Early Identification of ASD Through Telemedicine: Potential Value for Underserved Populations
- 1.1k Downloads
Increasing access to diagnostic services is crucial for identifying ASD in young children. We therefore evaluated a telemedicine assessment procedure. First, we compared telediagnostic accuracy to blinded gold-standard evaluations (n = 20). ASD cases identified via telemedicine were confirmed by in-person evaluation. However, 20% of children diagnosed with ASD in-person were not diagnosed via telemedicine. Second, we evaluated telediagnostic feasibility and acceptability in a rural catchment. Children (n = 45) and caregivers completed the telemedicine procedure and provided feedback. Families indicated high levels of satisfaction. Remote diagnostic clinicians diagnosed 62% of children with ASD, but did not feel capable of ruling-in or out ASD in 13% of cases. Findings support preliminary feasibility, accuracy, and clinical utility of telemedicine-based assessment of ASD for young children.
KeywordsAutism spectrum disorder Diagnosis Telemedicine Young children
APJ, AS, AW, and ZW conceived of the study and crafted the experimental design. APJ and AS provided oversight of study implementation across Vanderbilt sites. AW, NB, AN, JH, and AS helped design and implement the rapid diagnostic procedure and methods for capturing diagnostic agreement. AP and AS assisted with data collection and analysis for manuscript preparation. APJ, AW, and ZW significantly participated in drafting the article, revising it critically, and providing final approval of the manuscript. All authors are in agreement with accountability for all aspects of the work.
This project was completed with Hobbs Foundation funding through the [funding source redacted for blinded review], support from the Eunice Kennedy Shriver National Institute of Child Health and Human Development U54 HD08321, support from the [funding source redacted for blinded review] CTSA award No. UL1TR000445 from the National Center for Advancing Translational Sciences, and support from the [funding source redacted for blinded review] Department of Education/[funding source redacted for blinded review] Early Intervention System. Its contents are solely the responsibility of the authors and do not necessarily represent official views of any funding agency. All procedures performed in studies involving human subjects were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent/assent was obtained from all individual participants included in the study. Conflict of interest: the authors declare that they have no conflict of interest.
Compliance with Ethical Standards
Conflict of interest
All authors declare that they have no conflict of interest.
- Chawarska, K., Shic, F., Macari, S., Campbell, D. J., Brian, J., Landa, R., & Young, G. S. (2014). 18-month predictors of later outcomes in younger siblings of children with autism spectrum disorder: A baby siblings research consortium study. Journal of the American Academy of Child & Adolescent Psychiatry, 53(12), 1317–1327.CrossRefGoogle Scholar
- Christensen, D. L., Baio, J., Braun, K. V., Bilder, D., Charles, J., Constantino, J. N., & Yeargin-Allsopp, M. (2016). Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and developmental disabilities monitoring network, 11 sites, United States. Morbidity and Mortality Weekly Report: Surveillance Summaries, 65(3), 1–23.Google Scholar
- Cisco Systems. (2015). Cisco Jabber (Version 4.8) [Telecommunications platform]. San Jose, CA: Cisco Systems.Google Scholar
- Dawson, G., Jones, E. J., Merkle, K., Venema, K., Lowy, R., Faja, S., Kamara, D., Murias, M., Greenson, J., Winter, J., Smith, M. (2012). Early behavioral intervention is associated with normalized brain activity in young children with autism. Journal of the American Academy of Child & Adolescent Psychiatry, 51(11), 1150–1159.CrossRefGoogle Scholar
- Durkin, M. S., Maenner, M. J., Meaney, F. J., Levy, S. E., DiGuiseppi, C., Nicholas, J. S., Kirby, R.S., Pinto-Martin, J.A., Schieve, L. A. (2010). Socioeconomic inequality in the prevalence of autism spectrum disorder: Evidence from a US cross-sectional study. PLoS One, 5(7), e11551.CrossRefPubMedPubMedCentralGoogle Scholar
- Interagency Autism Coordinating Committee. (2013). IACC strategic plan for autism spectrum disorder research: 2013 update. US Department of Health and Human Services Interagency Autism Coordinating Committee.Google Scholar
- Lindgren, S., Wacker, D., Suess, A., Schieltz, K., Pelzel, K., Kopelman, T., Lee, J., Romani, P., Waldron, D. (2016). Telehealth and autism: Treating challenging behavior at lower cost. Pediatrics, 137(Supplement 2), S167–S175.Google Scholar
- Liptak, G. S., Benzoni, L. B., Mruzek, D. W., Nolan, K. W., Thingvoll, M. A., Wade, C. M., & Fryer, G. E. (2008). Disparities in diagnosis and access to health services for children with autism: Data from the National Survey of Children’s Health. Journal of Developmental & Behavioral Pediatrics, 29(3), 152–160.CrossRefGoogle Scholar
- Lord, C., Rutter, M., DiLavore, P., Risi, S., Gotham, K., & Bishop, S. (2012). Autism diagnostic observation schedule, second edition (ADOS-2). Torrance, CA: Western Psychological Services.Google Scholar
- McPheeters, M. L., Weitlauf, A., Vehorn, A., Taylor, C., Sathe, N. A., Krishnaswami, S., Fonnesbeck, C., Warren, Z. E. (2016). Screening for autism spectrum disorders in young children: A systematic evidence review for the U.S. Preventive Services Task Force. Rockville, MD: Agency for Healthcare Reseach and Quality (US).Google Scholar
- Mullen, E. M. (1995). Mullen scales of early learning. Circle Pines, MN: American Guidance Service.Google Scholar
- Sparrow, S. D., Cicchetti, D. V., & Balla, D. A. (2005). Vineland-II adaptive behavior scales: Survey forms manual. Circle Pines, MN: American Guidance Service.Google Scholar
- Stenberg, N., Bresnahan, M., Gunnes, N., Hirtz, D., Hornig, M., Lie, K. K., Lipkin, W. I., Lord, C., Magnus, P., Reichborn-Kjennerud, T., Schjølberg, S. (2014). Identifying children with autism spectrum disorder at 18 months in a general population sample. Paediatric and Perinatal Epidemiology, 28(3), 255–262.CrossRefPubMedPubMedCentralGoogle Scholar
- Suess, A. N., Romani, P. W., Wacker, D. P., Dyson, S. M., Kuhle, J. L., Lee, J. F.,… Waldron, D. B. (2014). Evaluating the treatment fidelity of parents who conduct in-home functional communication training with coaching via telehealth. Journal of Behavioral Education, 23(1), 34–59.CrossRefGoogle Scholar
- Warren, Z. E., & Stone, W. L. (2011). Clinical best practices: Diagnosis and assessment of young children. In D. Amaral, G. Dawson & D. Geschwind (Eds.), Autism spectrum disorders (pp. 1269–1280). New York: Oxford University Press.Google Scholar
- Wiggins, L. D., Baio, J., & Rice, C. (2006). Examination of the time between first evaluation and first autism spectrum diagnosis in a population-based sample. Journal of Developmental & Behavioral Pediatrics, 27(2), S79–S87.Google Scholar
- Zwaigenbaum, L., Bauman, M. L., Stone, W. L., Yirmiya, N., Estes, A., Hansen, R. L., McPartland, J.C., Natowicz, M.R., Choueiri, R., Fein, D., Kasari, C. (2015). Early identification of autism spectrum disorder: Recommendations for practice and research. Pediatrics, 136(Supplement 1), S10–S40.CrossRefPubMedGoogle Scholar