AI-Powered Call Center Agents for Healthcare Operations

heyrevia · Healthcare · 2023 · source Agentic: yes · promotional bias: med · source-backed · extracted with prompt 0.5.4 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Healthcare call-center operations

HeyRevia targets repetitive healthcare phone operations where providers and insurers still coordinate credentialing, prior authorization, claims, and benefits questions through long phone calls.

Constraints:

healthcare operations still rely heavily on phone calls
calls can involve IVR menus, hold music, and human representatives
calls can last around 30 minutes
voice agents must be controllable in production
healthcare data retention, client isolation, and vendor compliance are mandatory

Architecture

HeyRevia structures healthcare phone automation like an autonomous-control stack: telephony and speech services feed perception, planning decides call actions, control handles IVRs/hold/humans, and client-isolated hosting plus takeover keep it operable in healthcare.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  telephony-integration["Telephony integration"]
  perception-layer["Perception layer"]
  planning-layer["Planning layer"]
  call-control-layer["Call-control layer"]
  hold-state-controller["Hold-state controller"]
  speech-services["Speech services"]
  client-isolated-model-hosting["Client-isolated model hosting"]
  human-takeover["Human takeover"]
  telephony-integration --> perception-layer
  speech-services --> perception-layer
  speech-services --> call-control-layer
  perception-layer --> planning-layer
  perception-layer --> hold-state-controller
  hold-state-controller --> planning-layer
  planning-layer --> call-control-layer
  call-control-layer --> human-takeover
  client-isolated-model-hosting --> speech-services
  client-isolated-model-hosting --> planning-layer
  human-takeover --> telephony-integration

Drag to pan · scroll or use +/− to zoom · reconstructed from the source's described component topology

Components

Component	Role	Interacts with
`Telephony integration`	Connects AI agents to healthcare phone workflows and IVRs.	`Perception layer`
`Perception layer`	Detects IVR menus, hold music, human representatives, and audio state.	`Planning layer`, `Hold-state controller`
`Planning layer`	Decides next call steps based on call objective and heard context.	`Call-control layer`
`Call-control layer`	Presses buttons, waits, negotiates, or hands off during calls.	`Human takeover`
`Hold-state controller`	Pauses expensive processing while waiting on hold.	`Planning layer`
`Speech services`	Provide speech-to-text and text-to-speech under healthcare compliance constraints.	`Perception layer`, `Call-control layer`
`Client-isolated model hosting`	Keeps client data, retention, and training boundaries isolated.	`Speech services`, `Planning layer`
`Human takeover`	Allows operators to jump into a call when needed.	`Telephony integration`

Data flow

The voice agent understands IVRs and call objectives, detects hold and human states, pauses processing during hold, and can let a human jump into the call.

3 supporting quotes

“our AI actually understand all the ivrs so how it works is like it it understand the why you're making the call it's trying to understand like what are the manual items when it's listening into those phone calls”transcript, stated

“once it knows it's we and hold it going to pause all the processing powers or all the larer powers for that phone call it's just like going to sa silently and wait for the human to get get into the phone call”transcript, stated

“since we process all the information in real time and we work make AI agent like a human it's actually you can actually jump into the call like take over the phone call”transcript, stated

Why this architecture

The architecture emphasizes control and compliance because fluent audio alone does not solve production voice-agent operation in healthcare.

“if you have seen all the newer uh open air real time API you realize they can communicate really fluent uh Ino conversations but then you get a pro question of with all those like audio input and audio output how do you actually control the voice agent in production”transcript, stated

“how do you make sure all your uh solution providers like no matter is like speech to text text to speech all the service like vendors providing service also are suck to compliance and keep compliance”transcript, stated

Models used: large language model · speech-to-text models · text-to-speech models

Scale

The source frames phone calls as more than 30% of healthcare operations, often including long calls around claims, prior authorization, and benefits.
“that's more than 30% operations for the healthare are running on phone calls those phone calls can including um very simple calls like your appointments and the checkings”transcript, stated
The source frames phone calls as more than 30% of healthcare operations.
“that's more than 30% operations for the healthare are running on phone calls those phone calls can including um very simple calls like your appointments and the checkings”transcript, stated
Healthcare calls can include roughly 30 minutes of hold time that would otherwise waste human attention and model processing.
“if you are putting on hold for 30 minutes there's nothing you can do right you just a wasit waste your time there but for Rivia it knows it's talk to it knows is it's we and hold”transcript, stated

Alternatives considered (roads not taken)

Keep full model processing active during long hold times

Rejected because: HeyRevia pauses large-model processing while on hold to reduce token waste and cost.

“it's going to save you a ton of token just by predicting okay we should be we and hold for for uh for the human to answer the call”transcript, implied

Shared hosted model state across clients

Rejected because: The system hosts larger models with retention policies so client knowledge is only used for one client at a time.

“we post all the larger model ourself uh so we and we have a retention policy for all of them make sure like their data and all the AI training on their like knowledge going to only used for one client at a time”transcript, stated

Treating voice fluency as sufficient production control

Rejected because: The source says fluent realtime APIs still leave the problem of controlling voice agents in production.

Shared model learning across healthcare clients

Rejected because: HeyRevia hosts models and retention policies so one client's knowledge is only used for that client.

Patterns

`tool-use-orchestration` (orchestration)

The call agent coordinates telephony, speech recognition, IVR button presses, hold detection, and human conversation as a single production workflow.

`cost-aware-routing` (operations)

The system detects hold time and pauses LLM processing until a human returns, saving tokens during long calls.

`privacy-preserving-processing` (safety)

HeyRevia describes client-specific retention and training isolation so healthcare data is not shared across providers or patients.

“we have a retention policy for all of them make sure like their data and all the AI training on their like knowledge going to only used for one client at a time”transcript, stated

`human-in-the-loop-gate` (safety)

Because the system stays in the live phone call, a human operator can intervene and take over the call when needed.

“since we process all the information in real time and we work make AI agent like a human it's actually you can actually jump into the call like take over the phone call”transcript, stated

Tradeoffs

How to handle long healthcare calls economically.

Chose state-aware pausing during hold periods over continuous LLM processing for the entire call.

Rationale: The system saves token spend by recognizing that no reasoning is needed while waiting on hold.

“it's going to save you a ton of token just by predicting okay we should be we and hold for for uh for the human to answer the call”transcript, stated

How to satisfy healthcare data requirements.

Chose self-hosted large models with retention policies and client separation over shared model training or cross-client data reuse.

Rationale: The speaker presents data isolation as a core requirement for healthcare providers and patients.

Gotchas (what bit them)

Voice agents need production control beyond fluent audio generation.

Root cause: Real phone workflows require streaming input, ASR, LLM reasoning, TTS, telephony state, and call control to stay coordinated.

Resolution: Use an explicit perception/planning/control architecture rather than treating voice as a single prompt-response loop.

Hold time can waste both human attention and model tokens.

Root cause: Healthcare calls often include long periods where nobody is speaking to the caller.

Resolution: Detect the hold state, pause expensive processing, and resume when a human representative joins.

“if you are putting on hold for 30 minutes there's nothing you can do right you just a wasit waste your time there but for Rivia it knows it's talk to it knows is it's we and hold”transcript, stated

Healthcare voice automation has compliance requirements extending beyond the LLM itself.

Root cause: Speech-to-text, text-to-speech, and other vendors also handle regulated information.

Resolution: Require client-isolated data handling and compliance from every provider in the voice stack.

Evaluation

The source is a founder presentation and Q&A with qualitative claims and a small comparison against human call resolution, rather than a controlled benchmark.

Metrics: more than 30% of healthcare operations run on phone calls · AI handles 30-minute phone calls · some claims take humans two to three calls and the AI one to two calls

“that's more than 30% operations for the healthare are running on phone calls those phone calls can including um very simple calls like your appointments and the checkings”transcript, stated

“for some of the claims for humans us take probably two to three phone calls for them to actually find the right reason to why they get denied but for us like only we take up one to two costs”transcript, stated

Outcomes

HeyRevia reports that its agent can navigate IVRs, wait on hold, and negotiate with human representatives.
The founder claims the AI can resolve some claims in fewer calls than humans.
The source does not provide audited production metrics.

Auto-caption transcript; several terms are noisy, and the claims are from the company founder rather than an independent healthcare customer study.

“once the human enter the call is first trying to understand you are is talking to a human then it start to based on the object you have said to the uh agent it will trying to negotiate on your behalf with the human agent”transcript, stated