Status: Active

This project comes out of Duke’s Autism Center of Excellence (ACE). The ACE’s overall goal is to use a translational digital health and computational approach to address the critical need for more effective autism screening tools, objective outcome measures, and brain-based biomarkers that can be used in clinical trials with young autistic children. This Project will develop and evaluate a novel digital health approach to autism screening. Universal autism screening is recommended for children at 18 months. This is typically achieved via a caregiver questionnaire. However, research has shown that a commonly used autism screening questionnaire has reduced accuracy when used in real-world settings, such as primary care. By leveraging health data related to early medical conditions collected as part of clinical care, we will develop an automatic, objective tool for autism prediction at 18 months that can be implemented in primary care settings. To develop the models, we are using granular EHR data from children receiving early care at Duke University Health System as well as broad administrative data from children across North Carolina on Medicaid. Through engagement with stakeholders both within and outside of DUHS, we are using the prediction model to design a clinical decision support prototype that could assist providers in making appropriate and timely referrals. Through the design process, we will identify a set of key priority factors to consider when choosing a clinical decision support for autism screening that are applicable across a broad range of stakeholders in different health care settings. Finally, our methodological team is exploring best approaches for doing large scale, time-varying prediction with health data.

Research supported by: NICHD P50HD093074