Are these numbers right? That’s the question I asked my colleague Lora Perry about the new autism prevalence estimates—now estimating that 1 in 59 children have autism spectrum disorder (see Autism Rates & Autism Costs-A Future View). Her comment to me was that the rising numbers are may be due, in part, to better early childhood evaluations and diagnostic tools.
Her comment also made me take a look at what is happening with diagnostic tools for autism. There are a number of developments that have made their way to practice—and a number in the pipeline.
In the current service system, we have recent validation of telehealth-based evaluations. Remote telemedicine autism evaluations diagnosed children nearly as accurately as in-person assessment—in one test, remote psychologists identified 75% of the children with ASD; in a second test, the remote psychologists diagnosed ASD for about 64.4% of the children (see Autism Evaluations Conducted Via Telemedicine Nearly As Accurately As In-Person Assessment). About 91% of the families reported very high levels of satisfaction with the telemedicine consultation, and the telemedicine evaluation saved families an average of 3.9 hours of estimated travel time.
In February, Cognoa announced that the U.S. Food and Drug Administration (FDA) determined that Cognoa’s software for autism diagnosis and treatment is a Class II diagnostic medical device (see FDA Determines Cognoa Mobile Health Platform for Autism Diagnosis & Care Is A Class II Medical Device). The “device” is a software application/platform for a smartphone; the app consists of an artificial intelligence-based platform for pediatric behavioral health diagnostics and digital therapeutics, and has been clinically validated to identify autism in children as early as 18 months of age.
In the pipeline is the use of electroencephalograms (EEG) for autism diagnostic evaluations. EEG signals accurately predict autism spectrum disorder (ASD) in babies as early as three months of age—sensitivity, specificity, and positive predictive values exceeded 95% at some ages (see EEG Signals Accurately Predict Autism As Early As 3 Months Of Age). By analyzing readings from six different frequencies of the EEG, the researchers made predictions of ASD for each participant at each age studied: predictive accuracy by nine months of age was nearly 100%, and ASD severity was highly accurate by nine months of age.
And, most recently, a follow-up study of work originally done in 2017 that uses an algorithm to predict if a child has ASD based on metabolites in a blood sample, confirmed the original work. The original study identified children with autism 97.6% of the time, and the new study identified children with autism with 88% accuracy (see Success Of Blood Test For Autism Affirmed).
The challenge for health plans and provider organizations is deciding what approach to adopt and how to integrated the use of technology in the initial consumer evaluation process. To do this, executive teams need to ask themselves: what technologies should my organization invest in, that can be adapted to fit into existing service lines? This is where tech strategy and feasibility analysis come in. (For more on treatment technology strategy, see From Strategic Planning To Tech Strategy – Technology Trends & Decision-Making in Today’s Market Place. For more on new service line feasibility analysis, see How To Develop A New Service Line: Building A Diversification Strategy & Conducting A Feasibility Analysis.)
It’s too early to tell what the “most effective” new approach to diagnosing autism will be—but we do know that new technologies will be part of the equation. The task for executive teams is to be prepared.
For more on this topic, join OPEN MINDS Senior Associate Annie Medina on August 14 for her Executive Summit session, “Designing & Implementing Innovative Treatment Programs: An OPEN MINDS Executive Summit & Showcase,” at The 2018 OPEN MINDS Management Best Practices Institute.