Implementation of a Large Language Model for one of the leading Healthcare Organizations

May 31, 2023


This study aimed to implement an LLM-based chatbot for customer service in the healthcare sector, offering state-of-the-art conversational interactions and support. Beyond its primary role, the chatbot provides additional functionalities such as request classification, summarization, and sentiment analysis. These often-overlooked features are vital in enhancing customer service and delivering a more comprehensive user experience.

By harnessing the power of LLM, the healthcare client can offer personalized and efficient assistance to their customers. The chatbot's ability to classify requests, summarize information, and analyze sentiment enables the client to deliver tailored solutions and insights and analyze potential improvements. It is essential to recognize the immense potential of these by-products derived from LLM utilization and chatbot implementation, as they can significantly contribute to operational efficiency and customer satisfaction.


Existing chatbots used for customer service have several shortcomings that hinder their ability to meet customer expectations. One major limitation of these chatbots is their lack of language understanding, leading to generic or unrelated responses that frustrate customers. Another drawback is their limited contextual awareness, as they struggle to retain previous interactions or understand references made in subsequent messages. This results in disjointed conversations and a lack of understanding of the user's intent. Moreover, existing chatbots often rely on predefined response templates or keyword matching, making their interactions inflexible and less personalized. They may struggle to handle specific inquiries or unexpected requests, reducing their effectiveness in providing tailored support. Additionally, these chatbots face challenges in handling complex queries, providing accurate information, and lacking emotional intelligence to empathize with customers' emotions.

Advancements in natural language processing, machine learning, and AI algorithms are crucial to overcome these limitations. Improving language understanding, context awareness, response flexibility, and emotional intelligence are key areas for enhancing chatbot performance. By leveraging these advancements, chatbots can deliver seamless and personalized interactions, effectively addressing the diverse needs of customers. Advancements in multilingual support and a reduction in reliance on structured data sources can further enhance their capabilities. Overall, these improvements aim to create chatbots that provide a more satisfying customer service experience and better meet the evolving demands of users.

Technology Solutions

We proposed to implement a ChatGPT-based chatbot. Azure OpenAI provides all the tools and technologies necessary for the implementation of ChatGPT. Azure Cognitive Services, including Azure Language Understanding (LUIS), enhance the chatbot's language comprehension, while Azure Bot Service provides the platform for building and managing the chatbot application. OpenAI's ChatGPT model serves as the language generation capability for generating human-like responses. Integrating Azure Text Analytics and Azure QnA Maker further enhances the chatbot's understanding of user sentiments, key phrases, language detection, and handling frequently asked questions.

Azure SDKs and Bot Framework SDK cloud be used to customize the chatbot's functionalities. These tools and frameworks provide the necessary development environment and APIs for seamless integration. In order to fine-tune the chatbot we also used Azur Blob Storage and Cloud Compute resources.

Implementation Strategy

Azure OpenAI was leveraged to propose a chatbot based on ChatGPT, OpenAI's language model. With Azure Cognitive Services and OpenAI's technology, we proposed an interactive and intelligent chatbot that can engage in natural language conversations with users.

In the first step, we proposed using Azure Cognitive Services, such as Azure Language Understanding (LUIS), to enhance the chatbot's language comprehension. LUIS allows the chatbot to understand user intents, extract entities, and accurately interpret user queries. By integrating LUIS, the chatbot can better understand and respond to user requests, providing more accurate and context-aware answers.

Azure Bot Service provides a platform for building, deploying, and managing the chatbot. It offers a scalable and reliable infrastructure for hosting the chatbot application, ensuring high availability and efficient resource utilization. We proposed utilizing Azure Bot Service to handle user interactions, manage conversational flow, and integrate with the client's website.

To fine-tune the ChatGPT model we suggested Azure OpenAI to adapt it to specific chatbot requirements. Data from the client’s CRM system will be used for fine-tuning. Three sets of data to fine-tune ChatGPT are historical human-driven chats, call center transcripts, and customer feedback. Customer feedback is used to improve the performance of the model and make it more suitable for the desired conversational experience.

Additionally, the chatbot will be integrated with other Azure services, such as Azure Text Analytics and Azure QnA Maker, to enhance the chatbot's capabilities. Azure Text Analytics provides sentiment analysis, keyphrase extraction, and language detection, allowing the chatbot to understand user emotions and sentiments. Azure QnA Maker enabled the chatbot to provide answers to frequently asked questions, improving its ability to handle common inquiries.

By combining Azure's cognitive services, infrastructure, and OpenAI's ChatGPT model, we proposed a sophisticated chatbot that understands user intents, responds intelligently, and delivers a seamless conversational experience.

As future directions, we proposed implementing strategies for bias detection and analysis. Bias detection is an important topic in the language models. In addition, new LLM models are able to also read lab test results and provide initial feedback to customers. As a more advanced application, we are considering enhancing the bot to provide initial feedback on lab test results and some X-Ray images. Although these results need to be reviewed by doctors to provide feedback and validate the results of the model, we believe this could be a potential step forward.



Results and Impact

This project provided many benefits to our client:

  • Improving Customer Experience: The ChatGPT-powered chatbot offers more natural and engaging conversations, providing customers with a user-friendly and interactive experience. The chatbot can understand and respond to customer inquiries, provide relevant information, and assist with various tasks, resulting in enhanced customer satisfaction.
  • Increased Efficiency and Scalability: By automating customer interactions, the chatbot handles a large volume of inquiries simultaneously, reducing the need for manual intervention. This automation improves response times, enables 24/7 availability, and ensures consistent service quality across multiple channels, leading to increased operational efficiency and scalability.
  • Cost Savings: The chatbot we proposed helps reduce operational costs by minimizing the need for human customer service representatives. By reducing the number of times that the chatbot required human intervention the client is able to reduce the number of human resources needed.
  • Data-Driven Insights: The Azure ChatGPT-based chatbot integrates with Azure's analytics capabilities, allowing businesses to collect and analyze customer interactions, preferences, and feedback. These insights can provide valuable information for improving products, services, and customer support strategies, enabling data-driven decision-making.
  • Flexibility and Customization: Our suggestions were able to offer a high level of flexibility and customization options, allowing the client to tailor the chatbot's behavior, responses, and personality to align with their brand identity and customer preferences. This customization ensures a personalized and consistent customer experience, enhancing brand loyalty and engagement.
  • Continuous Improvement: With the ability to finetune and train Azure ChatGPT models, we enabled the client to continuously improve their chatbot's performance over time. By analyzing user feedback, monitoring interactions, and implementing iterative improvements, the chatbot can evolve and become more accurate and effective in addressing customer needs.

Overall, implementing an Azure ChatGPT-based chatbot can result in improved customer experience, increased efficiency, cost savings, valuable insights, customization options, and continuous improvement, leading to a more successful and efficient customer service operation.


The implementation of an LLM-based chatbot in the healthcare sector offers numerous benefits and outcomes. By harnessing the power of LLM technology, the chatbot provides personalized and efficient customer assistance, along with additional features such as request classification, summarization, and sentiment analysis. Through the integration of Azure Cognitive Services, Azure Bot Service, and OpenAI's ChatGPT model, a robust technology solution was proposed to develop and deploy the chatbot, addressing the limitations of existing chatbots used for customer service.

The implementation of the Azure ChatGPT-based chatbot could lead to improved customer experiences, increased operational efficiency, cost savings, data-driven insights, customization options, and continuous improvement opportunities. By enhancing language understanding, context awareness, response flexibility, and emotional intelligence, the chatbot delivers more satisfying interactions, handles complex queries, and empathizes with customer emotions. Integration with Azure services like Text Analytics and QnA Maker further augments its capabilities. Overall, the successful implementation of this chatbot empowers the healthcare client to provide exceptional customer service and elevate their customer support operations.

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