Assessing the Feasibility and Impact of Integrating an Artificial Intelligence-Based Autism Spectrum Disorder Diagnostic Aid into the Primary Care Echo Autism Stat Model: Study Protocol
Author(s)
Sohl K1, Kilian R2, Curran A3, Mahurin M3, Nanclares-Nogués V3, Taraman S2
1University of Missouri, School of Medicine, Columbia, MO, USA, 2Cognoa, Inc., Palo Alto, CA, USA, 3University of Missouri, Columbia, MO, USA
OBJECTIVES: This prospective observational study will be the first to evaluate the use of a novel artificial intelligence-based autism spectrum disorder (ASD) diagnosis aid (the Device) as part of a real world primary care diagnostic pathway into the ECHO Autism STAT diagnostic model. The Device produces recommendations for primary care clinicians after analyzing behavioral features from a caregiver questionnaire, trained video analyst responses to two short home videos, and a health care provider questionnaire. All inputs can be collected remotely. The primary study endpoint is time from initial ECHO Autism Clinician (EAC) concern to ASD diagnostic determination. Secondary endpoints include: time from initial caregiver concern to ASD diagnosis; time from diagnosis to treatment initiation and EAC and caregiver experience of Device use as part of the ASD diagnostic journey. Assessment of insurance coverage and reimbursement will be completed, although insurance will not be billed for this study.
METHODS: Research participants for this prospective observational study will be patients with suspicion of ASD or developmental delay (aged 18-72 months) and their caregivers. EACs will be in urban and rural areas. Outcome data will be assessed quantitatively and qualitatively and collected via a combination of electronic questionnaires, standard clinical care record reviews and analysis of Device outputs.
RESULTS: Participant recruitment is planned to begin in the first quarter of 2022.
CONCLUSIONS: Streamlining primary care ASD diagnosis could support the primary care clinician’s decision-making process while potentially reducing strain on specialty services. This could allow a greater proportion of children to commence early intervention during the critical neurodevelopmental window.
Conference/Value in Health Info
Value in Health, Volume 25, Issue 6, S1 (June 2022)
Code
MT23
Topic
Medical Technologies
Topic Subcategory
Diagnostics & Imaging, Digital Health, Medical Devices
Disease
Medical Devices, Neurological Disorders, Pediatrics