Customer feedback is essential for improving services, outcomes, and experiences in the healthcare industry. Yet, traditional feedback methods often fail due to low response rates, poor engagement, and an inability to capture nuanced sentiments. To address this, we partnered with a healthcare provider to create an intelligent voice-based feedback system that enhances engagement and delivers actionable insights in real-time.

This blog explores the system’s technical architecture, innovative features, and measurable impact, showcasing how AI is transforming feedback collection in healthcare.

Providing personalized, high-quality care relies on actionable feedback. Our client, a leading healthcare provider, struggled to gather meaningful insights from diverse demographics using static surveys and manual processes. These methods failed to capture nuanced emotions and ensure engagement.

To bridge this gap, we developed a conversational, voice-based feedback system. The solution aimed to boost engagement, provide real-time insights, and improve decision-making. This collaboration set the stage for redefining feedback collection in the healthcare sector.

Creating an intelligent voicebot for healthcare feedback presented unique hurdles that went beyond the technical scope of AI. It required us to deeply understand customer behavior, language nuances, and the operational demands of healthcare organizations. The challenges included:

Decoding Emotional Subtleties
Customers often communicate their emotions in ways that are subtle and deeply contextual. Capturing these cues and accurately interpreting sentiments required sophisticated NLP models capable of going beyond literal meanings.

Designing Dynamic Conversations
Unlike traditional surveys, this solution needed to adapt in real-time, reshaping question paths based on user responses. This meant embedding complex branching logic into the system while ensuring the flow felt natural and engaging.

Achieving Real-Time Accuracy
Processing speech input, analyzing sentiment, and generating audio responses in real-time required a highly optimized workflow. Ensuring high accuracy while maintaining low latency posed a significant technical challenge.

Seamless System Integration
To create an end-to-end solution, the voicebot had to integrate smoothly with the client’s internal systems for data storage, reporting, and analysis. Any misstep in this process could disrupt operational workflows.

Despite these challenges, the team worked with a singular focus on delivering a scalable, reliable, and user-friendly voicebot capable of transforming customer feedback into actionable insights.

The development of the Customer Care Feedback Survey Voicebot required an end-to-end system that seamlessly integrated cutting-edge technologies like speech-to-text (ASR), natural language processing (NLP), and text-to-speech (TTS). Each module needed to work in perfect harmony to deliver a real-time, conversational experience that felt intuitive and engaging to customers. Below, we provide an in-depth look into how the system was implemented, detailing the technical steps, tools, and processes used to bring the solution to life.

The backbone of the solution was a modular architecture that ensured each component performed its specific task efficiently while communicating seamlessly with the rest of the system. The key modules included:

Speech-to-Text (ASR): To transcribe customer voice inputs in real time.

Survey Logic: A JSON-based engine to adapt the survey flow dynamically.

Sentiment Analysis: Powered by Microsoft DeBERTa-v3-base to interpret feedback.

Text-to-Speech (TTS): Using Bark TTS for generating human-like voice responses.

Each module interacted through API calls, creating a pipeline where user input flowed from one module to the next, ensuring a smooth and cohesive survey experience.

Speech-to-text was a critical component, as it captured customer input with accuracy and speed. Nvidia Riva ASR was chosen for its superior performance in transcription, even for diverse accents and noisy environments.

Implementation Steps:

• Instance Setup:
  Nvidia Riva was deployed on an AWS t3.xlarge instance. The instance was selected for its balance of compute power (4 vCPUs) and 16GB memory, which allowed real-time processing with low latency.

• Configuration Details:

• Riva was installed and configured using Nvidia’s Docker containers (riva_speech_server).
• The ASR service was launched using the following command:

riva_start.sh --asr --language-code en-US

• This ensured that the ASR engine could handle English language input efficiently.

• API Integration:

• Customer speech input was routed to the ASR engine through a RESTful API:

import requests

def transcribe_audio(audio_file):

    response = requests.post(

        "http://riva-server-url/api/asr/transcribe",

        files={'file': audio_file},

        headers={'Content-Type': 'audio/wav'}

    )

    transcription = response.json().get('transcription', '')

    return transcription

• The API returned a JSON response containing the transcribed text:

{
  "transcription": "I am satisfied with the care I received.",
  "keywords": ["care", "satisfied"]
}

• Optimization:

• Custom vocabulary for medical terms was added to improve recognition accuracy.
• Noise cancellation filters were enabled during preprocessing to handle background noise effectively.

To create a personalized and adaptive survey, we developed a JSON-based template for survey questions. The branching logic ensured that the survey could adapt in real time based on the sentiment of customer responses.

Key Features of the JSON Template:

• Each question included metadata such as 

   question_id, question_text, and response_options.

• Branching was defined by

  next_question_id

  attributes, linking responses to subsequent questions.

• Example structure:

{

  "question_id": "q1",

  "question_text": "How would you rate your experience?",

   "response_options": 

     [{

       "response_text": "Good",
       "next_question_id": 

       "Q2_positive",
       "feedback_message": "We're thrilled you had a great   experience!"},

      {

      "response_text": "Bad",
      "next_question_id": "q2_negative",
      "feedback_message": "We're sorry to hear that. Let's understand what went wrong."
    }]

}

Integration Steps:

• A Python-based survey engine (survey_logic.py) parsed the JSON and dynamically generated the next question.
• The engine interacted with the sentiment analysis module (discussed next) to adjust the survey flow based on real-time feedback.

Understanding customer emotions was critical for guiding the conversation. Microsoft DeBERTa-v3-base was chosen for its ability to capture nuanced sentiment and context in text.

Deployment and Configuration:

• The sentiment analysis model was deployed using the Hugging Face transformers library:

from transformers import pipeline

sentiment_analyzer = pipeline("sentiment-analysis", model="microsoft/deberta-v3-base")

def analyze_sentiment(text):

    result = sentiment_analyzer(text)

    label = result[0].get('label')

    score = result[0].get('score')

    return label, score

• Preprocessing steps were implemented to clean the ASR transcriptions before analysis:
  • Lowercasing.
  • Removing filler words like “uh” and “um.”
  • Punctuation normalization.

Integration Steps:

• The transcribed text from Nvidia Riva ASR was sent to the sentiment analyzer via an API endpoint:

POST /api/sentiment/analyze

Content-Type: application/json

{

  "text": "I am not happy with the waiting time.",

   "keywords": ["not", "happy"]

}

• The response returned sentiment scores and labels:

{

  "label": "negative",

  "score": 0.87

}

• Based on the sentiment label, the survey engine adjusted the next question or prompted follow-up inquiries for negative feedback.

To maintain an engaging, conversational experience, we used Bark TTS to generate human-like speech for survey responses.

Implementation Steps:

• Bark TTS was configured to generate audio files on demand:

from bark import generate_audio

audio_output = generate_audio("Thank you for your feedback. Can you tell us more about the issue?")

• Audio responses were streamed back to the web application via the following API:

POST /api/tts/synthesize

Content-Type: application/json

{

  "text": "Thank you for your feedback."

}

• Cached frequently used phrases to reduce latency.

Enhancements:

• Voice tone and pitch were adjusted dynamically based on sentiment analysis results. For example:
  • Positive feedback used a cheerful tone.
  • Negative feedback used a calm, empathetic tone.

The web application served as the interface for customers, enabling seamless interactions with the voicebot. 

Technology Stack:

• Frontend: Built with React.js for responsiveness and real-time updates.
• Backend: FastAPI served as the integration layer for handling API calls and processing responses from ASR, sentiment analysis, and TTS modules.

Integration Highlights:

• Speech input was captured using the Web Speech API and sent to the ASR service:

const speechRecognition = new SpeechRecognition();

speechRecognition.onresult = (event) => {

  const audioInput = event.results[0][0].transcript;

  fetch('/api/asr/transcribe', {

    method: 'POST',

    body: audioInput

  });

};

• All services were containerized using Docker for easy deployment and scalability.

The team also provided a real-time survey dashboard for the client to monitor the processes.

The system underwent multiple rounds of testing to ensure high reliability:

• Functional Testing: Validated each module (ASR, TTS, sentiment analysis) individually and as a part of the integrated system.
• Performance Testing: Benchmarked latency at under 300ms for each API call, ensuring real-time interaction.
• User Acceptance Testing: Feedback was gathered from healthcare staff and customers to refine the user experience.

Finally, the entire system was deployed on the AWS t3.xlarge instance, with monitoring tools like Prometheus and Grafana to track system performance and uptime.

The deployment of the voicebot redefined how feedback was gathered and created a ripple effect across the organization, delivering measurable outcomes and strategic value. The results were transformative:

• Revolutionized Engagement: The voicebot’s conversational, human-like interactions increased customer survey participation, boosting survey completion rates by 18%. customers appreciated the ease and natural flow of providing feedback via voice rather than traditional forms.
• Streamlined Operations: Automating the feedback process led to a 14% reduction in survey administration costs. Staff previously involved in manual survey handling could now focus on higher-value tasks, improving overall operational efficiency.
• Actionable Insights, Real-Time Decisions: With the voicebot dynamically analyzing customer sentiment, the healthcare provider gained a deeper understanding of customer emotions. This enabled them to act quickly on negative feedback and amplify positive experiences, resulting in more personalized care strategies.
• Enhanced Customer-Centric Care: By incorporating real-time sentiment analysis, the organization could tailor services based on direct customer input, demonstrating a commitment to quality and care that resonated deeply with customers.
• Enhanced Accessibility with Multi-Language Support: The integration of multi-language support broadened the system’s reach, enabling patients from diverse linguistic backgrounds to provide feedback in their preferred language. This inclusivity improved engagement rates across demographics and ensured that all voices were heard, fostering a more customer-centric approach to care.

The results confirmed the power of combining advanced AI technologies with a user-first approach. What began as a need for better surveys transformed into a scalable, intelligent system that continues to shape the future of healthcare service delivery.

This project underscores the immense potential of AI and machine learning in revolutionizing traditional feedback mechanisms. By combining cutting-edge ASR, NLP, and TTS technologies, we created a system that engages customers more effectively and empowers healthcare providers with deeper insights and actionable data.

For startups and enterprises looking to harness the power of AI-driven solutions, this case study highlights the importance of integrating advanced technologies with user-centric design. As the healthcare industry continues to evolve, intelligent systems like this voicebot will play a pivotal role in enhancing customer experiences and outcomes.

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