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Posted by Bill Gadless on April 28, 2023


In recent years, chatbots have become an essential part of digital communication in various industries. In healthcare and biopharma, chatbots have the potential to revolutionize patient engagement, customer support, and knowledge dissemination. With the advent of advanced AI models such as OpenAI’s ChatGPT, it is now possible to create more intelligent and human-like chatbots tailored to specific audiences.

This blog post will explore how healthcare and biopharma companies can leverage ChatGPT to create audience-specific chatbots that integrate only with their own content to ensure accurate and controlled responses to users’ questions. We will discuss the reasons behind this strategy, its potential benefits, and a step-by-step guide to building a chatbot with this approach.

Language Models

In the realm of artificial intelligence and natural language processing, there are two broad categories of language models: general-purpose and narrow (or domain-specific) language models. While general-purpose models like ChatGPT are designed to handle a wide range of topics and conversational contexts, narrow language models are specifically trained to understand and generate content within a limited domain or industry, such as healthcare or biopharma. This section will explain the concept of narrow language models and discuss their relevance to the chatbot development process described in this post.

What is a Narrow Language Model?

A narrow language model is an AI model specifically trained to understand and generate content within a particular domain, industry, or topic area. By focusing on a smaller scope of knowledge, these models can offer higher accuracy and relevance when dealing with industry-specific jargon, concepts, and contexts. In the context of healthcare and biopharma, a narrow language model would be trained exclusively on medical and pharmaceutical content, enabling it to generate more precise and reliable responses within this specific domain.

Relevance to Healthcare and Biopharma Chatbots

Although ChatGPT is a general-purpose language model, integrating it with controlled content effectively turns it into a narrow language model for the healthcare or biopharma domain. This is achieved through the fine-tuning process, where the model is trained on a curated knowledge base containing industry-specific content. This approach combines the benefits of general-purpose models (such as natural language generation capabilities and versatility) with the advantages of narrow language models (such as domain-specific accuracy and understanding).

The use of a narrow language model (in this case, ChatGPT fine-tuned on controlled content) is particularly important when it comes to building a healthcare or pharma chatbot for the following reasons:

  1. Enhanced accuracy and reliability: A narrow language model can better understand and generate domain-specific content, ensuring that the chatbot provides accurate, reliable, and up-to-date information in response to users’ questions.
  2. Improved handling of technical terminology: Healthcare and biopharma sectors often involve complex terminology and concepts. A narrow language model can better understand and process this technical language, making it better suited for providing accurate and relevant answers to users.
  3. Tailored responses for specific audiences: By focusing on a specific domain, narrow language models can be customized to cater to the unique needs and preferences of different user groups within the healthcare and biopharma sectors, such as patients, healthcare professionals, or researchers.

Why Integrate ChatGPT with Controlled Content?

  1. Ensuring accurate and reliable information: The healthcare and biopharma sectors deal with highly sensitive and regulated information. Integrating ChatGPT with controlled content allows companies to ensure that their chatbots provide accurate, reliable, and up-to-date information in response to users’ questions. This minimizes the risk of miscommunication or misinformation, which is crucial when dealing with health-related issues.
  2. Protecting proprietary data: In many cases, healthcare and biopharma companies possess proprietary information that should not be disclosed to unauthorized users. By integrating ChatGPT with their own content, companies can protect sensitive data while still offering valuable insights and assistance to users.
  3. Customizing responses for specific audiences: Different users have different information needs, and a one-size-fits-all chatbot may not cater to everyone effectively. By creating audience-specific chatbots and integrating them with tailored content, companies can ensure that each user receives the most relevant and helpful information. For instance, a pharma company might create unique chatbots for each patients, physicians, investors and sales reps.
  4. Enhancing user experience: ChatGPT can generate natural, human-like responses, making chatbot interactions feel more genuine and engaging. Integrating it with controlled content ensures that users receive accurate information while enjoying an enhanced user experience.

How to Create an Audience-Specific Chatbot with ChatGPT

Step 1: Identify the target audience

Before developing the chatbot, it is crucial to understand the needs of the target audience. This can include patients, healthcare professionals, or other stakeholders. Consider their demographics, preferences, and informational needs to create a detailed user persona. This will serve as the foundation for the chatbot’s design and content.

Step 2: Curate and organize content

Next, gather and organize the content that the chatbot will use to answer users’ questions. This content should be accurate, reliable, and up-to-date. It may include articles, guidelines, research studies, or any other relevant materials. Organize the content into a structured knowledge base, making it easily accessible and understandable for the ChatGPT model.

Step 3: Train ChatGPT on the controlled content

Train the ChatGPT model using the curated content. This can be done by fine-tuning the model on the knowledge base, which ensures that the chatbot’s responses are generated based on the company’s controlled content. This step is crucial to guarantee the accuracy and reliability of the information provided by the chatbot.

Step 4: Customize the ChatGPT model for the target audience

After training the model, customize it to cater to the specific needs of the target audience. This can involve adjusting the language, tone, or complexity of the responses to match the preferences and understanding of the users. This customization ensures that the chatbot provides the most relevant and helpful information to each user.

Step 5: Integrate the chatbot with the company’s digital channels

Once the chatbot is developed, integrate it with the company’s digital channels, such as websites, mobile apps, or social media platforms. This will make the chatbot easily accessible to the target audience, allowing them to interact with it whenever they need assistance or information.

Step 6: Monitor and evaluate the chatbot’s performance

Regularly monitor and evaluate the chatbot’s performance to identify areas for improvement. Track metrics such as user satisfaction, engagement, and response accuracy to understand the chatbot’s effectiveness in meeting the needs of the target audience. Collect user feedback to gain insights into their experience and expectations, which can be used to further refine the chatbot’s design and content.

Monitoring User Questions to Drive Content Creation

A significant advantage of integrating ChatGPT with controlled content is the ability to monitor the questions asked by users, which can serve as a valuable source of insights for content creation. By closely tracking user interactions, companies can identify knowledge gaps and better understand the informational needs of their target audience. This section will discuss the benefits of monitoring user questions and provide a guide for leveraging this data to inform content creation strategies.

Benefits of Monitoring User Questions:

  1. Identifying knowledge gaps: By analyzing the questions asked by users, companies can identify areas where their current content might not be sufficient or clear enough. This helps them pinpoint topics that need more in-depth coverage or clarification in their knowledge base.
  2. Understanding user interests and concerns: Tracking user questions can reveal patterns in their interests and concerns, which can inform the creation of new content that addresses these topics. This ensures that the content remains relevant and engaging to the target audience.
  3. Enhancing the chatbot’s accuracy: By identifying frequently asked questions that the chatbot might struggle to answer, companies can prioritize updating their controlled content to address these issues, thereby improving the chatbot’s accuracy and usefulness.
  4. Informing marketing and product development: Insights from user questions can also help inform marketing campaigns and product development efforts by highlighting trending topics or unmet needs in the market.

How to Leverage User Questions for Content Creation:

  1. Collect and analyze user questions: Regularly collect user questions from the chatbot’s interactions and analyze them to identify common themes, patterns, or frequently asked questions. This can be done using text analytics tools or manual analysis, depending on the volume of data.
  2. Prioritize content updates and creation: Based on the analysis, prioritize content updates or new content creation to address identified knowledge gaps, user interests, and concerns. Consider the frequency and urgency of the questions when determining which topics to prioritize.
  3. Update the knowledge base: Revise and expand the knowledge base to include the new or updated content. This ensures that the chatbot can provide accurate and comprehensive answers to users’ questions, based on the company’s controlled content.
  4. Communicate new content to users: Inform users about the updated or new content through various channels, such as newsletters, social media, or website updates. This can help drive engagement and encourage users to explore the new content, further enhancing their experience with the chatbot.
  5. Continuously monitor and iterate: Regularly monitor user questions and iterate on the content creation process. This continuous improvement approach ensures that the chatbot remains a valuable and up-to-date resource for users.

Step 7: Update and maintain the controlled content

Monitoring user questions is an invaluable strategy for healthcare and biopharma companies to create audience-specific content that addresses the needs of their users. By incorporating these insights into their content creation process, companies can keep their chatbot’s knowledge base relevant, engaging, and accurate, further enhancing the user experience and solidifying their position as trusted sources of information in their respective industries.

To ensure that the chatbot continues to provide accurate and reliable information, it is essential to regularly update and maintain the controlled content. Review the knowledge base for outdated or incorrect information, and replace it with the latest and most accurate data. This maintenance process ensures that the chatbot remains a valuable resource for users over time.


Healthcare and biopharma companies can greatly benefit from leveraging ChatGPT to create audience-specific chatbots that are integrated with controlled content. This approach ensures that users receive accurate, reliable, and tailored information while enjoying an enhanced user experience. By following the steps outlined in this blog post, companies can harness the power of AI-driven chatbots to revolutionize patient engagement, customer support, and knowledge dissemination in the healthcare and biopharma sectors.

For more information on leveraging ChatGPT to create audience-specific chatbots with controlled content, get expert help from emagineHealth. Let’s get started.

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