Preventive Health with ChatIRIS: Integrating InterSystems IRIS for Enhanced Patient Engagement

ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model (Hochbaum, Rosenstock, & Kegels, 1952) as a psychological framework to craft empathetic replies.

Health Belief Model

The Health Belief Model suggests that individual health behaviours are shaped by personal perceptions of vulnerabilities to disease risk, alongside the perceived incentives and barriers to taking action.

Our approach disaggregates these concepts into 14 distinct belief scores, allowing us to dynamically monitor them over the course of the conversation.

In the context of preventive health actions (e.g. cancer screening, vaccinations), we find that the agent is fairly successful at picking up a person’s beliefs around health actions (e.g. perceived vulnerabilities and barriers). We demonstrate the agent’s capabilities in the specific instance of a colorectal cancer screening campaign.

Architecture

ChatIRIS's technical framework is intricately designed to optimize the delivery of personalized healthcare advice through the integration of advanced AI techniques and robust data handling platforms. Central to this architecture is the use of InterSystems IRIS, particularly its vector store and vector search capabilities, which play a pivotal role in the Retrieval-Augmented Generation (RAG) pipeline. This section delves deeper into how these components contribute significantly to the functionality and effectiveness of ChatIRIS.

Retrieval-Augmented Generation (RAG) Pipeline

The RAG pipeline is a fundamental component of ChatIRIS, tasked with fetching pertinent information from a comprehensive database to produce contextually relevant responses. Here's how the RAG pipeline functions within the broader architecture:

1. User Input Processing: Initially, user inputs are analyzed to extract key health queries or concerns. This analysis helps in identifying the context and specifics of the information required.
2. Activation of Vector Search: The RAG pipeline employs vector search technology from InterSystems IRIS’s vector store to locate the most relevant information. This process involves converting text data into vector representations, which are then used to perform semantic searches across the extensive knowledge base.
3. Data Retrieval: By leveraging the vector search capabilities, the system efficiently sifts through large volumes of data to find matches that are semantically close to the query vectors. This ensures that the responses generated are not only accurate but also specifically tailored to the user’s expressed needs.

Role of InterSystems IRIS Vector Store

InterSystems IRIS vector store is integral to enhancing the search functionality within the RAG pipeline. Below are the key advantages and functionalities provided by the vector store in this context:

1. Semantic Understanding: The vector store allows for the encoding of text into high-dimensional space, capturing the semantic meanings of words beyond simple keyword matching. This is crucial for understanding complex medical terminology and user expressions in healthcare contexts.
2. Speed and Efficiency: Vector search is known for its ability to provide rapid responses, even when dealing with large datasets. This is particularly important for ChatIRIS, where timely and relevant responses can significantly impact user engagement and satisfaction.
3. Scalability: As ChatIRIS expands to accommodate more users and increasingly complex health queries, the scalability of the vector store ensures that the system can handle growing data volumes without degradation in performance.
4. Continuous Learning and Updating: The vector store supports dynamic updating and learning, meaning it can incorporate new research, health guidelines, and user feedback to refine its search capabilities continuously. This helps keep the chatbot’s responses up-to-date with the latest medical advice and practices.

Integration with Health Belief Policy Model

The integration of vector search with the Health Belief Policy model allows ChatIRIS to align detailed medical information with psychological insights from user interactions. For example, if a user shows concern about vaccine side effects, the system can pull targeted information to address these fears effectively, making the chatbot’s responses more persuasive and reassuring.

This streamlined integration of InterSystems IRIS technologies enables ChatIRIS to function as a highly effective tool in promoting preventive health measures, leading to better health outcomes and improved public health engagement.

Case Study and Practical Implementation

A practical demonstration of ChatIRIS’s capability can be seen in its pilot implementation for colorectal cancer screening. Initially, the chatbot gathers basic health details from the user and progressively addresses their concerns about the screening process, costs, and potential discomfort. By integrating responses from the Health Belief Policy model and the RAG pipeline, ChatIRIS efficiently addresses misconceptions and motivates users towards taking preventive actions.

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