RCM Automation
Maximize revenue with intelligent automation
End-to-end revenue cycle management powered by agentic AI. From charge capture to payment posting, our platform automates the entire billing workflow while maximizing reimbursement.
Claim Automation
Denial Prevention
Payment Posting
Prior Auth AI
A/R Management
Revenue Analytics
Automate your revenue cycle
Our AI agents handle every step of the revenue cycle, from eligibility to payment.
Automated Claim Submission
AI-powered claim scrubbing and submission with real-time eligibility verification and prior authorization.
Denial Management
Intelligent denial prediction, root cause analysis, and automated appeal generation with 85% success rate.
Payment Posting
Automated ERA/EOB processing with intelligent payment matching and variance identification.
A/R Follow-up
Prioritized worklists with AI-driven collection strategies and automated patient outreach.
Revenue Analytics
Real-time dashboards with predictive revenue forecasting and performance benchmarking.
Prior Authorization
Automated prior auth requests with intelligent payer routing and status tracking.
From charge to cash
Charge Capture
AI automatically extracts charges from clinical documentation with 99% accuracy.
Claim Scrubbing
Multi-layer validation against payer rules, LCD/NCD policies, and coding guidelines.
Submission
Intelligent routing to clearinghouses with real-time status tracking.
Payment & Posting
Automated reconciliation with variance alerts and underpayment detection.
Solving Real Problems
Common RCM challenges and how we solve them
What revenue cycle automation actually covers
Revenue cycle management is the entire financial arc of a patient encounter, from the moment an appointment is booked to the moment the last dollar is collected. In a US practice that arc crosses a dozen systems and as many payer rulebooks, and almost every handoff between them is still performed by a person retyping data from one screen into another.
Revenue cycle management automation with agentic AI replaces those handoffs. Rather than a single model answering questions, a set of specialised agents each own a stage of the cycle, pass structured work between each other, and escalate to a human only when the decision genuinely requires one.
Patient access and eligibility
Before a visit happens, coverage has to be confirmed. Automated eligibility runs the EDI 270 request and parses the 271 response for active coverage, plan type, deductible status, copay and coinsurance, so front-desk staff are not calling payer IVR lines for information a transaction can return in seconds.
Charge capture and coding
Charges are extracted from clinical documentation and matched against the procedures actually performed. Missed charges are pure lost revenue, and they are invisible on any report because nobody ever billed for them. Automated capture reconciles the schedule against posted charges to surface encounters that were never billed at all.
Claim scrubbing and submission
Before an EDI 837 leaves the building it is validated against payer-specific edits, NCCI procedure-to-procedure pairs, LCD and NCD medical necessity policies, and plain formatting rules. Most denials are avoidable at this step, which is why scrubbing is the highest-leverage automation in the cycle.
Remittance and payment posting
The EDI 835 remittance advice returns payment detail alongside CARC and RARC codes explaining every adjustment. Automated posting reconciles those against expected contract rates and flags underpayments, which otherwise pass silently because a partially paid claim looks like a paid claim on a worklist.
Denial management and appeals
Denials arrive as reason codes, not explanations. Agents classify each denial by root cause, distinguish the ones worth appealing from the ones that need a corrected claim, and assemble the appeal packet with the clinical evidence and policy citation that supports it.
A/R follow-up
Ageing claims need status checks, and the EDI 276 and 277 pair provides them without a phone call. Agents work the entire A/R inventory on a cadence rather than the top of a worklist, so low-value claims that staff never reach still get pursued.
Where revenue quietly leaks
Revenue rarely disappears in one dramatic event. It drains through small, repeated failures that no single person is positioned to see: a claim denied for a missing prior authorisation, an underpayment three percent below contract, a secondary claim never filed, an appeal deadline missed by a week.
The economics of manual rework are what make this durable. When the staff cost of appealing a claim approaches the value of the claim itself, the rational decision is to write it off. Practices therefore abandon large volumes of genuinely collectable revenue, not through carelessness but through arithmetic. Automation changes that arithmetic, because the marginal cost of working one more claim falls close to zero.
Agentic AI versus rules-based automation
Practices have had rules engines and robotic process automation for years. They break for a predictable reason: they encode a fixed path through a process that is not fixed. A payer changes a portal layout, a form gains a field, an edit is updated, and the script fails silently.
Agentic systems are structured differently. They are given a goal, a set of tools, and the authority to choose among them, which means they can read an unfamiliar denial letter, decide it needs a corrected claim rather than an appeal, and route it accordingly. That flexibility is also why guardrails matter more, not less: every action is logged, bounded by policy, and reversible, with defined thresholds that force human review.
RCM Employee runs this cycle end to end
RCM Employee, which we built for XEHR LLC, is an autonomous AI FTE for the revenue cycle. It is not a tool a biller operates; it is assigned the work itself — eligibility and benefits, coding and claim scrubbing, denial appeals and payment posting — and works it continuously, in plain English, with a full audit trail behind every action.
It runs alongside XEHR, the AI-native EHR and practice management platform we built for XEHR LLC, or against the system a practice already uses. Where an existing EHR lacks payer connectivity, we add production-grade X12 EDI to it directly, including 837 claim submission, 270 and 271 real-time eligibility, and remittance handling, so the cycle described above can actually run.
Questions we get asked most
What is revenue cycle management automation?
It is the use of software to perform the administrative and financial steps of a patient encounter without manual data entry, covering eligibility verification, charge capture, coding, claim submission, payment posting, denial management and accounts receivable follow-up. Agentic AI extends this by handling steps that require judgement rather than a fixed rule, such as classifying a denial or assembling an appeal.
How does agentic AI differ from the automation already in my practice management system?
Most built-in automation is rules-based: it follows a predetermined path and stops when reality departs from that path. An agentic system is given a goal and a set of tools and selects its own route, so it can absorb a changed payer form or an unfamiliar denial reason without a developer rewriting the rule. Every action remains logged and bounded by policy.
Does automating the revenue cycle mean replacing billing staff?
In practice it reassigns them. Automation absorbs the repetitive volume — status checks, eligibility calls, straightforward resubmissions — while people handle payer negotiations, complex appeals, and the exceptions that need clinical or contractual context. The usual outcome is that a billing team works a much larger claim inventory rather than a smaller one.
How is protected health information handled?
Revenue cycle data is protected health information under HIPAA, so it requires a Business Associate Agreement with every vendor that touches it, encryption in transit and at rest, role-based access control, and an audit trail recording which agent or user accessed which record and when. Any AI component must operate under the same controls as the rest of the stack.
Which claims should be automated first?
High-volume, low-variation work returns the most immediately: eligibility verification, claim status checks and payment posting are largely deterministic and carry low clinical risk. Denial appeals and coding are higher value but need a human review step before they run unattended, so they are usually the second phase rather than the first.
Does this work with our existing EHR?
It has to. Practices rarely replace an EHR to adopt automation, so integration happens through whatever the system exposes — FHIR APIs where available, HL7 interfaces, EDI transactions through a clearinghouse, or database-level extracts. The integration path is a scoping question, not a barrier.
Let's build the future together
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