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

Adesuwa Emovon¹, Lauren Driggers-Jones², Matthew Engelhard³, Gary Maslow^2,4, Geraldine Dawson^2,4, Benjamin A Goldstein^3,4, Lauren Franz²

¹College of Medicine, Penn State, Hershey, PA, 17033, United States.

²Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, 27710, United States.

³Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, United States.

⁴Department of Pediatrics, Duke University School of Medicine, Durham, NC, 27710, United States.

PubMed PMC DOI PDF

Abstract

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 twe…

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NIH Funding

Grant #11393683
Machine Learning Methods to Develop and Deploy Real-Time Risk Surveillance for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder from the Electronic Health Record
DUKE UNIVERSITY
$163,204 (FY2026)
Grant #11419214
Duke Autism Center of Excellence: A translational digital health and computational approach to early identification, outcome monitoring, and biomarker discovery in autism
DUKE UNIVERSITY
$2,407,109 (FY2026)

Keywords

artificial intelligenceautismclinical decision supportelectronic health recordsuser-centered design

Article Info

PMID: 42494774
PMCID: PMC13394496
DOI: 10.1093/jamiaopen/ooag145
Received: October 3, 2025
Accepted: July 8, 2026

How to Cite

Emovon A et al. Understanding end-user contexts and identifying design preferences of an artificial intelligence-based clinical decision support tool for early autism detection. JAMIA Open2026;9(4):ooag145. doi:10.1093/jamiaopen/ooag145

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