The COVID-19 pandemic has caused unprecedented disruptions to healthcare, including direct impacts on service delivery related to autism spectrum disorder (ASD). Caregiver-mediated tele-assessment offers an opportunity to continue services while adhering to social distancing guidelines. The present study describes a model of tele-assessment for ASD in young children, implemented in direct response to disruptions in care caused by the COVID-19 pandemic. We present preliminary data on the outcomes and provider perceptions of tele-assessments, together with several lessons learned during the period of initial implementation.

Telemedicine tools have potential for increasing access to diagnostic services for children with autism spectrum disorder (ASD). Past work has utilized tele-assessment procedures in which remote psychologists observe administration of interactive screening instruments by trained, on-site providers. Although promising, this approach relies on two clinicians, limiting its efficiency and scalability. The present study examined the use, acceptability, and parents' perceptions of two caregivermediated tools for assessing ASD risk in toddlers, in which remote clinicians guided parents to complete interactive screening activities with their children. Most parents found tele-assessment to be comfortable, and many reported liking the parent-led nature of these tools. Parents also offered constructive feedback, which was used to modify the tele-assessment process for future study. Keywords Autism spectrum disorder • Diagnosis • Telemedicine • Young childrenAs the prevalence of autism spectrum disorder (ASD) has increased, so too have needs for screening, diagnosis, and intervention access for young children. Current prevalence estimates indicate that one in 54 children has ASD (Maenner 2020). To maximize early identification and intervention, American Academy of Pediatrics guidelines recommend combining routine developmental surveillance at all pediatric visits with standardized autism-specific screening for all children at 18 and 24 months of age, as well as at any point that caregivers report concerns (Hyman et al. 2020).

Barriers to identifying autism spectrum disorder (ASD) in young children in a timely manner have led to calls for novel screening and assessment strategies. Combining computational methods with clinical expertise presents an opportunity for identifying patterns within large clinical datasets that can inform new assessment paradigms. The present study describes an analytic approach used to identify key features predictive of ASD in young children, drawn from large amounts of data from comprehensive diagnostic evaluations. A team of expert clinicians used these predictive features to design a set of assessment activities allowing for observation of these core behaviors. The resulting brief assessment underlies several novel approaches to the identification of ASD that are the focus of ongoing research.