As hospitals and health systems continue to grapple with revenue leakage and clinical denials, many are turning to innovative technologies to bridge the gap between payers and providers. Organizations are leveraging artificial intelligence (AI) to streamline utilization management (UM), finding new ways to proactively identify appropriate levels of care, reduce avoidable denials, and foster greater collaboration with payers.
In fact, a recent Chartis Digital Transformation Survey found that 90% of healthcare executives have already prioritized leveraging digital and AI capabilities to achieve their goals. The survey also revealed that 91% of executives agree that health systems must “fundamentally change” their operations to better overcome current challenges and ensure a more sustainable near-term future.
Tanya Sanderson, RN, MBA, MHA, CHFP, CRCR, Senior Director of Revenue Integrity at Xsolis, has seen firsthand how this approach can transform financial performance and clinical workflows.
“Too often, appropriate inpatient level of care claims are submitted at a lower level of reimbursement because the UM team doesn’t believe it will be approved or downgraded after a payer claim denial — even though the data says it was appropriate, leading to lost revenue for the level of care provided,” she said.
Sanderson’s experience aligns with the journey of Inova Health System, a leading integrated health system in Northern Virginia. Prior to partnering with Xsolis, Inova struggled with manual UM processes that placed a significant burden on clinicians.
“Our RN workflows were extremely manual,” says Michelle Tutem-Greame, PA-C, M.S.H.S., Assistant Vice President of Revenue Integrity at Inova. “(Our RNs) were spending a lot of time every morning just creating lists of who got admitted from the emergency department overnight…it was a big time sucker for them in the beginning of their day.”
Inova implemented Xsolis’ Dragonfly platform, which leverages AI to provide a more data-driven approach to case prioritization and level-of-care decisions. The AI-driven Care Level Score™ now helps clinicians assess patient status in real time, updating continuously based on labs, imaging, and clinical documentation in the EMR.
“Eighty percent of our cases are now reviewed within the first 24 hours,” Tutem-Greame shared. “We’ve right-sized our observation rate, and we’re now getting paid for the level of care we provide.”
Sanderson emphasizes the importance of aligning these mid-revenue cycle improvements with backend denial management. “AI offers a shared framework that lets teams work together instead of against each other,” she says. “It helps bridge the gap between front-end and back-end teams, who have historically been stuck working across purposes.”
By providing a more objective, data-driven way to measure the appropriate level of care, AI-based tools can save time, prevent the “blame game,” and empower revenue cycle and UM teams to proactively ensure appropriate reimbursement.
“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,” says Erin Hodson, MSN, Vice President of Revenue Cycle at Inova.
As hospitals and health systems navigate the complex landscape of payer-provider collaboration, AI-driven utilization management emerges as a powerful solution to align incentives, streamline workflows, and protect revenue integrity. By bridging the gap between payers and providers, organizations can focus on delivering the most appropriate care while maximizing reimbursement for the services they provide.
Moreover, the Chartis survey indicates that digital maturity will be a key differentiator for leading health systems in the years to come. Executives believe the two greatest differentiators will be digital-first patient experiences and digitally enabled care. This suggests that the use of such AI tools should not be limited to only the largest health systems, but that health systems of every size must deploy AI-enabled interventions now.
The next five years will be telling, the survey says. Will providers advance toward proactive, AI-enabled care models, or continue to rely on reactive models that no longer guarantee survival in today’s competitive landscape?
Learn more about Xsolis’ Dragonfly platform to ensure your health system’s shift to proactive care and ensure revenue integrity for years to come.
