How Voice AI Really Works in Healthcare

Demystifying Voice AI Agents

"Hey, this is Erin, I need to see Dr. Sanders this week. My psoriasis is really flaring up and I need to see him asap."

To a human, that’s a straightforward request. To a voice AI agent, it triggers a rapid series of advanced steps designed to understand, interpret, and respond (accurately and empathetically) in less than a second. 

So, how does it happen? Let’s follow the journey of this one sentence through the cascade of voice AI, and break down how each layer, from listening → understanding →  responding, works together to transform patient communication.

Speech-to-Text (STT) - The Listening Ear 

The journey begins with listening. As soon as a patient speaks, the system’s first job is to detect language and transcribe speech into accurate text. But this isn’t basic dictation. Healthcare-specific STT systems are trained on real medical multilingual conversations, allowing the voice AI agent to: 

• Recognize clinical terms like psoriasis.

• Filter through background noise, varying speech patterns, and accents.

• Detect tone of voice, urgency, and subtle emotional cues.

Getting this step right is critical because even minor misinterpretations (like confusing medical terms or similar-sounding names) can derail the entire interaction downstream. 

Large Language Model (LLM) -  The Understanding Brain 

Once a patient’s speech is transcribed, the next step is understanding. This is where large language models (LLMs), the technology that powers tools like ChatGPT, act as the brain of the system. 

Unlike older automated voice systems that rely on strict rules or keyword matching, LLMs use natural language understanding to grasp meaning, context, and intent, resulting in a more flexible and robust interaction.

Here’s what that looks like in action: 

• When a patient says “my psoriasis is acting up,” the LLM interprets that as a signal of urgency and discomfort.

• It identifies “Dr. Sanders” as the patient's provider and recognizes the intent to schedule an appointment.

• It then connects to multiple knowledge systems to retrieve availability, verify records, and adheres to the complex scheduling rules a practice has to prepare the next best action.

• It maintains context throughout the entire conversation, remembering previously mentioned symptoms or preferences without needing repetition.

However, for this to succeed, the LLM must avoid "hallucinations" - fabricated or misleading responses - that occur when the system isn’t trained on healthcare-specific data.

What sets effective voice AI apart is its ability to integrate directly with healthcare systems, using EHR connections to both access and update patient information in real time. This integration enables the AI to read a patient’s visit history and personalize the interaction by offering up the same location as their last visit or proactively sending a text reminder for their annual wellness visit. 

Text-to-Speech (TTS) - The Responsive Voice 

Once the agent knows what to say, the final step is delivering a response that sounds natural, human, and easy to understand. 

Unlike early voice systems that spoke in robotic monotones, today’s TTS technology is designed to create remarkably human speech by:

• Using natural speech flow and intonation that knows when to pause and slow down for important details like appointment times or rising intonation to emphasize a question. 

• Adding emotional touches (“I understand”) to show care and attentiveness. 

• Incorporating delightful human details like the sound of typing or casual acknowledgements (“uh-huh”). 

Here’s how that might sound during a real interaction with Parakeet Health’s voice AI agent: 

“I see here Dr. Sanders is available Thursday morning at 9 AM. She’s also free later in the afternoon at 3 and 4:30. Want me to book one of those for you?” 

Small details like well-timed pauses, mimicking the speaker's pace, the sound of typing, and expressive inflection make a big difference. Without them, the conversation feels stiff and mechanical instead of supportive and patient-centered.

Building Conversations Patients Can Trust

Every step in the voice AI cascade must work seamlessly to deliver the experience patients expect today. When done right, it goes beyond call automation to build trust, improve access to care, and reduce the operational burden on healthcare teams. 

At Parakeet Health, we design our voice AI agents for accuracy and efficiency, but also to feel human. Trust shouldn’t be optional in healthcare communication. 

We’ve invested extensive engineering effort in ensuring fast, accurate responses, and building robust EHR integrations – so patients get the help they need without friction. 

-Aaron Lee, CTO @ Parakeet Health