VIDEO: Use of AI in radiology revenue cycle management

Artificial intelligence (AI) use in radiology has been seeing a lot growth on the clinical side, but AI is also making inroads to improve efficiency on the practice management side of medical imaging. This was a topic of discussion in sessions at the Radiology Business Management Association (RBMA) 2024 meeting.

Radiology Business spoke with Dave Walker, senior director of revenue cycle, Radiology Associates of North Texas (RANT), who spoke at the meeting on the use of AI for revenue cycle management. His practice began using AI about five years ago with coding and it expanded to other areas on the business management side. He said AI is becoming more prevalent at his practice and across radiology. Most importantly, they have had success using AI to capture more revenue and improve efficiency.. 

"Some of our big successes have been in our self-pay collections arena, doing a lot of modeling around propensity to pay, what's the best fee schedule to charge for patients. We have seen an 8% increase in our self-pay revenue, which for a practice that's primarily hospital-based, it's a big leap and has really done a lot. It's really been a big win for the practice," Walker explained.

He said the AI enabled them to work harder on the front end before a collection agency gets involved. Walker said this increased patient satisfaction, while at the same time increasing the practice's satisfaction because they are collecting more. AI is also being leveraged to take a better look at data analytics and insurance claims processing.

"We also use a lot of data analytics machine learning to look at our charge capture ratios. Five years ago our charge capture ratio was at 98.5%, but we now consistently stay at 99.7% because of our reports database. We're able to really quickly isolate what hasn't been billed, figure out why, and get it out the door. That alone has meant millions of dollars per year for us," Walker explained.