Health systems across the U.S. are increasingly turning to artificial intelligence to streamline mid-revenue cycle processes, reduce clinician burden and improve reimbursement accuracy.
During a featured session at Becker’s 10th Annual Health IT + Digital Health + RCM Conference, leaders from Falls Church, Va.-based Inova Health and AI company Xsolis shared how their partnership has transformed utilization review and financial performance.
The session featured:
- Erin Hodson, MSN, vice president of revenue cycle at Inova Health
- Michelle Tutem-Greame, PA-C, M.S.H.S., assistant vice president of revenue integrity at Inova Health
- Heather Bassett, MD, chief medical officer at Xsolis
- Tanya Sanderson, RN, MBA, MHA, CHRP, CRCR, senior director of revenue integrity at Xsolis
Here are four key takeaways:
Note: Quotes have been edited for length and clarity.
1. Manual workflows were a burden on clinicians.
Before partnering with Xsolis, Inova struggled with a lack of visibility into key metrics — such as observation rates and peer-to-peer utilization — and relied heavily on manual workflows.
“Our RN workflows were extremely manual,” Ms. Greame said. “They were spending a lot of time every morning just creating lists of who got admitted from the emergency department overnight…frankly, it was a big time sucker for them in the beginning of their day.”
2. Xsolis’ AI helped reframe utilization management
Inova implemented Xsolis’ Dragonfly platform in December 2024, enabling a more data-driven approach to case prioritization and level-of-care decisions. The AI-driven Care Level Score™ now helps clinicians assess status in real time, updating continuously based on labs, imaging and clinical documentation in the EMR.
3. Early results show measurable gains
Since go-live, Inova has seen significant efficiency improvements and measurable impact. “Eighty percent of our cases are now reviewed within the first 24 hours,” Ms. Greame said. “We’ve right-sized our observation rate, and we’re now getting paid for the level of care we actually provide.”
4. AI also bridges front-end and back-end teams
Ms. Sanderson emphasized the importance of aligning mid-revenue cycle improvements with backend denial management. Historically, these two teams can be stuck working across purposes as backend teams work to reduce denials and limit AR days while mid-cycle teams focus on tasks like documentation completeness. “AI offers a shared framework that lets teams work together instead of against each other,” she said.
Looking ahead, Inova leaders expressed interest in further AI-driven enhancements from Xsolis, such as DRG prediction and discharge planning.
“There aren’t other tools that can quantify (denial prevention) like Xsolis does and update continuously based on what’s going on in the patient,” Ms. Hodson said. “And they’re just going to go further and make all of this easier.”
