66 Piloting an AI-driven conversational agent for social needs screening and referral in pediatric primary care

Objectives/Goals: To evaluate the feasibility, acceptability, and usability of DAPHNE, an AI-powered chatbot for health-related social needs screening and referral in pediatric primary care, and to assess feasibility of study procedures to inform a future large-scale trial. Methods/Study Population: This pilot randomized controlled trial will enroll 60 caregivers of children ≤2 years from Nationwide Children’s Hospital pediatric primary care clinics. Participants will be randomized 1:1 to DAPHNE or standard care. The intervention provides private, natural language-based HRSN screening and personalized resource referrals via a secure, HIPAA-compliant mobile/web chatbot. Primary outcomes include feasibility (enrollment ≥70%, retention ≥70%), acceptability (WEQ ≥80%), usability (SUS ≥68), and technical performance (accuracy F1 ≥0.7, latency <3 seconds). Secondary outcomes include caregiver stress, self-efficacy, satisfaction, quality of life, provider workflow integration, study burden, and EHR-derived care utilization. Results/Anticipated Results: We anticipate that feasibility thresholds for enrollment, retention, and procedural adherence will be met. We expect caregivers to rate the chatbot above usability and acceptability thresholds, with high engagement and accurate resource navigation across devices. Provider surveys and interviews are anticipated to show positive perceptions of workflow integration. EHR data will capture referral patterns, visit adherence, and utilization trends. Findings will generate pilot data to refine the chatbot, optimize trial design, and inform comparator selection and clinical outcomes for a subsequent R01-level efficacy study. Discussion/Significance of Impact: This study will provide critical feasibility data on AI-driven HRSN screening in pediatrics. If effective, DAPHNE could reduce stigma, improve caregiver access to community resources, and inform scalable strategies for integrating conversational AI into pediatric primary care.