If you run a clinic or hospital network, you already know the cost of missed appointments. The average no-show rate across US healthcare sits between 5–30 percent, depending on specialty. Each empty slot represents lost revenue, idle provider time, and delayed patient care. The standard fix, calling patients one by one to confirm or reschedule, doesn’t scale.

In this post, you will learn how to build a voice agent that handles appointment reminder conversations using Amazon Nova 2 Sonic and Amazon Bedrock AgentCore. The agent authenticates patients by voice, manages appointments (confirm, cancel, or reschedule), collects pre-visit health information, and escalates to human staff when needed. You handle routine calls at scale, which can help reduce no-show rates. This sample focuses on the agentic side of the problem: voice conversation and tool orchestration. A browser-based interface is included for testing. To connect the agent to actual phone lines for outbound dialing, you would integrate a telephony service such as Amazon Connect Customer.

This walkthrough takes you through the full build. You get a working voice agent on Amazon Bedrock that conducts natural appointment conversations using the speech-to-speech capabilities of Amazon Nova 2 Sonic, plus seven healthcare-specific tools built with the Strands Agents SDK for patient authentication, scheduling, and escalation. The serverless deployment runs on Amazon Bedrock AgentCore with Amazon Cognito authentication, Amazon DynamoDB persistence, and Amazon Simple Notification Service (Amazon SNS) notifications. A browser-based React frontend streams bidirectional audio over authenticated WebSocket connections.

To automate appointment calls effectively, you need a conversational AI system that handles multiple steps in a single interaction: verifying identity, presenting appointment details, processing changes, and collecting health information.

A traditional approach chains together separate services. A speech-to-text model transcribes the patient’s words, a text-based large language model (LLM) generates a response, and a text-to-speech model reads it back. Each handoff introduces latency and drops context. The transcription step discards vocal cues like tone, hesitation, and urgency. The LLM doesn’t hear how the patient said something, only what they said. In healthcare, where a patient’s anxiety or confusion should change how the agent responds, that loss matters.

Amazon Nova 2 Sonic is a speech-to-speech model available on Amazon Bedrock that enables real-time conversational AI with bidirectional streaming. Instead of chaining separate transcription, reasoning, and synthesis services, Nova 2 Sonic processes speech natively in a single model—so vocal context like tone and pace isn’t lost to transcription. For more information about how Nova 2 Sonic processes speech and adapts to acoustic context, see the Introducing Amazon Nova Sonic blog post.