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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, 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.
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 include 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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