R1
Connecting Revenue Decisions Across the Care Journey

A denied claim is often created way before it reaches a payer. An authorization can be missed before the visit, documentation can remain incomplete after care or a coding issue can pass into billing unnoticed. By the time the denial appears, several teams may already have touched the account and the correction can require another round of manual work.

R1 has built Phare OS around changing that sequence. Rather than placing isolated automation tools at different points in the revenue cycle, the platform connects patient access, claim preparation, payer interaction and account resolution through shared data and AI-driven decisioning. The aim is to identify problems earlier and keep responsibility for the account moving forward instead of handing unfinished work from one queue to another.

That changes the role of automation. Instead of completing a single task faster, R1 is using AI to coordinate what should happen next across the revenue operation.

Give Every Decision the Same Context

Revenue-cycle work becomes fragmented when each team sees only the part of the account assigned to it. An authorization specialist may know why approval was difficult, while a denial team later sees only the payer response. Valuable context can disappear between those stages.

Phare OS reduces that separation through a shared Data Platform and Payer Atlas, supported by Phare Intelligence. Together, they give applications across the revenue cycle access to clinical information and payer behavior without forcing every workflow to build its own view of the account.

R1 is shifting revenue operations from isolated task automation toward a connected system where one stage can increasingly inform the next.

Payer Atlas adds a particularly important layer. R1 says the capability is informed by more than 1,500 live payer connections and over 600 million annual transactions. That allows the system to consider not only published payer requirements but also patterns in how those requirements are applied across plans and circumstances.

The result is a broader operating context for AI. A system working a denial can draw on information from earlier stages, while upstream workflows can learn from the patterns that later created payment delays.

Move Prevention Ahead of Correction

The front end of the revenue cycle is where many downstream problems first take shape.

Phare Access supports activities such as order matching and insurance verification while also assisting with authorization and scheduling workflows. Patient-facing functions extend into reminders and financial estimates, giving teams a better opportunity to resolve coverage questions before care moves into billing.

Once the encounter becomes billable, Phare Claim applies another layer of review. Documentation and coding risk can be surfaced before submission, while certain errors can be corrected automatically and more complex questions can be routed to specialists.

The important shift is timing.

A missing authorization discovered after submission creates a denial. The same issue identified before the visit becomes a workflow problem that can still be corrected. Likewise, a coding inconsistency found before billing is easier to resolve than one discovered after the claim returns from the payer.

R1 is therefore moving automation closer to the point where the problem originates, not just automating the appeal after a denial occurs, but reducing how many preventable issues reach that stage at all.

Keep the Account Moving After Submission

Traditional automation often completes one transaction and then returns the remaining work to a human queue. R1’s model is designed around continuing responsibility for the account.

Phare Flow operates after claim submission, where AI agents can check status and interpret payer requirements while also managing denials or reconciling payments. The system can determine what follow-up is needed and route more complex activity to people when judgment is required.

R1 reports that Phare Flow can reduce manual processing time by approximately 80 percent, resolve more than 40 percent of A/R defects without human involvement and accelerate cycle time by more than five days. These are company-reported figures, but they illustrate what becomes possible when automation continues past a single completed task.

The larger distinction is orchestration.
An AI agent can finish one step, evaluate what happened and initiate the next action rather than waiting for another work queue to be opened. Human staff remain part of the process where ambiguity or expertise makes their intervention necessary.

That approach changes productivity without assuming every account should be autonomous. Routine follow-up can continue electronically while specialists concentrate on the cases where interpretation still matters.

Learn from How Payers Actually Behave

Written payer policy is only part of the reimbursement environment. The way a requirement is interpreted in practice can vary across plans and circumstances, creating patterns that are difficult to see from individual claims.

R1 uses Payer Atlas to capture more of that operating reality. When paired with Phare Intelligence, the platform can apply those patterns to future revenue-cycle decisions rather than treat each denial as an isolated event.

That creates a feedback loop across the revenue operation. A recurring denial pattern may indicate that documentation should change earlier. A payer response may reveal that a particular authorization step requires different evidence. Information discovered after submission can therefore influence how the next claim is prepared.

R1 is extending this concept toward real-time adjudication. Its current strategy is to align clinical information with payer requirements early enough that more claims can move with less back-and-forth after submission. The company presents this as an evolving capability rather than a universally achieved state.

The direction is significant because the revenue cycle becomes more valuable when it learns from its own outcomes.

Keep Human Judgment in the System

AI can reduce repetitive work, but revenue operations still contain decisions where judgment matters.

Phare Claim routes complex documentation and coding questions to expert review. R1’s wider model combines AI-driven work with human oversight so difficult accounts can move to specialists rather than be forced through an automated path that does not fit.

R37, the company’s AI research and development lab, extends that model. Its work includes payer-policy intelligence and coding accuracy along with clinical-denial prevention and revenue-cycle reasoning. Those efforts feed new capabilities into Phare OS rather than treating the platform as a fixed collection of automation tools.

Recent deployments show how R1 intends that model to operate at enterprise scale. In 2026, UF Health announced a technology-focused collaboration with R1 to deploy Phare OS across its revenue-cycle environment, with R37 engineers working alongside health-system teams and human governance remaining part of the operating approach.

R1’s recognition as an AI-Powered Revenue Operations Platform reflects this system-level focus. Its advantage is not simply that AI performs more revenue-cycle tasks. It is that information from one stage that can increasingly shape what happens at the next.

A denial may still appear at the end of the process, but Phare OS is designed to make the revenue operation look further upstream. The goal is to identify where the problem began, correct what can be prevented and keep the account moving toward resolution with fewer disconnected handoffs.