AI’s new role in streamlining patient access

AI is moving fast in healthcare, and nowhere is the impact more immediate or more felt by patients than the revenue cycle front end. As health systems face persistent staffing shortages, rising administrative costs and growing reimbursement uncertainty, AI is shifting patient access work away from reactive check-and-chase tasks and toward proactive financial clearance that reduces downstream denials, rework and patient frustration.

How AI is reshaping front-end RCM

Front-end revenue cycle processes—scheduling, registration, eligibility verification, estimates and prior authorization—have historically been burdened by manual follow-up and fragmented workflows. Healthcare needs a new operating model where AI shrinks the volume of routine manual touches while managing the complexity of what remains with human-in-the-loop governance. In practice, that means fewer repetitive transactions and more intelligent orchestration.

Nick Barcelona, CFO of WVU Medicine, told R1 at Becker’s CEO + CFO Roundtable that AI adoption is a strategic priority for his health system, emphasizing the benefits of greater efficiency, a better provider experience and improved patient satisfaction.

“When I think about what’s coming, it’s exciting,” said Barcelona. “It’s AI looking at the schedule, figuring out when there are openings, geotagging providers based on their proximity to the patient, finding the ones that are closest and can see them soonest and getting the best appointment scheduled for the patient with empathy and urgency.”

Instead of staff spending time searching for missing data, re-reading notes and tracking statuses, AI converts work queues into orchestrated pathways identifying what’s routine, what’s missing information and what needs escalation. As orchestration matures, more encounters become known before the patient arrives—eligibility and coverage signals are validated earlier, estimates are generated more consistently and exceptions are routed to the right human experts before they become day-of-service disruptions or post-visit denials. The destination is a more consumer-like experience where financial clarity begins even before booking.

“When you start to automate, an increasing percentage of encounters become touchless” said Lee Kupferman, executive vice president of Phare operating system at R1. “So, when a patient books their appointment, you can say this is for an MRI. It’s with this doctor in this building on this day and you know what your co-pay is, you know exactly what the bill’s going to be before you even get there. It’s more like any kind of transaction you would encounter outside of healthcare.”

Prior authorization: a pressure point AI is relieving

Prior authorization is a powerful example of how AI can reduce friction. The process has become one of healthcare’s most significant administrative burdens, costing an estimated $35B annually and contributing to 92% of all care delays. AI-enabled prior authorization can identify when authorization is required, submit more complete requests with the right documentation, proactively track payer decisions and update status back into the EHR—at scale, with expert oversight where needed.

What health systems should do first

To manage the immediate changes AI adoption introduces—and realize value without eroding trust—health systems should:

• Start with readiness across process, people and data. AI will tend to amplify data issues, making data quality and governance prerequisites for scale.
• Pick early use cases with obvious friction and measurable value. Establish baselines using metrics like touches per account, cycle time, time to resolution and fallout rates.
• Embed governance into workflows, not just policy. Define what AI can complete independently, what requires review and what must always escalate—supported by auditable traceability, sampling and stop-the-line controls.
• Redesign roles around exceptions and oversight. As AI takes on more first-pass work, staff shift toward exception management, sensitive patient conversations and continuous improvement.

It’s important to remember that AI is not a set-it-and-forget-it tool in patient access, it’s an operating model change that requires careful, intentional management. The organizations that win with AI will be the ones that simplify workflows, formalize human-in-the-loop governance and reskill teams for higher-value work across the revenue cycle.