A Digital Health Solution to the Wait Time Crisis for ASD

Background: The average age of autism spectrum disorder (ASD) diagnosis is after 4 years despite the fact that children with ASD can be diagnosed as early as 2 years old and despite evidence that parents may detect developmental concerns in children with ASD before 12 months. Barriers to timely diagnosis include the large time burden and cost of comprehensive assessments, shortage of providers, and lack of resources in primary care settings. Delayed diagnosis results in delayed entry into early intervention programs and in many parts of the United States, parents wait over 1 year for services. Moreover, in many parts of the world, there may be no diagnostic assessment and no intervention services available at all. To address the problem of delayed access to intervention services, we developed a digital health platform that supports the delivery of parent-mediated Naturalistic Developmental Behavioral Interventions (NDBI)⁷. This scalable tool is intended to help parents and caregivers learn how to implement evidence-based interventions with their children while they are on lengthy wait lists for services. The tool uses microlearning approaches powered by an artificial intelligence (AI) framework that supports structured conversation, natural language processing and real time analytics. The tool supports training and practice, coaching, and measurement of fidelity as well as target goal identification and tracking. Training content comprises critical skills and strategies that are considered universal to all NDBI’s.

Objectives: This technology presentation aims to 1. Report on a Quality Improvement project that utilizes this system; and 2. To demonstrate this technology to audience members at INSAR. Specifically, audience members will interact with a chatbot that will teach them critical skills required to implement NDBI’s.

Methods: We initiated a quality improvement project in an autism provider network in the United Kingdom (Blue Sky Autism Project) to assess 1. The ease of technical and clinical workflow integration; 2. Acceptability and feasibility among clinicians who served as coaches for the families. As part of the QI project, we determined completion rates of users and collected feedback from clinicians who served as coaches.

Results: Integration of the system into the provider network’s workflow was uneventful and successful. Provider network clinicians trained in the system rated it as acceptable, feasible, and described high user satisfaction.

Conclusions: Scalable digital health tools hold promise to deliver evidence-based training for parent-mediated interventions for autism spectrum disorder. This approach of large-scale dissemination of evidence-based practice may allow parents to develop the capacity to start using basic interventions while they wait for clinic-based services.