Development of a Novel Telemedicine Tool to Reduce Disparities Related to the Identification of Preschool Children with Autism.
Liliana Wagner, Alison Vehorn, Amy S Weitlauf and 4 others
PMID 38064003WHAT IT FOUND
A new telehealth tool correctly identified autism in 63% of preschoolers when used alone.
It worked better for confirming autism in minimally verbal children than for ruling it out. Caregivers were satisfied, but clinicians said they still need a full history to be sure.
Key findings
01The tool accurately classified diagnoses in 63% of cases when used in isolation without history or other information.
02Diagnostic accuracy was much higher for confirming existing autism diagnoses (80%) than for ruling out autism in children with other developmental concerns (30%).
03All clinicians said they needed parent report and developmental history in addition to the assessment tasks to form a definitive impression.
STILL TO COME
How it was doneWhat they foundWhat it means for SLPs
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What it does not show
The pilot was very small (30 children) and used children who already had a diagnosis, so it does not show how the tool works for children coming in for a first-time evaluation. Clinicians were blinded to diagnoses and could not use the child's history, which is an artificial condition that does not reflect real-world practice. The tool was not tested for its ability to rule out autism effectively, showing poor accuracy for children with other developmental delays.
Declared interests
No specific funding sources or conflicts of interest were declared in the provided text.
The easy way to misread this
Do not use this tool to rule out autism. The study showed it was only 30% accurate at correctly identifying children who did not have autism. A negative result from this assessment does not mean a child is free of autism.