Singing River’s Practical Strategy for AI-Enabled Revenue Cycle Transformation

AI revenue cycle transformation is not just for large health systems

R1 is helping healthcare organizations rethink revenue cycle transformation by making AI practical, targeted and operationally grounded. That was the central theme of a recent Becker’s Healthcare webinar featuring leaders from Singing River Health System and R1, and it reflects an important idea behind Phare OS, healthcare’s first AI-native revenue operating system: AI-enabled transformation is not limited to the nation’s largest health systems.

Even midsize healthcare providers can use focused AI applications to improve revenue cycle performance today while building a more connected operating model. For many organizations, the conversation around AI in revenue cycle management has moved from theoretical potential to practical execution. The most effective use cases often begin with focused workflows where AI can help prioritize work, reduce repetitive manual tasks and give teams better information at the point of decision.

Singing River’s approach shows how a midsize health system can begin with a practical, high-impact use case while still building toward broader revenue cycle transformation. Rather than treating AI as a replacement for human expertise, the organization is using technology to help teams work higher in the value chain and focus attention where it can have the greatest operational and financial impact.

Why fragmented revenue cycle workflows are harder to sustain

For too many health systems, the revenue cycle still operates as a series of disconnected functions, with coding, utilization review, denials, follow-up, and appeals moving through separate workflows instead of as one dynamically orchestrated model. Singing River leaders explained why that approach is becoming harder to sustain amid labor pressure, margin constraints and growing administrative complexity.

Jason McNeill, chief financial officer of Singing River Health System, said the organization recognized that incremental improvements alone would not be enough to keep pace with today’s environment.

That view reflects a broader shift in how providers are evaluating revenue cycle technology. The goal is not automation for its own sake, but a more connected operating model that helps teams focus on the work that matters most. That is the promise of Phare OS and AI-enabled applications like Phare Audit – combining workflow intelligence, operational expertise and human judgment to improve outcomes in a focused, credible way.

Why mid-cycle is a great place to begin AI transformation

Singing River is a compelling example because it is applying this model initially in the mid-cycle, an area that often gets less attention than access or denials, despite its considerable downstream impact. Documentation, coding accuracy and review prioritization shape what happens later in the claim lifecycle. When those functions are fragmented, organizations risk rework, missed reimbursement, unnecessary clinician queries and preventable denials.

For Singing River, the challenge was not poor performance. In fact, leaders emphasized that their revenue cycle team was already strong. The issue was how to unlock the next level of improvement without burning out staff.

AI as workforce support, not replacement

AI can support revenue cycle teams by reducing the amount of repetitive, manual work required to identify accounts that need attention. In practice, that means experienced staff can spend more time applying judgment, resolving complex issues and supporting more accurate outcomes.

Sarah McGoldrick, executive director of finance at Singing River, shared a striking example from her team. A frontline employee came to her office overwhelmed by the volume and nature of the work. McGoldrick recalled the employee characterizing the work as “soul sucking.” Many team members, she said, were buried in manual, repetitive tasks that kept them from applying their expertise where it could have the greatest impact.

That is where Phare comes into focus, helping staff work higher in the value chain and at top of license. As McGoldrick put it, “I think that what I have seen the most from the AI standpoint and with some of these technologies as they’ve come up is that it really surfaces that value-add work and automates the stuff that you really don’t need a human to touch.”

Early proof point: Phare Audit in action

That principle is already taking shape through Phare Audit, which Singing River recently brought live. McGoldrick described the early response from staff to the new system.

Phare Audit helps surface accounts that warrant closer review, improving coding accuracy and helping teams prioritize effort where it is most likely to affect reimbursement. Just as importantly, it supports more precise clinician engagement, reducing unnecessary back-and-forth and focusing documentation clarification where it will matter most.

Why trust and transparency matter in AI adoption

For health systems evaluating AI solutions, trust is built through operational transparency: what the technology can do today, where human expertise remains essential and how success will be measured. That balance between technology and trust is a hallmark of the relationship Singing River has with R1. “We really were looking for an enterprise partner and an enterprise shift in the model,” McNeill said. He also emphasized the importance of transparency, noting that R1 laid out realistic goals and was clear about current and future AI capabilities.

McGoldrick echoed that point, saying that transparency builds credibility and has been a key factor in the success of their Phare Audit implementation.

Start small, prove value, build from there

That credibility matters in a market full of abstract AI claims. What organizations want now is proof – a focused use case, a live workflow and measurable operational value. Singing River’s experience shows how a health system can start with a defined mid-cycle challenge, use AI to reduce low-value effort, improve productivity and accuracy and build from there.

As McGoldrick put it, AI is coming, whether we like it or not. The question is not whether the revenue cycle will change, but how organizations can adopt the right model to make that change meaningful.

What midsize health systems can learn from Singing River

Singing River’s experience shows that AI-enabled revenue cycle transformation does not have to begin with a massive, all-at-once technology overhaul. It can begin with a defined operational challenge, a clear workflow and a partner that understands both technology and revenue cycle operations. For midsize health systems, that approach can make AI adoption more practical, measurable and sustainable.