An AI-based chatbot to support health-related social needs among pediatric primary care population: Protocol for a

Emre Sezgin1,2, Elizabeth Clarkson1, Faith Logan1

  • 1The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio United States of America.

Plos One
|April 20, 2026
PubMed

Insights

AI chatbots can help identify children's unmet health-related social needs (HRSNs) and connect families to resources. This pilot study tested DAPHNE, an AI chatbot, for feasibility and acceptability in pediatric care.

Area of Science:

  • Pediatric Primary Care
  • Health Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Unmet health-related social needs (HRSNs) significantly impact early childhood health outcomes, leading to developmental delays and increased healthcare use.
  • Current screening methods for HRSNs in pediatric primary care are resource-intensive and inconsistently applied.
  • AI-powered chatbots offer a scalable, cost-effective solution for identifying needs and linking families to community services.

Purpose of the Study:

  • To evaluate the feasibility, acceptability, and usability of DAPHNE, an AI-driven chatbot for identifying unmet HRSNs and providing resource referrals in pediatric primary care.
  • To assess the DAPHNE chatbot's potential to improve caregiver outcomes and streamline HRSN screening and referral processes.
  • To inform the design of a larger multi-site trial on the efficacy and implementation of AI chatbots for addressing HRSNs.

Main Methods:

  • A pilot randomized controlled trial involving 100 caregivers of children under two years old, randomized to standard care or DAPHNE + standard care.
  • Participants completed surveys at baseline and follow-up points over six months; intervention group received weekly chatbot prompts and on-demand access.
  • Mixed-methods analysis integrated survey data, chatbot engagement metrics, and qualitative interviews with caregivers and primary care providers.

Main Results:

  • The study will assess primary outcomes of feasibility (recruitment, retention, completion), acceptability, and usability of the DAPHNE chatbot.
  • Secondary outcomes include caregiver-reported measures (stress, self-efficacy, quality of life) and EHR-derived data (screening documentation, visit adherence, healthcare utilization).
  • Primary care provider participation will evaluate workflow integration and current HRSN practices.

Conclusions:

  • This pilot study protocol provides a framework for evaluating AI chatbot interventions for addressing HRSNs in pediatric primary care.
  • Findings will guide the development of future trials to establish the efficacy and implementation strategies for AI-driven solutions like DAPHNE.
  • The research aims to enhance early childhood health by improving the identification and management of critical social needs.

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