We need to reduce call volume between providers and payers. What AI agents can answer eligibility and claim questions?

Yes. If the objective is specifically reducing provider↔payer call volume, there is now a meaningful category of AI agents built for exactly this—not just generic healthcare chatbots.

Best-fit AI agent categories

Vendors I'd put on an initial shortlist

For a payer/TPA trying to answer provider calls:

• Opyn — particularly interesting because its Provider Interaction Agent explicitly handles eligibility & benefits, claims, and prior-auth intake through voice or chat. It is designed to answer provider questions without requiring portal logins.
• EHVA — very directly targeted at this problem. It reports an 81% autonomous resolution rate for in-scope provider calls covering eligibility, benefits and claims in a TPA deployment.
• QuickIntell — offers payer/member-service voice agents for identity verification, eligibility/benefits, claim status and prior-auth status, with connected-system write-back and escalation.
• Sempera AI — voice agents for health-insurance call centers that can verify members, check eligibility and resolve claims questions against live systems.

For a provider/health system trying to stop calling payers:

• SuperDial — one of the strongest fits for outbound payer calls. Its agents work across phone, payer portals, APIs and EDI for eligibility, authorizations, claims and denials.
• Prosper AI — combines API-based eligibility with an agent that calls the payer when the API cannot answer, including navigating IVRs and speaking with payer representatives.
• Substrate AI — more focused on eligibility/coverage and downstream eligibility-denial resolution, combining EDI, payer portals and APIs.
• Tahoma AI — offers separate agents for eligibility, claims, denials, A/R and payments, including payer-portal interaction.

There is also a broader ecosystem—including Infinitus, Notable, Hyro, Waystar, Availity, pVerify and others—covering adjacent payer/provider workflows.

The important distinction

I'd separate the market into two very different architectures:

1. Payer-side "answering agent"

Provider → AI agent → payer eligibility/claims system → answer This is the most direct way to reduce incoming provider calls. The agent authenticates the provider, gathers member/claim identifiers, queries the payer's systems and answers the question.

2. Provider-side "payer-chasing agent"

Provider → AI agent → payer IVR/portal/API → answer → EHR/RCM This reduces outbound calls from providers to payers. The agent does the tedious work that billing staff currently perform.

For your stated goal—reducing call volume between providers and payers—I'd actually evaluate both sides, because deploying an agent on only one side can simply shift the workload to the other.

What I'd prioritize

For a payer, I'd start with an Inbound Provider Agent covering:

1. Eligibility/active coverage
2. Benefits and copay/deductible information
3. Claim status
4. Claim receipt/processing status
5. Denial reason/status
6. Payment/remittance status
7. Prior-auth status
8. "What information is missing?" questions
9. Provider authentication
10. Seamless human escalation for exceptions

The key technical requirement is live transactional access, not an LLM answering from a knowledge base. EHVA, for example, describes querying the administration system during the call rather than relying on static information.

If you're evaluating this as a payer/TPA transformation opportunity, I would benchmark vendors on % of provider calls fully resolved, authentication success, real-time core-admin integration, auditability, PHI controls, escalation rate, and cost per resolved call rather than generic "AI accuracy."

If you tell me whether you're looking at this from the payer side or provider side, I can narrow this to the 5–7 strongest vendors and compare capabilities, integrations, estimated economics, and where each fits in the call flow.

2. Provider-side "payer-chasing agent"

Provider → AI agent → payer IVR/portal/API → answer → EHR/RCM This reduces outbound calls from providers to payers. The agent does the tedious work that billing staff currently perform.

For your stated goal—reducing call volume between providers and payers—I'd actually evaluate both sides, because deploying an agent on only one side can simply shift the workload to the other.

What I'd prioritize

For a payer, I'd start with an Inbound Provider Agent covering:

1. Eligibility/active coverage
2. Benefits and copay/deductible information
3. Claim status
4. Claim receipt/processing status
5. Denial reason/status
6. Payment/remittance status
7. Prior-auth status
8. "What information is missing?" questions
9. Provider authentication
10. Seamless human escalation for exceptions

The key technical requirement is live transactional access, not an LLM answering from a knowledge base. EHVA, for example, describes querying the administration system during the call rather than relying on static information.

Reducing friction and call volume between providers and payers requires agentic AI that can interface directly with electronic health records (EHRs), payer portals, clearinghouses, and voice IVRs.

Leading AI platforms and purpose-built agents addressing eligibility, benefits, and claim-status inquiries are categorized below by how they operate:

• Voice & Phone-Automation AI Agents (Best for automating the actual calls/IVR navigation when digital channels fail):
  • Infinitus AI: Specializes in voice and digital AI agents designed specifically for healthcare and life sciences to handle payor touchpoints. Their agents navigate IVR phone trees, speak with payer representatives or use internal knowledge graphs and direct API integrations to check benefit eligibility, track claim status, and handle prior authorizations.
• Portal-Navigating & Agentic RCM Platforms (Best for deep, source-accurate benefit checks that bypass basic electronic transactions):
  • Honey Health: Uses advanced agentic AI that actively navigates payer web portals directly to retrieve granular, CPT-level benefit information that standard 270/271 electronic data interchanges often miss.
  • Thoughtful AI, Infinx, Notable, and Adonis: Provide AI-native revenue cycle management (RCM) agents built to autonomously handle eligibility verification, patient access workflows, and status tracking across multiple systems.
• Enterprise Health Ecosystem Agents (Best for deep EHR and administrative integration):
  • Oracle Health Agents: Offers specialized capabilities like the Oracle Health Eligibility Verification Agent and Prior Authorization Agent designed to pre-fill information, query payer systems, and give real-time pricing and coverage determinations.
  • UiPath Conversational Agents: Uses voice context grounding and workflow orchestration (via Maestro) to let clinicians query patient eligibility, coverage rules, and authorizations directly, linking data securely from the EHR and payer APIs.
• Augmented Clearinghouses & Traditional Platforms (Best for layered, reliable high-volume processing):
  • Waystar, Experian Health, Availity, and Phreesia: Layer conversational and predictive AI on top of robust, established data clearinghouse frameworks to automate bulk eligibility and status checks before a patient encounter.

If you'd like to narrow this down, tell me:

• Are you looking for a solution embedded in your EHR/workflow or an outbound phone/voice agent to call payers?
• What is your estimated monthly volume of eligibility and claim calls?

To reduce provider–payer call volume, the highest-impact AI agents are usually revenue cycle and payer-interaction agents that can retrieve data, navigate payer workflows, answer routine questions, and escalate exceptions. The best fit depends on whether you want the agent to sit on the provider side, payer side, or act as a shared interoperability layer.

1. Eligibility & benefits verification agents

Goal: Eliminate calls asking “Is the patient covered?” and “What are the benefits?”

These agents can:

• Verify active coverage
• Return deductible, copay, coinsurance, and out-of-pocket details
• Confirm network status
• Identify coverage gaps before scheduling
• Flag likely denial risks

Examples:

• Availity — widely used payer-provider connectivity platform for eligibility and claims workflows.
• Sohar Health — focuses on API-based eligibility and coverage discovery.
• QuickIntell — offers eligibility verification and revenue-cycle agents.

2. Claim status inquiry agents

Goal: Replace provider calls asking “Where is my claim?”

These agents can:

• Check claim status through APIs, portals, EDI, or phone calls
• Retrieve denial reasons
• Identify missing information
• Provide next-step recommendations
• Update provider systems

Examples:

• SuperDial — uses voice and other payer interfaces for eligibility, authorization, and claims workflows.
• Operator Labs — focuses on AI agents that call payers, navigate IVRs, and document outcomes.

3. Denial and appeal agents

Goal: Reduce back-and-forth after claims are rejected.

These agents can:

• Classify denial reasons
• Pull payer policies
• Draft appeal packages
• Track appeal status
• Recommend prevention actions

Examples:

• Adonis — denial prevention and revenue-cycle intelligence.
• Crosby Health — focuses on AI-generated claims appeals.
• Tahoma — offers agents for claims, denials, and accounts receivable workflows.

4. Prior authorization agents

Goal: Reduce provider calls about authorization requirements and status.

These agents can:

• Determine if authorization is required
• Gather documentation
• Submit requests
• Track payer responses
• Answer status questions

Examples:

• Cohere Health
• Rhyme
• Notable These vendors are listed among platforms automating prior authorization and revenue-cycle workflows.

5. Payer call-center provider support agents

Goal: Let providers self-serve instead of calling payer service centers.

A payer-side AI agent can answer:

• Eligibility questions
• Benefit questions
• Claim status
• Prior auth status
• Network/provider questions
• Documentation requirements

Examples:

• Observe.AI — provides healthcare payer AI agents for coverage, benefits, claims-related inquiries, and provider/member support workflows.

Recommended target architecture

For a large payer-provider ecosystem, I would typically prioritize:

1. Eligibility agent

• Highest call volume reduction
• Mostly deterministic data retrieval

2. Claim status agent

• High provider frustration
• Strong ROI because status calls are repetitive

3. Prior authorization status agent

• Major administrative burden

4. Denial resolution agent

• Higher complexity but direct financial impact

5. Conversational provider portal agent

• A single AI interface where providers ask: - “Why was claim 123 denied?”
• “Is authorization required?”
• “What documentation is missing?”

Key capabilities to require

For healthcare payer-provider use cases, look for:

• HIPAA-compliant architecture and BAAs
• EDI/X12 support (270/271 eligibility, 276/277 claim status)
• FHIR/API connectivity where available
• Payer portal navigation
• Human escalation workflows
• Audit logs and transcripts
• Ability to write outcomes back into provider systems

The biggest opportunity is usually not replacing all calls—it is moving routine “information retrieval” calls to AI and reserving human teams for exceptions, disputes, and complex cases.

To cut down on the massive friction and phone tag between providers and payers, healthcare organizations are deploying conversational and agentic AI platforms. These AI agents plug directly into Electronic Health Records (EHRs), clearinghouses, and core payer adjudication engines via APIs and standard X12 transactions (like 270/271 for eligibility and 276/277 for claim status).

Top AI agents and platforms specialized in answering eligibility and claim questions include:

• Infinitus AI : Specialized voice and chat AI agents designed specifically to automate routine touchpoints between providers and payers, handling real-time benefit verification, eligibility checks, and claim status tracking.
• Thoughtful AI : Offers autonomous AI "agents" tailored for revenue cycle management (RCM), including dedicated eligibility and claims verification agents that reduce manual administrative burdens.
• Honey Health : Noted for agentic source-portal verification, capable of navigating complex payer websites and systems autonomously to pull down deep eligibility and benefits data.
• Availity : A massive health information network leveraging AI and automated clearinghouse capabilities to streamline multi-payer eligibility, authorizations, and claim interactions.
• Waystar : Integrates advanced AI and machine learning across its revenue cycle platform to predict claim issues, automate eligibility checks, and supply fast answers to status inquiries.
• Notable Health : Uses intelligent automation and conversational AI agents to orchestrate patient access, capturing real-time coverage details and eligibility without manual intervention.
• Experian Health : Combines automated eligibility verification with coverage discovery tools to find hidden or secondary insurance data quickly.

To help narrow down the right solution, let me know:

• Are you operating primarily from the payer side or the provider side?
• Do you need an agent that handles voice calls/phone trees , or a digital/portal interface?

To meaningfully reduce call volume between providers and payers, healthcare organizations are deploying agentic AI platforms that blend electronic data interchange (EDI/APIs), Robotic Process Automation (RPA), and specialized Voice AI agents.

Standard electronic transactions (like X12 270/271 for eligibility) handle basic checks, but roughly 20 % to 40 % of complex plans, specialty benefits, and detailed claims inquiries still force staff onto the phone. AI agents bridge this gap by executing end-to-end digital lookups or making autonomous phone calls.

Top AI Agent Platforms for Eligibility & Claims

• • Prosper AI (Prosper AI Platform Guide)
  • Best for: End-to-end voice and portal automation handling high-friction payer calls.
  • Capabilities: Features voice agents (such as "Alex") that dial insurers, navigate IVR phone trees, wait on hold, speak naturally with representatives to pull complex eligibility/benefit details (copays, deductibles, accumulators), and write structured data straight back to the EHR.
• • Infinitus (Infinitus Payer Solutions)
  • Best for: Automating routine touchpoints and interactive voice interactions with health plans.
  • Capabilities: Uses conversational voice AI and copilots to quickly check eligibility, verify specific benefit coverage, track claim statuses, and handle complex appeals or prior authorization follow-ups.
• • Infinx (Infinx AI Eligibility Verification)
  • Best for: Combining real-time API clearinghouse data (via partners like Availity) with intelligent escalation.
  • Capabilities: Surfaces copays, deductibles, and visit limits on a centralized dashboard, automatically routing incomplete or edge-case claims to human specialists.
• • SuperDial (SuperDial Voice AI)
  • Best for: High-volume outbound payer-call management for medical billing and RCM teams.
  • Capabilities: Navigates cumbersome phone trees, holds for representatives, and runs eligibility, claim status follow-ups, and credentialing verification with a human-in-the-loop fallback mechanism.
• • Kore.ai (Kore.ai Healthcare Automation)
  • Best for: Enterprise-grade conversational AI across both payer and provider digital front doors.
  • Capabilities: Delivers real-time member coverage details, tracks claim progress, approvals, and denials, and automates pended claim resolutions using specialist reasoning agents.

Key Architectural Approaches to Implement

1. The Hybrid Portal/Voice Layer: Do not rely solely on chat or basic APIs. Use electronic 270/271 batch sweeps for the 60–80% of straightforward requests, and trigger Voice AI agents to handle the nuanced exceptions that typically require phone calls.
2. Human-in-the-Loop Safeguards: Ensure your chosen platform features seamless escalation. When an AI agent encounters a non-standard claim denial or ambiguous policy rule, it should package the transcript, policy context, and a confidence score into a workspace (like UiPath Action Center or an RCM dashboard) for rapid human sign-off.

If you can share whether you are operating primarily from the provider side (clinic/health system) or the payer side (health plan), or tell me your estimated monthly call volume , I can help narrow down the ideal deployment architecture and vendor fit.

Yes. If the goal is specifically to reduce provider↔payer phone volume, there are now several credible AI-agent approaches. The important distinction is whether you want an agent that calls the payer on behalf of the provider, or a payer-side agent that answers inbound provider calls.

Strongest options to evaluate

Infinitus is probably the first company I'd investigate if you're trying to eliminate outbound provider calls. Its agents can verify benefits/eligibility, determine network status and authorization requirements, and handle claims status and appeals-related calls. It combines digital data sources with voice calls when electronic data isn't sufficient.

SuperDial is particularly interesting for a revenue-cycle organization because it explicitly covers eligibility & benefits, authorizations, claims/denials, and provider data across phone, portals, APIs, EDI, documents, and faxbacks.

If you are the payer and the problem is providers calling your call center, EHVA is worth looking at. It is designed specifically to answer provider calls about claims, eligibility and benefits and claims an 81% autonomous resolution rate in one TPA deployment. That's a vendor-reported case study, so I'd validate the number against your own call mix.

The highest-ROI agent types

I'd prioritize these in roughly this order:

1. Eligibility & benefits agent

   • Is coverage active?
   • Effective/termination dates
   • Deductible/OOP remaining
   • Copay/coinsurance
   • In/out-of-network
   • Procedure-specific benefits
   • Referral requirements
   • Authorization requirements

2. Claim-status agent

   • Has the claim been received?
   • Current status/location
   • Pending reason
   • Missing information
   • Adjudication status
   • Payment/check/EFT information
   • Denial reason
   • Appeal/reconsideration status

3. Authorization-status agent

   • Is PA required?
   • Was it submitted?
   • Current status
   • Missing documentation
   • Approval/denial
   • Expiration
   • Appeal status

4. Provider-data agent

   • Network participation
   • Tax ID/NPI
   • Practice/location
   • Credentialing status
   • Directory information

5. Inbound provider-call agent

   • Instead of having providers call a human payer representative, the AI answers the phone, authenticates the caller, retrieves the account/claim, and gives the answer immediately.

One architectural point I'd emphasize

Don't build this as simply a voicebot.

The better architecture is:

Provider → phone/portal/API → AI agent → payer systems / EDI / clearinghouse → answer → audit trail

The AI should first check electronic sources and only use voice/IVR when necessary. Infinitus, for example, explicitly describes collecting digital information before making calls to shorten phone interactions.

That matters because the real objective isn't merely “automate calls.” It's “eliminate the need for the call.”

If you're evaluating this from the payer side, I'd focus the RFP around inbound provider calls. If you're evaluating it from the provider/health-system side, I'd focus on outbound eligibility + claim-status + authorization calls.

I can also give you a vendor-by-vendor comparison of 10–15 companies, including capabilities, integrations, deployment model, evidence of scale, and which ones are actually capable of answering claims/eligibility questions end-to-end.

To reduce administrative phone friction and call volumes between healthcare providers and payers, organizations deploy healthcare-specific conversational and voice AI agents that can autonomously navigate payer interactive voice response (IVR) systems, wait on hold, parse digital clearinghouse/API data, and converse with human representatives to resolve eligibility and claim status. The primary platforms specializing in this payer-provider automation include:

• Prosper AI : Enterprise-grade voice AI agents built to handle complex RCM operations, autonomously navigating payer IVRs for real-time benefits verification, claim status checks, and prior authorization follow-ups with native EHR write-backs.
• Infinitus : Specialized voice AI (featuring Infinitus Eva ) that automates repetitive administrative calls to commercial and Medicare payers to verify coverage, check claim statuses, and process formulary or appeal updates.
• EHVA : Dedicated conversational and voice framework designed explicitly for health insurance carriers, TPAs, and provider networks to field high-frequency provider inquiries regarding member eligibility and claim payments.
• SuperDial : Tailored for revenue cycle management (RCM) companies and billing teams needing automated outbound calling agents to tackle aging claim follow-ups and eligibility verifications.
• Sierra : Conversational enterprise agent platform utilized by major health insurance networks to let members and providers check co-pays, coverage limits, and real-time eligibility instantly.

Core Capabilities of Payer-Provider AI Agents

• Automated IVR Navigation : The AI calls the payer, listens to the menu, inputs data via DTMF tones or natural language, and holds in the queue until a representative picks up.
• API-First Deflection : Systems use real-time EDI/API queries (check first ) to resolve eligibility instantly; phone-based AI agents are only deployed for complex exceptions or when real-time data is missing.
• Structured Data Output : Transcripts and retrieved details (such as copay amounts, deductible accumulations, or claim paid dates) are converted into standardized JSON format for direct EHR or billing system ingestion.
• Secure Escalation : Calls requiring clinical determinations or complex appeals are cleanly transferred to human staff with full conversation context and transcripts attached.

If you'd like to narrow down the right platform, tell me:

• Are you operating primarily on the provider side (making calls to multiple payers) or the payer side (receiving calls from providers)?
• What is your primary EHR or billing system infrastructure?

Yes. If the goal is specifically to reduce provider↔payer phone calls for routine eligibility and claim-status questions, there are now several credible AI-agent approaches. I’d separate them into three categories:

The architecture I'd look at

Rather than having an LLM "answer questions" from a knowledge base, I'd build a payer-side transactional agent:

Provider calls → AI voice agent → identity/provider verification → eligibility/claims system → deterministic answer → call record/write-back

For example:

"Is member John Doe active, what is their deductible, and was claim 123456 received?"

The agent should query the authoritative system in real time, not try to infer the answer from documents. It can then respond with the exact coverage/claim information, provide the claim status, explain a rejection code, and escalate only when the question falls outside its authorized scope.

That's important because the biggest opportunity isn't really "AI answering the phone." It's eliminating the phone call altogether where possible. Availity's newer Extend architecture is explicitly designed around agents executing workflows against live network data, including eligibility and claims.

Where I'd start

For a payer trying to reduce call volume, I'd prioritize these use cases:

1. Eligibility / coverage

   • Active/inactive
   • Effective/termination dates
   • Plan/product
   • Deductible/OOP
   • Copay/coinsurance
   • Benefit limitations

2. Claim status

   • Was it received?
   • Pending/processed/denied?
   • Payment date/amount
   • Denial/rejection reason
   • Missing information
   • Corrected-claim status

3. "Why?" questions

   • Why wasn't this service covered?
   • Why did the claim deny?
   • What information is missing?
   • Does this need a corrected claim?

4. Routing

   • Prior authorization → authorization workflow
   • Complex medical/coverage question → human
   • Provider dispute → appropriate team
   • Fraud/security-sensitive request → human

EHVA is particularly worth benchmarking if your primary KPI is provider calls avoided, because its published case study is unusually close to the problem you described: 13,500 monthly provider calls, with 81% of in-scope calls autonomously resolved.

If you're evaluating this as a payer, I can also give you a shortlist of 8–10 vendors specifically for provider-call deflection, scored on eligibility, claims, voice, FHIR/EDI integration, Epic/Availity integration, HIPAA controls, human escalation, and expected ROI.

To reduce phone friction and cut administrative costs between providers and payers, AI agents specialize across two distinct operational directions: outbound agents (that provider billing teams use to call payer IVRs or portals) and inbound/digital agents (that payers deploy to let providers self-serve data without waiting on hold).

Top HIPAA-compliant AI agent platforms addressing eligibility, benefits, and claim inquiries include:

• Prosper AI : Purpose-built voice AI agents for revenue cycle management (RCM) and patient access. It handles inbound/outbound calls, navigates complex payer IVRs, waits on hold, verifies up to 60 data points for eligibility/benefits, and processes claims status checks with direct EHR write-backs.
• SuperDial : A specialized outbound payer-call automation platform. It is engineered specifically for RCM teams chasing claim statuses and denials, featuring deterministic call flows covering hundreds of payer systems.
• Infinitus : Focuses heavily on interactive voice AI agents and copilots that automate routine touchpoints with payers. It retrieves real-time details on insurance eligibility, benefits, and claim/payment statuses without staff needing to wait on hold.
• Kore.ai : An enterprise conversational AI platform providing prebuilt toolkits for health plans and provider networks. It powers real-time member and provider self-service eligibility trackers, claim status lookups, and benefit verification across digital and voice channels.
• Hyro : A conversational AI provider that unifies voice, web, and SMS. Payers use Hyro to automate coverage and eligibility inquiries, provider network lookups, and EOB explanations, deflecting routine calls away from human contact centers.
• Voicegain Casey : A generative AI voice assistant framework built for healthcare call centers that automates pre-call claim status, benefit inquiries, and eligibility verification, while assisting live agents with real-time transcription and summarization during handoffs.

To help narrow down which platform fits your architecture, are you looking for outbound agents (for your staff to call payers) or inbound self-service (for payers/providers to query your systems)? Let me know your primary EHR or clearinghouse environment (e.g., Epic, athenahealth, Availity) so I can tailor the integration requirements.

Yes. There’s a fairly mature category emerging specifically for payer/provider administrative calls. The important distinction is whether you want AI to answer providers calling your payer organization, or AI to make calls from providers to payers.

If you're the payer: reduce inbound provider calls

These are the most directly relevant vendors I’d evaluate:

EHVA is particularly on-point. It describes agents that answer provider calls, authenticate the caller, query administration systems in real time, and return eligibility, benefits and claim-status information. Its published 2026 case study reports 81% autonomous resolution on in-scope provider calls for a TPA, covering 13,500 monthly calls.

Voicegain markets its Casey platform specifically to health plans, TPAs, UM/CM organizations and PBMs, with automation of routine calls including eligibility, claim status, benefits and prior authorization.

QuickIntell also has a payer-oriented voice agent supporting identity verification, benefits/eligibility, claim status, prior-auth status, escalation and system write-back.

If you're the provider: stop your staff from calling payers

This is a somewhat different, and arguably more crowded, market.

SuperDial is built around automating outbound payer calls for eligibility, benefits verification, prior auth, claim follow-up, credentialing and enrollment.

Operator Labs similarly has agents that dial payer IVRs, wait on hold, interact with payer representatives, capture claim/denial information and put structured results back into the provider's workflow.

The architecture I'd recommend

If you're a payer trying to reduce provider-to-payer calls, I wouldn't start with a generic chatbot. I'd build a Provider Services AI Agent with tightly scoped transactional tools:

1. Caller authentication

   • NPI/TIN
   • provider/facility verification
   • member identifiers
   • appropriate authorization checks

2. Eligibility agent

   • active/inactive
   • effective/termination dates
   • product/plan
   • network status
   • PCP/referral requirements

3. Benefits agent

   • deductible
   • copay/coinsurance
   • remaining accumulators
   • covered/non-covered services

4. Claims agent

   • claim received?
   • current status
   • paid/denied/pended
   • denial reason
   • payment amount/date
   • missing information
   • next action

5. Escalation agent

   • Anything involving interpretation, appeals, disputes, exceptions or policy judgment → human.

The key is that the AI should query the payer's source-of-truth systems, rather than answer from an LLM knowledge base. EHVA explicitly describes this real-time tool-calling model for its provider-call product.

Where I'd focus first

For a payer looking for measurable call reduction, I'd prioritize:

Eligibility → claim status → benefits → payment status → prior-auth status.

Those are repetitive, structured transactions with relatively clear answers. I would not initially let an agent handle complex appeals, medical-necessity questions, contractual disputes, or anything requiring discretionary interpretation.

There's also an important governance issue: AI callers/agents need reliable identity and authorization handling. Recent research on healthcare voice agents found that conversational fluency isn't enough for audit-grade compliance, particularly around verifying and segmenting the different stages of insurance calls.

If you tell me whether you're the payer, provider, or an intermediary—and roughly how many provider calls/month you're dealing with—I can narrow this to 3–5 vendors and give you a buy-vs-build architecture, expected automation rate, and ROI model.