JMIR Publications

ABSTRACT

Background:

The Extension for Community Healthcare Outcomes (ECHO) Autism Program trains participating clinicians to screen, diagnose and care for children with autism spectrum disorder (ASD) in primary care settings. This study will assess the feasibility and impact of integrating an artificial intelligence (AI)-based ASD diagnosis aid (the ‘Device’) into the existing ECHO Autism STAT diagnosis model. The prescription-only Software as a Medical Device is designed for use in 18-72-month-olds at risk for developmental delay. The Device combines behavioral features from 3 distinct inputs (a caregiver questionnaire, two short home videos analyzed by trained video analysts, and a healthcare provider questionnaire) in a machine learning algorithm to produce either an ‘ASD positive’, ‘ASD negative’ or ‘no result (indeterminate)’ output. The Device is not a standalone diagnostic, however, healthcare providers can leverage the Device output, in conjunction with their clinical judgment, when formulating a diagnosis.

Objective:

To assess the feasibility and impact of integrating an AI-based ASD diagnosis aid into the ECHO Autism STAT diagnosis model. Time from initial ECHO Autism Clinician (EAC) concern to ASD diagnosis is the primary endpoint. 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.

Methods:

Research participants for this prospective observational study will be 1) patients with a concern for developmental delay (aged 18-72 months) and their caregivers and 2) Up to 15 trained EACs, recruited by the ECHO Autism Communities research team from across rural and suburban areas of the United States. EACs will provide routine clinical care and conduct best practice ECHO Autism diagnostic evaluations in addition to prescribing the Device. Outcome data will be collected via a combination of electronic questionnaires, standard clinical care record reviews and analysis of Device outputs. Expected study duration is no more than 12 months.

Results:

The study has Institutional Review Board Approval from the University of Missouri-Columbia (IRB assigned project number 2075722). Participant recruitment is planned to begin in the first or second quarter of 2022.

Conclusions:

This prospective observational study will be the first to evaluate the use of a novel AI-based ASD diagnosis aid as part of a real world primary care diagnostic pathway. If Device integration into primary care practice proves feasible and efficacious, prolonged delays between first ASD concern and eventual diagnosis may be reduced. Streamlining primary care ASD diagnosis could potentially reduce strain on specialty services and allow a greater proportion of children to commence early intervention during a critical neurodevelopmental window. Clinical Trial: Registered with ClinicalTrials.gov (Protocol Identifier: NCT05223374)