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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.
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.
Abstract:
Unmet health-related social needs (HRSNs) are major drivers of poor health outcomes in early childhood. Children with unmet HRSNs are at greater risk for developmental delays, caregiver stress, and increased healthcare utilization, yet current screening approaches in pediatric primary care are resource-intensive and inconsistently implemented. AI-powered chatbots (conversational agents or virtual assistants) may offer a private, secure, scalable, and cost-effective alternative for identifying unmet needs and connecting families to services. This protocol describes a pilot randomized controlled trial designed to evaluate the feasibility, acceptability, and usability of DAPHNE, an AI-driven chatbot developed to facilitate the identification of unmet HRSNs and provide personalized community resource referrals. One hundred caregivers of children under two years of age will be recruited from Nationwide Children's Hospital pediatric primary care clinics and randomized to either the standard care (control) group or DAPHNE+ Standard care (intervention) group (n = 50 each arm). Caregivers will complete surveys at baseline, 1 month, 3 months, and 6 months post-intervention (depending on the measure). For the intervention group, participants will receive weekly chatbot prompts and on-demand access throughout the 6-month study period. Primary outcomes include study feasibility (recruitment, retention, and survey completion across both arms), acceptability (caregiver-reported ratings in both arms and intervention-specific ratings), and usability of the DAPHNE chatbot (System Usability Scale among intervention participants). Secondary outcomes includes caregiver-reported outcome measures (caregiver stress, self-efficacy, satisfaction with resource access, quality of life), and electronic health record-derived measures (including documentation of HRSN screening and referrals, adherence to well-child visits, missed appointments, emergency department utilization, and estimated healthcare costs). In addition, ten primary care providers will also participate to assess workflow integration and report on current HRSN practices. Mixed-methods analyses will integrate survey data, chatbot engagement metrics, and qualitative interviews to refine both the intervention and the study protocol. The results of this study will inform the design of a future multi-site trial to evaluate the efficacy and implementation of DAPHNE for addressing HRSNs in pediatric primary care. Trial registration: NCT07168382.
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