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Training an AI Chatbot to Manage Health in Underserved Populations: Methodological Approach.

Allison Diane Ihle1, Breann Wicks1, Vangelis Metsis2

  • 1The University of Texas Health at San Antonio, San Antonio, TX, United States.

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Summary
This summary is machine-generated.

This study developed an AI chatbot, JUN, to support childbearing women on community supervision. The mobile health app achieved 89% accuracy in crisis detection and showed potential for improving health management.

Keywords:
artificial intelligencechatbotchildbearingcommunity supervisioncriminal legal systemdigital healthhealthhealth disparitiesmHealthmethodsmobile healthparolepregnantwomen’s health

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Area of Science:

  • Health equity and digital health interventions
  • Artificial intelligence in healthcare
  • mHealth app development and evaluation

Background:

  • Significant health disparities persist for childbearing women involved with the criminal legal system.
  • These women often face barriers to care and could benefit from accessible mobile health (mHealth) solutions.
  • Artificial intelligence (AI) offers potential for targeted health and safety support via mHealth apps.

Purpose of the Study:

  • To systematically design, optimize, and test an AI chatbot for childbearing women on community supervision.
  • To detail strategies and findings from a methodological case study.
  • To explore the feasibility of AI-driven mHealth interventions for underserved populations.

Main Methods:

  • Utilized an AI chatbot (JUN) within an mHealth app, guided by the Information Systems Research framework.
  • Employed a retrieval-augmented generation framework for chatbot design and configuration.
  • Demonstrated feasibility using an in-context learning approach with relevance, design, and rigor cycles.

Main Results:

  • The JUN chatbot achieved 89% accuracy in detecting crisis situations (N=178).
  • Participants reported increased usability for nighttime health management and cited caregiving/pregnancy as motivators.
  • Barriers included transportation, work flexibility, insurance, and stress from legal system involvement; anonymity and app store access were suggested enhancements.

Conclusions:

  • Successfully illustrated the design, optimization, and testing of an AI chatbot for mHealth support.
  • The study highlights the potential of AI-driven interventions for childbearing women on community supervision.
  • Presents possibilities for developing and testing similar interventions for at-risk populations.