“A substantial portion of our coding is done with that engine, and it allows us to give feedback to doctors at a level we’ve never been able to, so accuracy is a huge part of this,” Mr. Gaines said. “The machine will call out if you’re down coded from a 5 to a 4. It will call out why that happened and will show that maybe you didn’t include certain notes that you should have. The machine can develop a profile about how you can document. That activity would take thousands of hours. The machine can do it with relative ease.”

The AI in market for billing and coding already stands at $2.4 billion and is expected to grow to $8.4 billion by 2033, according to an article published in March at media.market.us. This expected growth doesn’t surprise the Mayo research team, and an important piece to the growth could be patient satisfaction. With less subjectivity built into medical billing, patients could have more information about their

care—particularly related to how much it’s going to cost.

Patients “always ask, ‘What are we going to do, and how much is it going to cost?’ Dr. Morey said. “And we never have a good answer for it because we don’t know how much it’s going to cost until later. And they don’t find out until a month after that. If we can improve the ability to use AI to code these charts, maybe we can also know what it’s going to cost the. That might be a long way off, but it’s a possibility.”

While the study demonstrated the potential of AI in automating the billing process, the authors also noted limitations. The model was developed and tested using data from a single health system, which may not generalize to other health care environments with different coding practices or patient populations.

Additionally, the AI model is currently limited to professional billing codes for ED encounters. Future research could explore the application of AI to other areas of the revenue cycle, such as facility charges, procedural charges, or inpatient billing.

Dr. Morey emphasized that the research represents a “proof of concept,” but much work remains before AI-driven billing can be widely implemented. Continuous monitoring and retraining of the AI model will also be crucial to its success. As coding guidelines change and new clinical practices emerge, AI will need to adapt to ensure continued accuracy.