When a team of emergency physicians at Mayo Clinic in Rochester, Minnesota, decided to take on a research piece on artificial intelligence (AI) for medical billing, they first pieced together a team to get it done.

Jacob Morey, MD, MBA, knew they needed an AI expert, and Dr. Morey found Derick Jones, MD, an emergency physician trained in data and analytics. Medical billing’s impact on finances begged for an expert in that field. Richard Winters, MD, FACEP, is an emergency physician.

The result is a research piece published online in September in Annals of Emergency Medicine that looked at 321,893 adult ED encounters from their health system from January to September 2023. They developed an ensemble model, using natural language processing and machine learning techniques to predict billing codes from clinical notes combined with clinical characteristics and orders. By the end, the researchers said their conclusion matched the theory.

Machines could learn patterns and predict evaluation and management professional billing codes from Levels 2-5.

“It was a validation,” Dr. Jones said. “What we expected the model to be learning was proven in the results. It was aligned and very reassuring.”

While the Mayo team closes the book on this research article, experts in the reimbursement and coding field say the story of AI for medical billing is barely into Chapter 1. One plot could focus on enormous potential to save time and money and streamline a tedious, necessary process of managing reimbursement.

The other storyline warns about putting too much stock in AI too early.

But investigating ways to make things more efficient is worth the work and exactly why the research team said they tackled this research, and the subsequent piece, titled “Artificial Intelligence to Predict Billing Code Levels of Emergency Department Encounters.”

They wanted to shed light on the possibilities that exist to improve how physicians are reimbursed for their services.

“Taking our notes and putting them into billing codes is a repetitive process,” Dr. Morey said. “That’s something that has high potential to be automated by AI. And now, we’ve seen better AI tools over the past few years. Now that technology is available to take our notes and use natural language processing to be able to take that data, make it into more structured data, in a model and automate it.”

The AI used in the study analyzed various factors, such as the number of medical orders, discharge disposition, and specific notes within the clinical record.