Scholars@Duke publication: Understanding end-user contexts and identifying design preferences of an artificial intelligence-based clinical decision support tool for early autism detection.

Understanding end-user contexts and identifying design preferences of an artificial intelligence-based clinical decision support tool for early autism detection.

Journal articles - Journal Article

Emovon, A; Driggers-Jones, L; Engelhard, M; Maslow, G; Dawson, G; Goldstein, BA; Franz, L

Published in: JAMIA Open

August 2026

OBJECTIVES: Building on innovations for autism detection-where artificial intelligence (AI)-based models monitor clinical data within electronic health records-this study evaluates the context for clinical decision support (CDS) deployment and identifies design preferences. MATERIALS AND METHODS: This observational study utilized contextual inquiry to elicit perspectives from 8 clinicians and twenty caregivers during 18- to 24-month well-child visits at Duke-affiliated clinics. Data were analyzed using rapid qualitative analysis techniques. RESULTS: Workflow analysis identified 6 user tasks, 3 technology-user interactions, and 5 clinical decision points. Technologies that streamlined screening included patient portals, digital tablets, and note templates. Clinicians identified 2 major barriers-limited screening tool accuracy and challenges in implementing follow-up steps-and 3 facilitators: electronic screening, early intervention provider input, and staff referral coordination support. For design, CDS should include clear, actionable outputs, with explanations of prediction data, visual summaries linked to next steps, and educational resources. Embedding CDS within the EHR, with outputs delivered at key points during the clinical encounter, along with caregiver-facing materials, would improve workflow efficiency. DISCUSSION: Findings highlight key integration points for an autism detection AI-based CDS tool and stress the need for clinical utility and caregiver-centered communication. Effective design requires alignment with clinical workflow, including the timing of outputs, meaningful explanations, and integration with caregiver communication. CONCLUSION: Findings will inform the design of an AI-based CDS tool for autism detection, providing workflow-informed integration points and user preferences. Future work should refine explainability and optimize delivery of outputs within clinical encounters to support decision-making and caregiver engagement.