Some agents work at the front end, helping with eligibility, benefits verification and prior authorization. Others operate in the mid cycle, supporting coding, documentation and claims. At the back end, AI is applied to denials, appeals, accounts receivable and revenue recovery.
The scale of the back end problem is significant. According to a KFF analysis of ACA Marketplace claims data, insurers on HealthCare.gov turned down roughly one in five in network claims in 2024, and a missing prior authorization or referral accounted for nine percent of those denials.
Quick answer: the five platforms covered here are Infinitus, Notable, athenahealth, Waystar and DataRovers. They represent different approaches to healthcare RCM automation, from payer communication and patient access through coding, claims, denials and post denial workflows.
Definitions
What are AI agents in healthcare RCM?
AI agents in healthcare revenue cycle management are software systems designed to analyse information, determine the appropriate next step, and perform or support actions inside an RCM workflow. That is different from traditional automation, which generally follows predefined rules: if X happens, do Y.
An AI agent can weigh several pieces of information, interpret the situation, and decide what should happen next. In healthcare RCM that could mean checking insurance information, contacting a payer, reviewing documentation, identifying a coding issue, analysing a denied claim, determining the next action, preparing an appeal, or spotting recurring denial causes.
The useful question is not whether a vendor uses AI. It is which part of the revenue cycle the agent can actually act on.
Market map
The healthcare RCM AI agent landscape
Four stages, four different problems, and five platforms that each solve a different one.
Front end
Eligibility, benefits, patient access and prior authorization. The objective is making sure patient and coverage information is correct before care and claims move further down the cycle. This stage is also under regulatory change. Under the CMS Interoperability and Prior Authorization Final Rule, CMS-0057-F, affected payers must implement certain provisions by January 1, 2026, with most API requirements following by January 1, 2027.
Mid cycle
Documentation, coding, charge capture and claim preparation. The focus is creating accurate clinical and financial information before claims are submitted.
Back end, and the post denial workflow inside it
Claims, denials, appeals, accounts receivable and revenue recovery. Within that sits a narrower workflow: denial, analysis, root cause, recommended action, appeal. That is where the DataRovers RCM AI Agents operate.
Side by side
Quick comparison of the five platforms
| Platform | Primary position | Main capability | Key use cases | Typical fit |
|---|---|---|---|---|
| Infinitus | Front end | Voice AI, Eva | Benefits verification, payer calls, PA follow up | Providers, health systems, specialty and patient access teams |
| Notable | Front end and workflow | AI platform | Patient access, RCM workflows, referrals, denial handling | Health systems and provider organisations |
| athenahealth | Mid cycle, ambulatory RCM | athenaOne AI | Coding, insurance, PA calls, denial reduction | Ambulatory and specialty practices |
| Waystar | End to end RCM | AltitudeAI | Claims, denials, documentation, appeals, payment workflows | Providers, hospitals, health systems, enterprise RCM |
| DataRovers | Back end, post denial | Denials360 AI Agents | PA denial assessment, action plans, denial analysis, appeals | RCM, reimbursement and denial management teams |
Platform one
Infinitus, AI agents for payer communication
Primary focus: front end RCM.
Infinitus concentrates on automating payer and pharmacy communication through AI voice agents. Its digital assistant, Eva, performs benefits verification, prior authorization follow up and prescription support, and Infinitus says its health system solution can automate calls to commercial and government payers and to pharmacies.
Its prior authorization offering is focused on what happens after a PA has been submitted, including calling payers for status, requirements and updates, across specialty medications, procedures and diagnostic tests.
| Dimension | Detail |
|---|---|
| Key capabilities | Benefits verification, eligibility information, payer phone calls, PA follow up and status, prescription support |
| Where it fits | Insurance and benefits, then payer communication, then authorization follow up |
| Typical teams | Patient access, benefits verification, prior authorization, specialty pharmacy, revenue cycle operations |
| What is different | Automating the payer interactions that traditionally consume staff phone time |
Platform two
Notable, AI workflow automation across patient access and RCM
Primary focus: front end and administrative workflows.
Notable takes a broader workflow automation approach. Its AI platform is used across patient access, referral management and revenue cycle workflows, with capabilities extending into documentation requests and denial handling. Notable also offers voice AI for patient facing workflows.
| Dimension | Detail |
|---|---|
| Key capabilities | Patient access, referral management, administrative and revenue cycle workflows, voice AI, denial related workflows, patient communication |
| Where it fits | Front end, then administrative workflow, then selected RCM workflows |
| Typical teams | Patient access, revenue cycle, operations, referral management, contact centres |
| What is different | Positioned as a broad healthcare workflow platform rather than an agent aimed at one RCM problem |
Platform three
athenahealth, AI native RCM for ambulatory care
Primary focus: mid cycle and ambulatory RCM.
athenahealth has expanded AI capabilities directly into its athenaOne platform. In 2026 the company introduced a broad set of AI native RCM features targeting insurance, coding, prior authorization and claim denials.
| Dimension | Detail |
|---|---|
| Key capabilities | Coding automation, insurance workflows, documentation support, prior authorization calls, claim workflows, denial reduction |
| Where it fits | Mid cycle, then claims, then selected back end workflows |
| Typical teams | Medical billing, coding, practice operations, revenue cycle, specialty practice administration |
| What is different | The AI is built into the existing athenaOne environment rather than sitting as a separate layer |
Platform four
Waystar, agentic AI across the revenue cycle
Primary focus: end to end RCM.
Waystar is one of the broader platforms in this comparison. Its AltitudeAI platform applies AI across the healthcare payment lifecycle, and in 2026 Waystar expanded its agentic capabilities to include claim resolution, clinical documentation and patient financial experiences.
| Dimension | Detail |
|---|---|
| Key capabilities | Eligibility, prior authorization, documentation, coding, claims, denial prevention and recovery, clinical appeals, recoupment management, patient financial workflows, agentic claim resolution |
| Where it fits | Front end, mid cycle and back end |
| Typical teams | Revenue cycle, patient access, coding, clinical documentation, denial management, finance, hospital operations |
| What is different | AI connected to an existing payment network and end to end RCM infrastructure rather than a single point in the cycle |
Platform five
DataRovers, AI agents for post denial workflows
Primary focus: back end RCM and denial resolution. The workflow starts after the denial.
DataRovers does not perform front end prior authorization submission or manage the whole authorization lifecycle. Its RCM AI Agents for prior authorization denials and appeals analyse what happens after a claim or prior authorization has been denied. The platform reads denied claims across a queue, identifies root causes, and recommends an action for each specific denial.
Step 1, denial assessment
The agent analyses the denied claim or prior authorization denial, examining the denial reason, claim information, CARC and RARC information where available, clinical documentation, payer requirements and other supporting information. The goal is not simply to classify the denial. It is to determine what caused it and what should happen next.
Step 2, root cause analysis
The agent analyses denial patterns across the claim population rather than one case at a time, identifying recurring causes by payer, specialty, facility, denial reason, procedure or claim characteristics.
Step 3, action plan
After the analysis, the Action Plan Agent recommends a next step based on the denial and its root cause. Some cases need additional information, some need a different operational action, and some are appropriate for an appeal. The agent moves beyond explaining why a claim was denied and says what to do about it.
Step 4, prior authorization denial assessment
This is the important distinction. DataRovers is not a front end prior authorization platform. It enters when the authorization has already been denied and the question is what to do now. For softer or potentially addressable denials the agent identifies possible actions rather than treating every denial as an appeal. For a deeper look, see our 2026 guide to AI agents for prior authorization denials.
Step 5, appeal agent
When an appeal is the right next step, the Appeal Agent analyses the denial, determines a strategy, reviews relevant payer policies and available clinical notes, identifies supporting information, prepares payer specific forms, drafts the appeal, runs a quality and compliance check, provides an appeal quality score, and flags opportunities for the analyst to strengthen it. The result is an appeal ready package for analyst review.
Step 6, appeal quality check
The process does not stop when the appeal is written. The generated appeal goes through a check that returns a score, identified opportunities and recommendations for improvement, giving the analyst another layer of review. DataRovers supports the analysis and preparation of the appeal. It does not claim to track the appeal through the payer lifecycle after submission.
Capability comparison
DataRovers compared with the other RCM AI agents
Coverage by stage first, then the full capability detail.
| Capability | Infinitus | Notable | athenahealth | Waystar | DataRovers |
|---|---|---|---|---|---|
| Eligibility and benefits | Yes | Yes | Yes | Yes | No |
| Front end PA workflows | Yes | Yes | Yes | Yes | No |
| Payer communication | Yes | Yes | Yes | Yes | No |
| Coding | No | No | Yes | Yes | No |
| Claim workflows | No | Yes | Yes | Yes | No |
| Denial analysis | No | Yes | Yes | Yes | Yes |
| PA denial assessment | No | No | Yes | Yes | Yes |
| Root cause analysis | No | Yes | Yes | Yes | Yes |
| Denial based recommendations | No | No | Yes | Yes | Yes |
| Action plans | No | No | Yes | Yes | Yes |
| Appeal strategy | No | No | Yes | Yes | Yes |
| Clinical notes analysis for appeals | No | No | Yes | Yes | Yes |
| Payer policy analysis | No | No | No | Yes | Yes |
| Payer specific appeal forms | No | No | No | Yes | Yes |
| Appeal generation | No | No | Yes | Yes | Yes |
| Appeal quality scoring | No | No | No | No | Yes |
| Analyst improvement recommendations | No | No | No | No | Yes |
| End to end RCM | No | Partial | Partial | Yes | No |
| Post denial specialist | No | No | No | Partial | Yes |
Yes is retained only where the vendor publicly documents that capability. No means the capability is not part of the primary documented positioning reviewed for this comparison, rather than proof that the vendor cannot perform it.
Fit
Who are these AI agents designed for?
The right agent depends on the organisation's size, workflow and the RCM problem it is trying to solve.
| Platform | Organisational fit | Primary teams | Primary workflow |
|---|---|---|---|
| Infinitus | Provider organisations, health systems, specialty operations | Patient access, PA, benefits | Payer communication |
| Notable | Health systems and provider organisations | Patient access, operations, RCM | Workflow automation |
| athenahealth | Ambulatory and specialty practices using athenaOne | Billing, coding, practice operations | Mid cycle and ambulatory RCM |
| Waystar | Providers, hospitals and large health systems | RCM, finance, coding, denials | End to end payment lifecycle |
| DataRovers | RCM organisations, hospitals and health systems with significant denial workloads | Denials, reimbursement, appeals, RCM | Post denial analysis and action |
Rather than defining organisations only by employee count, it is more useful to look at workflow complexity and claim volume. A large hospital system may need end to end RCM infrastructure. A specialty practice may need coding and authorization automation. A high volume denial operation needs something different again: an agent that can analyse denied claims at scale and decide what should happen next.
Front end, mid cycle and post denial AI, side by side
| RCM stage | Main question | Typical AI use |
|---|---|---|
| Front end | Can we get the patient financially and administratively ready for care? | Eligibility, benefits, patient access, prior authorization |
| Mid cycle | Is the encounter documented and coded correctly? | Documentation, coding, charge capture |
| Claims | Can we submit a clean and accurate claim? | Claim validation and correction |
| Post denial | Why was this denied and what should happen next? | Denial analysis, root cause, action plans |
| Appeal | Should this be appealed, and how should the appeal be prepared? | Strategy, payer policy, clinical documentation, appeal generation |
Evaluation
What should healthcare organisations look for?
Start with the workflow, not the AI label.
What part of RCM does it actually automate?
A platform built for eligibility verification solves a different problem from an agent built for denied claim analysis.
Does it analyse healthcare specific data?
Look for the ability to work with clinical, claims, payer and RCM information rather than relying on generic text generation.
Can it recommend an action?
Identifying a denial is useful. Determining what should happen next is far more operationally valuable.
Does it support human review?
Complex revenue cycle decisions still need analyst oversight. A good workflow makes the analyst's job easier while keeping review and control in place.
Can it operate at claim volume?
Judge an agent not only on what it does with one claim, but on how it performs across a full queue.
Can you measure the impact?
Useful measures include staff productivity, denial turnaround time, appeal preparation time, denial recovery, appeal quality, manual touches, revenue recovered and root cause visibility.
Outlook
Where AI agents in healthcare RCM are heading
The direction is moving from assistance toward action. Earlier automation focused on helping staff find information. The next stage is agents that understand, decide, act and review, operating inside individual workflows rather than sitting apart as a chatbot or a dashboard.
The opportunity is not necessarily to automate the whole revenue cycle with one platform. Front end teams may need payer communication and eligibility automation. Mid cycle teams may need coding and documentation support. Back end teams need denial analysis, action plans and appeals. That makes the future less about one universal agent and more about specialised agents inside specific workflows.
FAQ
Frequently asked questions
What are AI agents in healthcare revenue cycle management?
AI agents for healthcare RCM are software systems that analyse healthcare, payer and financial information and then perform or recommend actions inside revenue cycle workflows. They can support eligibility, coding, claims, denial management, appeals and payment workflows.
How are AI agents used in healthcare RCM?
Across the cycle: eligibility and benefits verification, prior authorization workflows, coding, claim preparation, denial analysis, appeals and payment related workflows.
What is the difference between RCM automation and AI agents?
Traditional RCM automation follows predefined rules. AI agents analyse information, determine an appropriate action, and execute or recommend the next step within a workflow.
Can AI agents manage healthcare claim denials?
Yes. They can analyse denial information, identify patterns and root causes, recommend next actions and, depending on the platform, support appeal preparation and recovery workflows.
Can AI agents handle prior authorization denials?
Yes. Some systems focus on what happens after a prior authorization has been denied, analysing the denial, identifying possible next steps and, where appropriate, supporting an appeal. Our guide to AI agents for prior authorization denials covers this in detail.
Does DataRovers perform front end prior authorization?
No. DataRovers is not a front end prior authorization submission platform. Its workflow begins after a prior authorization or claim denial and focuses on denial assessment, root cause analysis, recommendations, action plans and appeal preparation.
What does the DataRovers Appeal Agent do?
It analyses the denial, determines an appeal strategy, reviews relevant payer policies and clinical notes, prepares payer specific forms and drafts the appeal. It then runs a quality and compliance check and returns a score with recommendations for the analyst.
Does DataRovers track appeals after submission?
No. The workflow described here covers denial analysis and appeal preparation. It is not positioned as an end to end appeal tracking system after the appeal has been submitted.
Final takeaway
The market is not one category
- Infinitus focuses on payer communication and front end administrative work.
- Notable applies AI to patient access and broader healthcare workflows.
- athenahealth is embedding AI across its ambulatory RCM environment.
- Waystar is expanding agentic AI across the end to end payment lifecycle.
- DataRovers focuses on the post denial workflow, analysing denied claims, identifying root causes, recommending actions and preparing appeals.
So the question is not which AI agent is best. It is which part of your revenue cycle creates the biggest operational and financial bottleneck, and what an agent can actually do inside that workflow. If that bottleneck is denied claims or denied prior authorizations, see how DataRovers RCM AI Agents analyse denials, build action plans and prepare appeal ready packages for analyst review.