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GARDE-Chat: a scalable, open-source platform for building and deploying health chatbots.

Guilherme Del Fiol1, Emerson Borsato1, Richard L Bradshaw1

  • 1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States.

Journal of the American Medical Informatics Association : JAMIA
|January 12, 2026
PubMed
Summary

GARDE-Chat is an open-source platform for creating digital health chatbots. This tool supports various chatbot types and integrates with health systems, aiding scalable population health interventions.

Keywords:
chatbotsconsumer healthdigital healthlarge language modelspopulation health

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Area of Science:

  • Digital Health
  • Health Informatics
  • Human-Computer Interaction

Background:

  • Chatbots are increasingly utilized for health education, patient engagement, and healthcare access.
  • GARDE-Chat is an open-source platform designed to streamline the creation and deployment of chatbot-based digital health interventions.

Purpose of the Study:

  • To introduce GARDE-Chat, an open-source platform for developing and disseminating digital health interventions.
  • To highlight the platform's features that support diverse chatbot development and integration.

Main Methods:

  • GARDE-Chat was developed iteratively, prioritizing features based on real-world use cases.
  • The platform was built as open-source to foster collaboration and broad adoption in research and clinical settings.

Main Results:

  • Key features include a visual interface for non-programmers, support for scripted/LLM/hybrid chatbots, and sharing capabilities.
  • The platform integrates with external data sources (e.g., EHR, REDCap) and offers flexible delivery via web or text.
  • Since July 2022, GARDE-Chat has supported interventions in pragmatic trials for genetic testing, COVID-19, tobacco cessation, and cancer screening.

Conclusions:

  • GARDE-Chat is a versatile platform for developing, implementing, and disseminating scalable, chatbot-based population health interventions.
  • The platform has been validated in multiple studies and is accessible to researchers and healthcare systems.
  • Ongoing development focuses on script creation efficiency, safe LLM integration, and clinical system interoperability.