How LLMs are Transforming Revenue Cycle Management

A New Era of RCM Automation

EDI digitized claims in the 1990s and 2000s. RPA automated workflows in the 2010s by looping repetitive flows. But denial and AR work changes with every claim: different payer, different code, different conversation, different resolution.

LLMs enable AI agents—a new level of RCM automation that hasn’t been possible until now. Agents can read websites or PM systems like a human, talk to payer reps, navigate insurance portals, and choose the next best action—reliably, with an audit trail.

Crucially, AI agents review and reason about their own work to ensure accuracy and decide what to do next.

Where does Amperos fit? Amanda, our AI agent, is a multi‑modal AI coworker for RCM—phone + portals—built by healthcare billers. Clinics see real outcomes on denials, status, and collections without adding headcount.

Why RCM Needs AI Agents

High repetition, with variation. Working claims is repetitive. Billers have to check the PM, check the payer portal, call insurance, take the next action—but each iteration is different. Insurers, denial reasons, plan‑level quirks, portal layouts, and live conversations all vary. AI agents can respond to these variations in real time.

Multi‑platform workflows. RCM teams work across multiple systems—phone, PM/EMR, payer websites, and work queues. Multi‑modal agents operate across all of them and carry context from one step to the next—just like a human.

Legacy automation is brittle. Clearinghouse outages and payer non‑responsiveness expose gaps in traditional automation. RPA breaks when UIs change. Teams fall behind, AR grows, and timely filing limit losses increase. AI agents help you scale work and react in real time to denials and status changes.

So what can Amperos do? Like a human collector, Amanda—Amperos’ AI for AR—works across the PM, payer portals, and calls, carrying information across steps. She validates her own work before handing it off—and she can do it at the scale of hundreds of FTEs for a single practice.

RPA vs. LLM Agents—What’s Actually Different?

RPA automates fixed workflows by repeating patterns of keystrokes and clicks—great for copy‑pasting a set of numbers from a spreadsheet into a PM system. When the layout changes, RPA “clicks the wrong spot,” and breaks. Even if it doesn’t break, RPA completes the action without reviewing whether the outcome makes sense.

LLM agents can do the same actions as RPA (click, type), and they can also read and think. Instead of clicking the same pixel, an agent has an objective (e.g., find claim status), locates the right screen, extracts the information, and verifies whether it’s sufficient to progress the claim. If not, it tries an alternate path. It can also speak naturally with humans. That flexibility lets it adapt to new portals, new payers, and new workflows without breaking.

Here’s an analogy: You have two robots. One can’t read—it loops clicks all day. But the other reads, speaks, and reasons—so it adapts when layouts, scripts, or requirements change.

How Amperos applies this: Amanda is built on LLMs. After every action—portal or call—she pauses to review whether the work is complete and accurate. If not, she tries again or escalates. The goal is simple: don’t pass bad information or incomplete work to your team.

So What Do AI Agents in RCM Actually Do?

Below are the core capabilities and how they translate to outcomes in your workflows.

Speech

What: Agents can hold real conversations—not from a fixed script, but guided by an objective and training.

Unlocks: Full automation of calls to payers (and, where appropriate, patients or facilities). No more staff hours lost to hold times, and less time chasing revenue by phone.

Amperos: Amanda uses state‑of‑the‑art LLMs and speech technology. Trained by experienced billers, she has handled 150k+ calls to date for customers.

Perception (Screen & Speech Understanding)

What: Because agents can read on‑screen text and understand spoken responses, they can reason about what they see and hear. That means verifying the right fields in a portal, catching inconsistencies during a call, and requesting reprocessing when criteria are met.

Unlocks: Higher‑quality automation. Each result is “pre‑audited” by the agent for completeness before your team touches it.

Amperos: Every portal action and call summary is checked by an AI auditor for accuracy. The handoff includes transcripts, screenshots/PDFs, and structured fields your team can trust.

Planning (During Calls and Across Steps)

What: Advanced agents don’t just talk; they plan. They understand denial categories, know which questions unlock the next step, and recognize when an appeal or reprocessing is appropriate.

Unlocks: Calls that go beyond generic status checks—down to the specific evidence required for resolution.

Amperos: Amanda knows thousands of denial scenarios and knows when and how to request reprocessing or gather documents to move the claim forward.

Cross‑Tool Use

What: The agent reviews your PM/workqueue to find unpaid claims, uses that context in payer portals, then calls when necessary, and finally proposes or executes next steps.

Unlocks: End‑to‑end automation, not just isolated workflows that then require your team to jump in. No one needs to “task” the bot for each phase.

Amperos: Amanda orchestrates actions across systems and writes back structured updates. Your collectors review what Amanda surfaced, apply judgment, and close out the claim—resulting in 2–5× more claims per FTE.

Memory & Grounding

What: Agents leverage prior attempts, payer policies, and internal playbooks—just like an experienced biller leans on their training.

Unlocks: Detailed, context‑aware work, including the ability to drill down to specific CPT codes or denial reasons. That means capabilities far beyond one or two canned questions, like just asking for claim status.

Amperos: Amanda was trained with input from dozens of real RCM team members. She distinguishes, for example, prior auth vs. non‑covered services vs. frequency denials and adjusts the approach accordingly.

Reliability, Guardrails, and Auditability

What: Well‑built agents review their own work for completeness and capture artifacts for human review. Recordings, transcripts, and screenshots are attached to every result.

Unlocks: Trust and auditability. Your team sees exactly what happened and can reuse documentation for appeals, training, root‑cause analysis, and more.

Amperos: Every call is recorded and transcribed; every portal action is captured and exported to PDF. No action is taken without evidence collection.

What’s the Impact of Using AI Agents?

Productivity. Because AI agents take on the high‑friction, low‑judgment portions of AR and collections, your team spends less time per claim and can work more claims every day.

Amperos customers see their collectors working 2–5× more claims within roughly eight weeks of go‑live.

Cost to collect. Agent time costs less than human time, and throughput improves at the same time. As a result, your cost‑to‑collect drops meaningfully.

Amanda costs 50–80% less than a human performing the same action (payer calls, portal checks), while also increasing total volume worked.

Lower denials. With more capacity, more denials get worked to resolution, and fewer dollars are written off.

Amperos customers often see denials decrease by up to 80% after full deployment.

Faster collections. Agents can touch claims as soon as they are denied or stall in AR, rather than waiting for a human to have capacity. That shortens the time to status and accelerates cash.

Amperos customers have seen 90+ day AR decrease by 40%+ and overall AR balances decrease by ~20% on average.

Better data. Agents take perfect notes. Every call is recorded and transcribed, and every field from portals is extracted and attached to the claim.

Amperos customers use platform data to gain clear visibility into payer‑ and code‑level patterns, enabling upstream fixes that prevent denials in the first place.

Real‑World Examples

EyeCare Services Partners. ESP’s RCM team was understaffed. Within one week, Amanda was up and running. Within about two months, the staffing gap was effectively closed, and ESP lowered cost‑to‑collect by reducing low‑performing headcount.

Tend Dental. The team had a growing backlog it couldn’t work through with existing headcount. Amanda went live in 3 days and cleared the backlog in ~8 weeks. Within about eight weeks, 90+ day AR decreased by ~40%.

Inpatient provider. This provider was an industry leader, but was facing staffing constraints and timely filing risk. With Amanda, the team worked claims ~2× faster within a month and achieved a record collections month of ~$200M.

Fit Assessment: Questions to Ask Before You Buy

Use these to decide whether an AI agent—and a given vendor—is a good fit for your workflows.

Scope & capabilities

1. Can the agent act across workflows (phone + portals + PM), or does it only do one—leaving your team to tie together the AI’s actions?

2. How was the AI trained—by real billers on real workflows, or is it an out‑of‑the‑box model that doesn’t understand denial codes and payer nuance?

Evidence & reliability

1. Does the agent provide proof of work—screenshots, recordings, transcripts, and structured fields—or is it a black box?

2. How are errors detected and remediated? Is each result vetted for quality before handoff?

Integration & change management

1. What’s the time to first calls and how quickly should productivity improve?

2. Does the system write back to your PM/workqueue (fields, notes, attachments), or is information siloed in another tool?

3. What does the expansion ladder look like once the first workflow proves out?

Pro tip: Request a free trial run with your data. Make sure the agent can produce real results—not just a canned demo clip.

How to Try an AI Agent (Next Steps)

There’s a lot of AI hype. If an agent is truly value‑add, the vendor should be comfortable proving it on your data.

Want to try Amanda, Amperos’ AI coworker for denials and collections, at no cost? Set up a time to learn more here.