Early accurate identification and treatment of young children with Autism Spectrum Disorder (ASD) represents a pressing public health and clinical care challenge. Given mounting evidence that early, accurate diagnosis of ASD is possible and very young children who receive intervention can demonstrate substantial gains in functioning, current American Academy of Pediatrics practice guidelines endorse formal ASD screening at 18 and 24 months of age. Ideally, this recommended screening would translate into early diagnosis and treatment for most children. Unfortunately, large numbers of children are still not screened for ASD; waits for specialized diagnostic assessment can be very long; and the average age of diagnosis in the US remains between 4 to 5 years of age. Further, groups from traditionally underserved communities are even more likely to be diagnosed at later ages. Delays in accurate diagnosis contribute to high levels of family stress and limit access to ASD behavioral intervention services, which are increasingly recognized as very important to children?s functioning. The current project tests a novel e-health system (?Autoscreen?) for rapid screening, diagnostic triage, referral, and treatment engagement of young children within community pediatric settings, with an embedded focus on reaching underserved populations. This tool fuses advanced computational strategies with expert knowledge and tools for ASD screening/assessment in order to realize an e-health application that not only provides support for enhanced screening across diverse communities, but also establishes a clear decision pathway for early, accurate diagnostic triage and meaningful engagement in early treatment. We will integrate (a) our computational analyses of gold-standard assessments conducted across a very large-sample of toddlers with ASD and other developmental concerns with (b) a novel interactive assessment designed to target core areas of earliest ASD vulnerability, in order to create an e-screening application that can guide early action within traditional community pediatric settings. We will rigorously evaluate the feasibility, usability, compatibility, and potential utility of this tool in the current work. This evaluation will be conducted in order to ultimately create and test the efficacy of this innovative e-health product in terms of identifying and linking children with ASD more rapidly to meaningful interventions at young ages. We believe the potential market for such a system is quite substantial with common use developmental screening instruments identifying roughly 7-9% of children in first year of life. In this capacity, our tool will ultimately be designed as a practical, feasible, and reimbursable health-screening module for wide-scale use in community pediatric settings.
Early accurate identification and treatment of young children with Autism Spectrum Disorder (ASD) represents a pressing public health and clinical care challenge. The current project tests a novel e-health system (?Autoscreen?) for rapid screening, diagnostic triage, referral, and treatment engagement of young children within community pediatric settings, with an embedded focus on reaching underserved populations. This tool fuses advanced computational strategies with expert knowledge and tools for ASD screening/assessment in order to realize an e-health application that not only provides support for enhanced screening across diverse communities, but also establishes a clear decision pathway for early, accurate diagnostic triage and meaningful engagement in early treatment.
