Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Dec 8:10.1007/s10803-023-06176-3.
doi: 10.1007/s10803-023-06176-3. Online ahead of print.

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

Affiliations

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

Liliana Wagner et al. J Autism Dev Disord. .

Abstract

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.

Keywords: Autism spectrum disorder; Preschool children; Tele-assessment; Telemedicine.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest Liliana Wagner, Laura Corona, Amy Weitlauf, and Zachary Warren are all co-authors of the TELE-ASD-PEDS. They do not receive compensation for the use of this instrument.

Similar articles

Cited by

References

    1. Bishop CM (2006). Pattern recognition and machine learning, Springer.
    1. Bishop-Fitzpatrick L, & Kind AJH (2017). A Scoping review of health disparities in autism spectrum disorder. Journal of Autism and Developmental Disorders, 47(11), 3380–3391. 10.1007/s10803-017-3251-9 - DOI - PMC - PubMed
    1. Bone D, Goodwin MS, Black MP, Lee CC, Audhkhasi K, & Narayanan S (2015). Applying machine learning to facilitate autism diagnostics: Pitfalls and promises. Journal of Autism and Developmental Disorders, 45(5), 1121–1136. 10.1007/s10803-014-2268-6 - DOI - PMC - PubMed
    1. Chlebowski C, Robins DL, Barton ML, & Fein D (2013). Large-scale use of the modified checklist for autism in low-risk toddlers. Pediatrics, 131(4), e1121–e1127. 10.1542/peds.2012-1525 - DOI - PMC - PubMed
    1. Chunara R, Zhao Y, Chen J, Lawrence K, Testa PA, Nov O, & Mann DM (2021). Telemedicine and healthcare disparities: A cohort study in a large healthcare system in New York City during COVID-19. Journal of the American Medical Informatics Association: JAMIA, 28(1), 33–41. 10.1093/jamia/ocaa217 - DOI - PMC - PubMed

LinkOut - more resources