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, OH.

Insights

An AI chatbot, DAPHNE, shows promise for identifying unmet health-related social needs (HRSNs) in young children. This pilot study evaluated its feasibility, acceptability, and usability in pediatric primary 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 settings are often resource-intensive and inconsistently applied.
  • AI-powered chatbots offer a scalable, cost-effective, and private solution for identifying these needs and connecting families with resources.

Purpose of the Study:

  • To evaluate the feasibility, acceptability, and usability of DAPHNE, an AI chatbot designed to identify unmet HRSNs and provide community resource referrals.
  • To assess the potential of DAPHNE as an alternative to traditional screening methods in pediatric primary care.
  • To inform the design of a larger trial investigating the efficacy and implementation of DAPHNE.

Main Methods:

  • A pilot randomized controlled trial involving 100 caregivers of children under two years old, recruited from pediatric primary care clinics.
  • Participants were randomized to standard care or DAPHNE plus standard care, with data collected via surveys at multiple time points over six months.
  • Mixed-methods analysis integrating survey data, chatbot engagement metrics, and qualitative interviews with caregivers and primary care providers.

Main Results:

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

Conclusions:

  • The pilot trial provides crucial data on the feasibility, acceptability, and usability of the DAPHNE AI chatbot for addressing unmet HRSNs in young children.
  • Findings will guide the development of a future multi-site trial to rigorously test the efficacy and implementation of this innovative digital health tool.
  • AI-driven solutions like DAPHNE hold potential to improve early identification of social needs and enhance access to essential community services for families.

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