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A single mid-sized RCM team can spend hundreds of hours a month on one task: calling insurance payers to ask "what's the status of this claim?" It's a question that gets asked thousands of times a week across the industry, and until recently, the only way to get an answer was a human on the phone.
Agentic AI is changing that. In healthcare claims processing, it refers to AI systems that can independently carry out multi-step claims workflows, placing outbound calls to payers, navigating IVR menus, interpreting responses, and logging structured outcomes, without needing a human to manage each step. Unlike rule-based automation, which only works within fixed scripts, agentic AI adapts in real time to how a payer representative actually responds, which is what makes it capable of handling claim status checks, denial follow-up, and prior authorization calls at scale.
Quick Summary
- Claim follow-up calls are one of the most labor-intensive, repetitive tasks in revenue cycle management (RCM), and traditional automation can't handle the variability of live payer conversations.
- Agentic AI voice agents place the calls themselves, hold real conversations with payers, and capture claim status, denial reasons, and authorization details directly into billing or PM systems.
- The healthcare claims management market is projected to grow from $50.32B (2025) to $62.11B (2026), reaching approximately $413B by 2035, a 23.43% CAGR between 2026 and 2035.
- Administrative costs across the health insurance industry rose 4.4% ($5.8B) in 2025 alone, adding pressure on RCM teams to find efficiency gains that don't require adding headcount.
- Trigma has built and deployed this exact kind of system in production for a US healthcare RCM company, detailed further below with a full case study.
Why Claims Follow-Up Is Under So Much Pressure Right Now
Every submitted insurance claim eventually needs a follow-up call, sometimes several. Someone has to verify status, ask about denial reasons, confirm pending documentation, and log all of it accurately. At scale, across a multi-specialty practice or an outsourced billing operation, this adds up to thousands of calls a month, and the volume only grows with claim complexity.
The financial pressure behind this problem is well documented. Per the National Association of Insurance Commissioners' 2025 Annual Health Industry Commentary, administrative expenses across the U.S. health insurance industry grew 4.4%, a $5.8 billion increase, in 2025 alone. Claims PMPM (per member per month) reached $337, tightening margins across the board. None of that growth comes from claims getting simpler to process, it comes from volume, complexity, and the labor required to keep up with both.
At the same time, the broader healthcare claims management market itself is scaling fast. Towards Healthcare projects the global market to grow from $50.32 billion in 2025 to $62.11 billion in 2026, reaching approximately $413 billion by 2035, a 23.43% compound annual growth rate between 2026 and 2035. That kind of growth curve reflects an industry actively investing in tools to handle claims volume differently than it has for the last two decades.
Put simply: hiring more staff to keep pace with claim follow-up doesn't scale the way call volume does. That's the gap agentic AI is built to close.
What Makes This “Agentic,” Not Just Automated
Traditional claims automation, IVR trees, RPA bots, keyword-matching scripts, works fine for structured, predictable tasks: data entry, eligibility checks against a known format, routing a document to the right queue. It breaks down the moment a conversation doesn't follow a fixed script, which is exactly what happens on a real payer call.
A payer representative might ask a clarifying question mid-call, put you on hold, or phrase a denial reason in a way no keyword-matcher would catch. Agentic AI handles this differently: instead of matching against a decision tree, it runs a conversational engine that interprets intent, asks follow-up questions, rephrases when it doesn't get a clear answer, and confirms details back before ending the call. It's the difference between a system that executes a script and one that can actually hold a conversation.
This distinction matters for a specific, practical reason: claim follow-up calls are structured enough to automate (the questions being asked are largely the same every time) but variable enough that a rigid script fails constantly. Agentic AI sits in exactly that gap.
How an Agentic AI Voice Agent Actually Handles a Claims Call
A production-grade agentic AI system for claims follow-up typically runs through a few consistent steps on every call:
Outbound call initiation.
The system dials the payer for a queued claim, working through a batch of calls in parallel rather than one at a time.
Real-time speech-to-text.
As the payer representative speaks, their response gets converted to text in near real time, fast and accurate enough that nothing downstream is working off a delayed or garbled transcript.
Conversational reasoning.
The number of engineers, architects, and specialists a company can put behind a project, and the range of technologies they cover.
Text-to-speech response.
The agent's next question or confirmation gets spoken back naturally, with round-trip latency low enough that it doesn't feel like talking to a machine on a delay.
Structured data capture.
Claim status, denial reason, authorization detail, whatever the call was for, gets logged in a structured format and pushed into the billing or practice management system, no manual re-entry required.
Escalation when needed.
If a call hits something genuinely ambiguous or outside its scope, it routes to a human specialist rather than guessing. This isn't a fully unattended system, and it shouldn't be, the goal is removing humans from the repetitive part of the job, not from oversight entirely.
This general approach, real conversation over rigid scripts, is covered in more depth in Trigma's broader guide to AI voice agents, and it maps closely to the kinds of questions RCM and billing teams are actively asking right now, how to get structured data back into billing software without manual re-entry, how to navigate payer IVRs without a human on the line, and how to keep HIPAA compliance intact while doing it.
Where Agentic AI Fits Across the Claims Lifecycle
Claim status follow-up is the clearest starting point, but the same underlying approach extends to several adjacent, high-volume RCM workflows:
1. Denial management
Instead of a human calling to ask why a claim was denied and manually logging the reason, an agent can place that call, capture the specific denial code and explanation, and route it directly into a resubmission workflow.
2. Eligibility and benefits verification
Confirming coverage, deductible status, and remaining balances before a service is rendered is another high-volume, repetitive call type that fits the same pattern.
3. Prior authorization follow-up.
Chasing authorization status with a payer, often across multiple calls per case, is one of the more time-sensitive workflows in RCM, and one where faster turnaround directly affects patient care timelines.
4. Credentialing calls
For practices onboarding new providers or launching new locations, credentialing status checks with payers are another recurring, structured call type that scales poorly with manual staffing.
5. A/R backlog reduction
For practices facing an aging accounts receivable backlog, the bottleneck is often simply call volume, more claims need follow-up than staff have hours to make calls for. Automating the call itself is what unlocks faster backlog reduction, not just faster individual call handling.
What to Look For When Evaluating an Agentic AI Voice Platform for RCM
If you're evaluating vendors in this space, a few criteria separate a genuinely production-ready system from a proof of concept:
- Real conversational capability, not a scripted IVR tree. Ask for a live call demo against an actual payer scenario, not a canned script.
- Structured output into your existing systems. The value of the call is lost if the outcome still requires manual re-entry into your billing or practice management software.
- Scale and concurrency. A system that handles a handful of calls well doesn't necessarily handle thousands of parallel calls during a month-end spike. Ask specifically how the platform scales under load.
- HIPAA compliance, end to end. This includes call recording storage, transcript handling, and access controls, not just the conversation layer.
- Human escalation paths. Any vendor claiming a fully autonomous system with zero human oversight for edge cases should be a red flag, not a selling point.
- A real, verifiable case study. Ask for evidence of the system running in production, not just a demo environment.
Agentic AI in Production for Healthcare Claims (A Working Example)
Trigma designed and built exactly this kind of system for a US-based healthcare revenue cycle management company. The AI voice agent places outbound calls to payers, holds real-time conversations, and captures claim status, denial reasons, and authorization details, with escalation to a human specialist for cases outside its scope. The platform runs calls in parallel at scale, with a unified dashboard giving the operations team live visibility into every call, batch, and outcome.
The full breakdown of the problem, the architecture, and the results is documented in the case study: AI Voice Agents for Healthcare Claims Follow-Up.
How Trigma Helps Healthcare and RCM Organizations Implement This
Trigma works as a technology and development partner for healthcare organizations and RCM companies looking to integrate agentic AI into claims operations, whether that means building a new voice agent from the ground up, integrating one into an existing EHR or practice management system, or extending an existing automation stack with agentic capabilities for denial management, eligibility checks, or credentialing workflows.
If your team is evaluating where to start, claim status follow-up is typically the highest-leverage first deployment: high call volume, well-defined structure, and a clear cost baseline to measure results against. From there, the same architecture extends naturally into denials, prior auth, and credentialing.
