AI Voice Assistant for a Medical Center

Results

Hymux Technologies delivered an end-to-end AI voice assistant for a mid-size medical center to automate patient phone interactions. The solution is capable of handling up to 100–150 calls per day, managing appointment bookings, cancelling visits, answering FAQ, routing calls to operators, and providing real-time updates from the clinic’s internal systems. The product significantly reduced operator workload, improved response times, and increased patient satisfaction.

Customer Background

The medical center receives a large daily volume of calls related to:

• Appointment scheduling
• Doctor availability
• Insurance coverage
• Service descriptions
• Test preparation instructions
• Follow-up recommendations
• Location & working hours
• Repeated inquiries and routine questions
  

The goal was to automate the first line of communication and enable 24/7 patient service, while keeping human operators available for complex cases.

Business Challenge

The client faced several pain points:

• High load on call center operators: Up to 60% of daily calls were simple clarification questions.
• Limited working hours: Patients could not get information at night or during peak times.
• Frequent scheduling errors: Manual entry occasionally caused double-booking and missed cancellations.
• No centralized knowledge base: Doctors, administrators, and call center staff used outdated PDF instructions or verbal communication.
• Need to maintain compliance: The medical center required a solution aligned with HIPAA/GDPR, depending on region.

Solution

Hymux Technologies implemented a AI voice assistant that automatically:

• Receives inbound calls,
• Recognizes patient speech in real time,
• Identifies intent (scheduling / FAQ / cancellation / routing),
• Retrieves registered information via a RAG pipeline,
• Executes booking/cancellation via API integration,
• Generates natural, empathetic spoken responses,
• Hands off to a human operator when needed (fallback),
• Logs all interactions in CRM.

The system is fully scalable and can be deployed on-premise or in the cloud.

Technology Stack

Voice Processing

• Speech-to-Text (STT): Deepgram/WhisperX/Google STT
• Text-to-Speech (TTS): ElevenLabs/Azure Neural Voices
• VAD (Voice Activity Detection): Silero VAD

LLM & Reasoning

• Model: GPT-4/Llama 3 70B (depending on privacy requirements)
• Framework: LangChain for orchestration
• RAG (Retrieval-Augmented Generation):
  • Embeddings: OpenAI/Instructor-xl
  • Vector DB: Milvus/Pinecone

Backend

• Python FastAPI for LLM gateway
• gRPC/REST API for integrations
• PostgreSQL for conversation logs
• Redis for session memory

Telephony

• Asterisk/FreePBX
• SIP Trunk integration
• WebRTC Media Gateway for streaming audio

Ops

• Docker + Kubernetes
• Elastic Stack for logging
• Prometheus + Grafana for monitoring

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Architecture Overview

Core modules delivered:

1. SIP → Media Stream Gateway
   Converts phone audio into low-latency audio for analysis.
2. STT module
   Converts patient speech to text in real-time.
3. NLU with Intent Classification
   Detects core user intents:
   • Schedule appointment
   • Cancel appointment
   • Reschedule
   • FAQ
   • Talk to operator
   • Emergency redirect
4. RAG Knowledge Service
   Provides verified, structured answers from medical center documents:
   • Doctors’ profiles
   • Services
   • Prices
   • Preparation instructions
   • FAQ
   • Work schedules
5. LLM Gateway
   Generates the final, human-like response with safety and medical context enforcement.
6. Appointment Management API
   Integration with medical CRM to:
   • Check free slots
   • Book a visit
   • Send confirmations
   • Cancel appointments
7. Conversation Memory Layer
   Preserves dialogue context during the call for coherent communication.
8. TTS module
   Converts response text to natural-sounding voice.
9. Fallback Routing
   If the assistant is unsure by > 25% confidence threshold:
   • Transfers call to human operator
   • Provides operator with “conversation summary”

Features Delivered

1. Real-Time Voice AI Agent

• Understands patient speech
• Provides medical info from structured RAG system
• Handles multi-turn conversations
• Recognizes emotions (fallback)

2. Appointment Management

• Available doctor lookup
• Accurate calendar integration
• Automatic booking confirmations
• Cancellation and modification
• Synchronization with CRM

3. Notification Service

• SMS and email confirmations
• Reminders for upcoming visits
• Automated follow-up messages

4. Monitoring & Analytics

• Call logs
• Intent analytics
• Common problem detection
• Daily/weekly performance dashboards

Implementation Timeline

The project was delivered in 3 main phases:

Phase 1: Discovery (2–3 weeks)

• Infrastructure audit
• Call center workflow mapping
• Knowledge base collection
• Telephony assessment
• Architecture definition
• BRD + FRS creation

Phase 2: Prototype (6–8 weeks)

• Real-time STT/TTS pipeline
• First version of RAG system
• Initial telephony integration
• Intent detection model
• Appointment booking API
• Internal QA

Phase 3: Production Release (10–12 weeks)

• Load optimization
• Security hardening
• Monitoring/alerting setup
• Failover logic
• Deployment to clinic environment
• Staff training
• Documentation + support handover

Team

• Project manager
• Front-end developer
• Back-end developer
• ML engineer
• QA engineer
• DevOps engineer/System administrator

Learn more about our healthcare development services.