smaller hospitals likely don’t have that luxury.
Any one of these factors is probably manageable on its own. But stack them together, and the AI decision gets even more complicated.
Meanwhile, the problems AI can help with, like claims management and A/R resolution, continue to get worse. At some point, the cost of waiting starts to feel higher than the cost of adoption.
We put together a checklist that tackles the biggest barriers keeping healthcare organizations from moving forward with AI. Five pain points with a fix for each one.