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.
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Acknowledgements
All authors contributed to the study conception and design. ZW obtained funding for the trial. LW and AV oversaw study execution, focus groups, measure creation, data management, and data analysis, with AW, LC, and ZW contributing to measure design. JW ran the computational analysis. AML led recruitment efforts and assisted with data entry and analysis. The first draft of the manuscript was written by LW, with contributions by AV, AW, and ZW. All authors read, commented on, and approved the final manuscript.
Funding
The study was supported by funding from NIH/NIMH (R21MH118539), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54 HD08321), and the Vanderbilt Institute for Clinical and Translational Research. The Vanderbilt Institute for Clinical and Translational Research (VICTR) is funded by the National Center for Advancing Translational Sciences (NCATS) Clinical Translational Science Award (CTSA) Program, Award Number 5UL1TR002243-03. This work was also supported with funding from the Learning Health Systems Scholars grant (K12 HS026395) from the Agency for Healthcare Research and Quality (AHRQ) and /Patient-Centered Outcomes Research Institute (PCORI).
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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.
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All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Analysis of existing clinical data was approved by the Institutional Review Board at Vanderbilt University Medical Center.
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Wagner, L., Vehorn, A., Weitlauf, A.S. et al. Development of a Novel Telemedicine Tool to Reduce Disparities Related to the Identification of Preschool Children with Autism. J Autism Dev Disord (2023). https://doi.org/10.1007/s10803-023-06176-3
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DOI: https://doi.org/10.1007/s10803-023-06176-3