Development of a Novel Telemedicine Tool to Reduce Disparities Related to the Identification of Preschool Children with Autism.

The wait for ASD evaluation dramatically increases with age, with wait times of a year or more common as children reach preschool. Even when appointments become available, families from traditionally underserved groups struggle to access care. Addressing care disparities requires designing identific...

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Publicado en:Journal of Autism & Developmental Disorders Vol. 55; no. 1; pp. 30 - 43
Autores principales: Wagner, Liliana, Vehorn, Alison, Weitlauf, Amy S., Lavanderos, Ambar Munoz, Wade, Joshua, Corona, Laura, Warren, Zachary
Formato: research tables/charts Journal Article
Publicado: Springer Nature Jan2025
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jan2025
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      pub: Springer Nature
      place: New York, New York
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        atl: Development of a Novel Telemedicine Tool to Reduce Disparities Related to the Identification of Preschool Children with Autism.
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        au:
          Wagner, Liliana
          Vehorn, Alison
          Weitlauf, Amy S.
          Lavanderos, Ambar Munoz
          Wade, Joshua
          Corona, Laura
          Warren, Zachary
        affil: https://ror.org/05dq2gs74 Vanderbilt Kennedy Center, Treatment and Research Institute for Autism Spectrum Disorders, Vanderbilt University Medical Center, 1241 Blakemore Avenue, # 161, 37212, Nashville, TN, USA
      sug:
        subj:
          Autism Spectrum Disorder Diagnosis
          Telemedicine
          Healthcare Disparities
          Clinical Assessment Tools
          Human
          Male
          Female
          Child, Preschool
          Focus Groups
          Funding Source
          Machine Learning
          Early Intervention
          Race Factors
          Ethnic Groups
          Linguistics
          Health Services Accessibility
          United States
          Descriptive Statistics
          Child, Preschool: 2-5 years
          Male
          Female
      ab: The wait for ASD evaluation dramatically increases with age, with wait times of a year or more common as children reach preschool. Even when appointments become available, families from traditionally underserved groups struggle to access care. Addressing care disparities requires designing identification tools and processes specifically for and with individuals most at-risk for health inequities. This work describes the development of a novel telemedicine-based ASD assessment tool, the TELE-ASD-PEDS-Preschool (TAP-Preschool). We applied machine learning models to a clinical data set of preschoolers with ASD and other developmental concerns (n = 914) to generate behavioral targets that best distinguish ASD and non-ASD features. We conducted focus groups with clinicians, early interventionists, and parents of children with ASD from traditionally underrepresented racial/ethnic and linguistic groups. Focus group themes and machine learning analyses were used to generate a play-based instrument with assessment tasks and scoring procedures based on the child's language (i.e., TAP-P Verbal, TAP-P Non-verbal). TAP-P procedures were piloted with 30 families. Use of the instrument in isolation (i.e., without history or collateral information) yielded accurate diagnostic classification in 63% of cases. Children with existing ASD diagnoses received higher TAP-P scores, relative to children with other developmental concerns. Clinician diagnostic accuracy and certainty were higher when confirming existing ASD diagnoses (80% agreement) than when ruling out ASD in children with other developmental concerns (30% agreement). Utilizing an equity approach to understand the functionality and impact of tele-assessment for preschool children has potential to transform the ASD evaluation process and improve care access.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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