Mobilized Technology for Rapid Screening and Clinical Prioritization of ASD
Autism rates continue to rise with more and more children being referred for autism screening every day. The behavioral tests currently administered for diagnosis are several hours long and the diagnosis process as a whole is cumbersome for families. In addition, clinical professionals capable of administering the exams tend to be too few and well above capacity. The average time between initial evaluation and diagnosis for a child living in a large metropolitan area is greater than one year and approaches five years for families living in more remote areas. The delay in diagnosis is not only frustrating for families, but prevents many children from receiving medical attention until they have past developmental time periods when behavioral therapy would have had appreciable impact. To combat this significant public health challenge, Dr. Wall has developed algorithms that rapidly analyze a short set of parent/caregiver-directed questions and a 2-5 minute video of the subject to yield an accurate classification of on or off the autism spectrum. The algorithms are derived from two of the most reliable and widely used behavioral instruments, the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS) and can be administered in minutes as compared to the hours typically required for delivery of the current methods. This study was designed to test the potential of these highly abbreviated approaches directly in a high-volume clinical facility at Children’s Hospital Boston. Through the use of a mobilized web and iPad-friendly framework, Dr. Wall’s group enrolled 200 or more children, a majority of which met standard clinical criteria for an autism diagnosis, and a smaller but sizable percentage of which were children with other developmental delays. In so doing, it was possible to measure both the sensitivity and specificity of these algorithms and to evaluate the efficacy of the mobilized approach for assisting the clinical diagnostic process overall. The hope is that the work will bring us closer to a comprehensive technology that can provide rapid assessments and enable patient prioritization at the nearest and most appropriate clinical care facilities, and a technology that increases the reach to a larger percentage of the risk population to ensure timely delivery of therapies.
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