Build a Custom Voice AI for Automated Patient Follow-Ups
Choose a voice AI provider based on their API's reliability and integration options. A custom-built system using a core API offers more control than a SaaS platform.
We built a follow-up system for a 7-provider orthopedic group needing to confirm appointments and collect post-op pain scores. The system was live in 3 weeks, integrated with their practice management software, and now handles over 200 daily calls automatically.
The choice is between a rigid, all-in-one patient engagement platform and a flexible system built on foundational APIs. The custom route is for clinics needing specific call scripts, direct EMR integration, and no per-user pricing. It requires an engineering partner to build and deploy.
What Problem Does This Solve?
Many clinics first try an all-in-one patient engagement platform. These platforms bundle texting and email reminders, but their voice capabilities are often an afterthought. They typically charge per provider per month, a cost that becomes substantial for a group practice needing a single, specific feature.
The main failure is rigidity. These platforms use template-based call scripts that cannot handle conditional logic. A script that needs to ask a patient their pain score, and if the score is over 7, offer to transfer them to a nurse, is impossible. The system is designed for simple "Press 1 to confirm" interactions, not dynamic, multi-turn conversations.
Consider a 4-location physical therapy practice. They wanted to automate post-visit check-ins, asking about exercise completion and pain levels. Their existing platform could only send a generic text message. It had no API for voice, no way to parse a patient's spoken response, and no function to transfer the call to the front desk if the patient reported high pain. They were paying for a communication suite but still had to do the most critical follow-ups by hand.
How Does It Work?
Our process starts with mapping your exact call flows into a state machine. We define the script, the questions, and the logic for every possible patient response. We use the Claude API to generate conversational, natural-sounding dialogue variants for each step, so the interaction feels less robotic.
We build the core system using Python and FastAPI, integrating two key APIs. We use Twilio's Programmable Voice API to programmatically initiate and manage the calls. For the voice itself, we use the ElevenLabs API for its high-quality, low-latency text-to-speech synthesis, ensuring response times under 400ms to avoid awkward pauses.
The FastAPI application runs on AWS Lambda, providing a serverless architecture that scales with call volume. It manages the conversation logic and uses a Supabase database to track the state of each call. This means if a call to a patient with poor reception drops, the system can automatically retry 15 minutes later, picking up where it left off. This architecture handles over 500 concurrent calls for a regional health client.
Finally, we connect the system directly to your EMR or practice management system's API. The service pulls the daily schedule of patients to call and writes outcomes back to the patient record in real time. A confirmed appointment is updated instantly. A high pain score is flagged in the EMR for a nurse to review. The entire system is built and deployed in 3 weeks and typically costs under $75 per month to operate.
What Are the Key Benefits?
Live in 3 Weeks, Not 6 Months
From scripting to a live system making calls in 15 business days. Avoid the lengthy implementation cycles of large, enterprise patient engagement platforms.
Pay for Usage, Not for Seats
A one-time build cost followed by low, pay-as-you-go API fees. Your monthly bill is based on call minutes, not your provider headcount.
Your Scripts, Your Logic, Your Code
You get the complete Python source code in your own GitHub repository. There is no vendor lock-in; you own the system you paid to build.
Real-Time Alerts for Failed Calls
We configure structured logging with structlog and alerts that fire if API dependencies fail or the call completion rate drops below 95%.
Connects Directly to Your EMR
Direct API integration with your existing patient management system. We write call outcomes directly to the patient chart, eliminating manual data entry.
What Does the Process Look Like?
Workflow Mapping (Week 1)
You provide your ideal call scripts and read-only API access to your patient scheduling system. We deliver a detailed call flow diagram for your approval.
Core System Build (Week 2)
We build the FastAPI service, integrate the Twilio and ElevenLabs voice APIs, and set up the Supabase database. You receive a recorded demo of a test call.
Integration and Testing (Week 3)
We connect the service to your EMR, test the end-to-end flow with a batch of non-patient numbers, and deploy to AWS Lambda. You receive the production system access.
Launch and Monitoring (Weeks 4-6)
We go live with a small patient cohort, monitor call success rates, and fine-tune scripts. You receive the full source code and a technical runbook.
Frequently Asked Questions
- How much does a custom voice AI system cost?
- The cost depends on the complexity of the call script and the quality of your EMR's API documentation. A simple appointment confirmation bot is a 2-week build. A multi-turn survey with conditional logic and escalations is typically a 4-week project. We provide a fixed-price quote after our initial discovery call.
- What happens if the AI misunderstands a patient?
- We program the system to handle misinterpretations gracefully. After two attempts to understand a response, it will say, 'I'm having trouble understanding, let me connect you to a staff member.' The call is then transferred to your front desk. This ensures no patient gets stuck in a frustrating loop.
- How is this different from a platform like Solutionreach?
- Solutionreach is an excellent tool for broadcast messaging and simple reminders. Syntora builds custom applications for business operations. We build systems that can have complex, multi-step conversations, write structured data back to your EMR, and execute conditional logic that is specific to your clinic's workflow.
- Is this system HIPAA compliant?
- Yes. We exclusively use HIPAA-eligible services on AWS and will sign a Business Associate Agreement (BAA). All patient data is encrypted in transit and at rest. The system is designed to be stateless, processing PHI without storing it long-term. Call recordings are disabled by default to minimize compliance surface area.
- Will this sound like a generic, robotic voice?
- No. We use modern neural text-to-speech APIs from ElevenLabs that produce natural, human-sounding voices with appropriate intonation. We can select from a library of voices to find one that matches your practice's tone or even clone a specific voice, with consent, for a truly unique caller experience.
- How difficult is it to change the call script later?
- The scripts are stored in a simple configuration file within the codebase. A developer can change a sentence in under 15 minutes. Adding a new question or a new logical branch might take 1-2 hours. Since you own the source code, your team or any Python developer can make these modifications without our involvement.
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