Background: The Extension for Community Health Outcomes (ECHO) Autism Program trains 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 Screening Tool for Autism in Toddlers and Young Children (STAT) diagnosis model. The prescription-only Software as a Medical Device, designed for use in children aged 18 to 72 months at risk for developmental delay, produces ASD diagnostic recommendations after analyzing behavioral features from 3 distinct inputs: a caregiver questionnaire, 2 short home videos analyzed by trained video analysts, and a health care provider questionnaire. The device is not a stand-alone diagnostic and should be used in conjunction with clinical judgment. Objective: This study aims to assess the feasibility and impact of integrating an AI-based ASD diagnosis aid into the ECHO Autism STAT diagnosis model. The time from initial ECHO Autism clinician concern to ASD diagnosis is the primary end point. Secondary end points include the time from initial caregiver concern to ASD diagnosis, time from diagnosis to treatment initiation, and clinician and caregiver experience of device use as part of the ASD diagnostic journey. Methods: Research participants for this prospective observational study will be patients suspected of having ASD (aged 18-72 months) and their caregivers and up to 15 trained ECHO Autism clinicians recruited by the ECHO Autism Communities research team from across rural and suburban areas of the United States. Clinicians 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, reviews of standard clinical care records, and analysis of device outputs. The expected study duration is no more than 12 months. The study was approved by the institutional review board of the University of Missouri-Columbia (institutional review board–assigned project number 2075722). Results: Participant recruitment began in April 2022. As of June 2022, a total of 41 participants have been enrolled. 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 proves feasible and efficacious, prolonged delays between the first ASD concern and eventual diagnosis may be reduced. Streamlining primary care ASD diagnosis could potentially reduce the strain on specialty services and allow a greater proportion of children to commence early intervention during a critical neurodevelopmental window. Trial Registration: ClinicalTrials.gov NCT05223374; https://clinicaltrials.gov/ct2/show/NCT05223374
Background: Pediatric specialist shortages and rapidly rising autism prevalence rates have compelled primary care clinicians to consider playing a greater role in the autism diagnostic process. The ECHO Autism: Early Diagnosis Program (EDx) prepares clinicians to screen, evaluate, differentiate, diagnose and provide longitudinal care for autistic children in primary care settings. Canvas Dx is a prescription-only Software as a Medical Device designed to support clinical diagnosis or rule out of autism, including in primary care settings. It is FDA authorized for use, in conjunction with clinical judgement, in 18-72-month-olds with indicators of developmental delay. Objective: To assess the feasibility and impact of integrating the Device into the ECHO Autism: EDx workflow. Time from the first clinical question of developmental delay to autism diagnosis is the primary endpoint. Secondary endpoints explore clinician and caregiver experience of Device use. Methods: Children aged 18-72-months-olds with concern for developmental delay indicated by either a caregiver or health professionals were eligible to participate in this prospective observational study. Experienced ECHO Autism: EDx Clinicians were recruited to evaluate the inclusion of the Device as part of their diagnostic evaluations. Outcome data was collected via a combination of electronic questionnaires, standard clinical care record reviews and analysis of Device outputs. Institutional Review Board Approval was provided by the University of Missouri-Columbia (IRB assigned project number 2075722). Results: 80 children and seven clinicians completed the study. On average, time from clinical concern at study enrollment to final autism diagnosis was 39.22 days, compared to 180-264 day waits at adjacent specialist referral centers. The vast majority (93%) of caregivers reported being satisfied with the ECHO Autism: EDx plus Device evaluation their child received and endorsed that they would recommend it to others and that they felt comfortable using the Device. The Device produced determinate autism predictions or rule outs for 52.50% of participants, and in all cases these were consistent with the final clinical determination. Participating clinicians reported Device use was feasible and reduced several challenges associated with their previous diagnostic process, however, they noted it did not obviate the need for additional structured observation in every case. Conclusions: The ECHO Autism: EDx plus Device workflow offers considerable time savings compared to specialty center referral and was strongly endorsed by caregiver participants. Embedding the Device into the ECHO Autism: EDx workflow was feasible and helped streamline several workflow efficiencies. Clinicians still utilized their training and application and interpretation of DSM-5 criteria when formulating the diagnosis for indeterminate cases. Clinical Trial: ClinicalTrials.gov NCT05223374; https://www.clinicaltrials.gov/study/NCT05223374?term=NCT05223374&rank=1.