Case Study: Medical Inquiry RAG Agent

Business Impact Overview

Transforming patient support with intelligent medical Q&A automation

The Challenge

Healthcare staff spent hours answering repetitive patient questions about departments, medicines, and hospital services. Response times were slow, and accuracy varied depending on staff availability and knowledge.

The Solution

A RAG-powered system with specialized AI agents (Department, Hospital, Medicine, Greeting) that route queries intelligently and retrieve accurate answers from a compliant medical knowledge base in real time.

The Results

• 80% reduction in response time for common inquiries
• 95% accuracy in medical information retrieval
• 24/7 availability for patient questions
• Freed staff to focus on critical patient care

System Overview

The system is structured around several specialized agents, each responsible for handling a distinct type of inquiry.

Department Agent

Discusses specific medical departments.

Hospital Agent

Offers details about hospitals.

Greeting Agent

Manages greetings and goodbyes.

Medicine Agent

Provides information about specific medicines.

Additionally, a User Proxy agent handles initial user inputs, and an Operator Agent routes queries to the appropriate specialized agent based on the content of the user's message.

Technical Setup

The implementation uses Python with several libraries

LangGraph

LangGraph for constructing a state graph that orchestrates the flow of conversation through different states and agents.

Typing & Operator

Typing and Operator for defining types and functions that help manage the system's state.

LangChain

LangChain for building the AI chat model and managing the flow between different agents.

Creating The Agent

Each agent is created using a specialized function that sets up a LangChain workflow with a specific prompt template. This template instructs the agent on how to respond based on the tools available to it. The agents use predefined tools (greeting, medicine, hospital, department) to handle specific types of content.

Workflow Design

The system workflow is defined using a StateGraph from the LangGraph library. The workflow includes

User Proxy to receive initial inputs.

Operator Agent to analyze the input and determine the appropriate specialized agent to handle the query.

Specialized Agents to handle specific queries as determined by the Operator.

Final Response Agent to compile and finalize the response before it is returned to the user.

Execution Flow

User Input Handling Initial messages from users are captured by the User Proxy agent.

Routing Logic The Operator Agent examines the content of the user's message and decides which specialized agent should take over based on the message's content.

Query Processing The selected specialized agent processes the query using its assigned tools and constructs a response.

Final Compilation The Final Response Agent collects responses from the specialized agents and formats them for delivery to the user.

System Workflow Diagram

The diagram below illustrates the AI-powered system workflow, starting from the user input and routing through specialized agents before generating the final response. Each query is processed by the appropriate agent, ensuring accurate and efficient handling of medical inquiries.

Challenges & Solutions

Key obstacles we overcame to deliver a seamless medical inquiry experience

Complexity In Routing

Determining the appropriate agent for a given query can be complex. This was addressed by designing a robust Operator Agent that uses patterns and keywords to route the query accurately.

Integration Of Multiple Tools

Each agent might need to interact with multiple tools; managing these interactions required careful planning and testing to ensure seamless operation.

Maintaining Conversation Context

It was crucial to preserve the state across different agents. This was achieved by passing the entire conversation history to each agent, allowing them to maintain the context of the interaction.

Conclusion

This AI-powered medical inquiry system demonstrates how specialized agents can collaborate to handle complex user queries effectively. By leveraging state-of-the-art AI models and a well-architected workflow, the system provides accurate, context-aware responses to users' medical inquiries. This case study illustrates the potential of AI in transforming customer service operations, particularly in sectors where accuracy and speed are crucial, like the medical field.

This implementation serves as a blueprint for similar systems across different industries, showcasing a scalable and efficient approach to managing customer interactions with AI.

Frequently Asked Questions

Common questions about our Medical Inquiry RAG Agent

How does the system ensure medical information accuracy?

The system retrieves answers from a curated, compliant medical knowledge base using RAG technology, ensuring all responses are accurate and up-to-date.

Can the agent handle multiple questions in one conversation?

Yes. The system maintains conversation context across multiple queries, allowing patients to ask follow-up questions naturally.

Is patient data kept secure?

Absolutely. The system is designed with healthcare compliance in mind, ensuring all patient interactions are secure and private.

Medical Inquiry RAG Agent: AI-Powered Healthcare Q&A