InterLang: Enhancing Conversational Social Prescriptions with LangChain Chatbot Agents and FHIR Resource Integration

Motivation

The motivation behind the InterLang project is rooted in the innovative integration of LangChain chatbot agents with the Fast Healthcare Interoperability Resources (FHIR) framework to revolutionize conversational social prescriptions in healthcare. This project aims to leverage the rich and standardized data available through FHIR, an emerging standard in healthcare data exchange, to inform and empower these advanced chatbot agents.

FHIR provides a robust structure for health data, encompassing clinical, administrative, and financial information. By tapping into this wealth of data, LangChain chatbot agents can access a comprehensive view of a patient's health goals, treatments, and progress. This allows the agents to engage in more meaningful and context-aware conversations with patients, offering personalized advice and support.

A key aspect of this project is the focus on health goals, which are essential for effective patient care. Goals in healthcare range from achieving specific health outcomes, like reducing HgbA1c levels in diabetes, to broader objectives like enhancing patient satisfaction or managing healthcare costs. By integrating FHIR resources, LangChain agents can access these goal data and use them to guide conversations, provide reminders, and suggest interventions.

For instance, in managing a chronic condition like diabetes, a chatbot can remind patients of their medication schedule, suggest nutritional advice, or motivate increased physical activity, all aligned with the patient's specific health goals. In a hospital setting, these agents can assist in procedural goals, such as ensuring timely wound care or patient repositioning, to prevent complications.

Implementation

• InterSystems FHIR: Manages and standardizes healthcare data exchange.
• LangChain Agents and Tools: Drive the chatbot's conversational intelligence.
• GPT-4 Models: Provide advanced natural language processing capabilities.
• Docker: Ensures consistent, containerized deployment environments.
• SpringBoot: Facilitates the creation of scalable, enterprise-level back-end services.
• Streamlit: Enables rapid development of user-friendly web interfaces.

See our project on Github for a simple way to deploy a Streamlit application wth connection to InterSystems FHIR. This has previously been requested.

Video Demo

Demo

Authors

• Zacchaeus Chok
• Varun Swaminathan
• Gabriel Yang