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How AI Innovation Can Break Down Barriers in the Crisis of Late Diagnosis of Autism in Children




The medical community agrees that, for children with developmental delays such as autism, early is everything. Early diagnosis and early interventions have been shown to make life-changing differences. With as many as 25% of children at risk for a developmental delay in the U.S. alone, there is no debate that it is our duty as a nation to provide early, accessible, and equitable care for all children and their families.

Yet, as demonstrated in a recent report, entitled “The State of Pediatric Autism Diagnosis in the U.S.: Gridlocks, Inequities, and Missed Opportunities Persist”, long wait times and barriers to care in pediatric specialty centers across the U.S. have reached crisis level. The survey – designed and conducted by Scott Badesch, Former President of the Autism Society of America, and sponsored by my company, Cognoa – reveals the inefficiencies and inequities in the status quo processes for evaluating and diagnosing developmental delays and autism. From unnecessarily complex and lengthy diagnostic processes and a dwindling specialist workforce to inadequate reimbursement and access, the report's findings confirm an unfortunate reality: we, as a nation, are failing our children.

While the current state of affairs appears grim, technological innovations can offer a significant and immediate reprieve from a number of these significant barriers.

The healthcare community, along with payors, policymakers, and technologists, need to act swiftly to ensure such technology is made available, and affordable, to address these dire issues and ensure no more children are left behind.

Early is Everything – The Lifelong Effects of Diagnostic Delays

The CDC estimates that autism affects 1 in 36 children in the U.S. today.

Although autism can be reliably diagnosed as early as 18 months, the average age of diagnosis has remained stagnant at over 4 years of age. Non-white children, females, and those from rural areas or disadvantaged socio-economic backgrounds are often diagnosed even later or missed altogether.

Early interventions during the critical early neurodevelopmental period, before the age of 3, have been shown to dramatically improve lifelong outcomes for children. In fact, studies have even shown that 4 out of 10 children diagnosed with autism at age 2-3 have such significant improvements with early intervention that the diagnosis loses relevance by age 6. In contrast, lack of or delayed treatment increases the likelihood of lifelong, comorbid mental health conditions, while all-cause medical costs are approximately double for children who experience a longer time to diagnosis compared with a shorter time to diagnosis.

Process Paralysis

As highlighted in the survey report, long wait times for autism diagnosis at pediatric specialty care centers across the U.S. are unacceptably common. According to the study, some 61% of specialty centers surveyed report wait times of over 4 months between the initial request for an autism evaluation and the evaluation itself. Disturbingly, 25% reported waitlists stretching to over half a year, and 21% reported waitlists extending beyond a year or were so inundated with requests that they could no longer accept new referrals.

One of the primary factors exacerbating this issue, as identified by 77% of clinics in the study, is the extreme length of assessment processes and heavy documentation burdens that clinicians must shoulder. 69% of clinics also identified staffing issues, including clinician and administrator shortages, and an alarming 43% cited burdensome reimbursement processes, lack of reimbursement, and a refusal to accept Medicaid and commercial insurance as significant barriers.

Accessibility to diagnostic centers is another constraint compounding the problem. Over 80% of counties in the U.S. do not have diagnostic centers for autism within commuting distance, making it even more challenging for families living in these areas to receive timely interventions.

A lack of standardization in the diagnostic process presents further challenges. More than 30 different tools are used across the country with the types of assessment and provider required for the autism diagnosis to be recognized varying from state-to-state and payer-to-payer. Additionally, 83% of centers report that autism evaluations take over 3 hours, with 25% reporting completion times of over 8 hours. This lack of efficiency and standardization only serves to keep children and their families in limbo for longer.

AI Busting Barriers

Rather than referring all children for specialist evaluations, the American Academy of Pediatrics (AAP) has stressed that pediatricians in primary care “can significantly affect the outcome of children with autism by making an early diagnosis and providing referral for evidenced-based behavioral treatment” in the primary care setting – rather than referring all children for specialist evaluations. As the first line of care for children, pediatricians are well positioned to play a significant role in tackling diagnostic delays and ensuring early interventions when they matter most.

Harnessing the power of AI and technological innovations can empower pediatricians to evaluate, diagnose, and manage children with developmental delays and autism accurately and rapidly. AI has the immense potential to help streamline and standardize the autism evaluation and diagnostic process, address physician shortages by expanding the pool of providers who can evaluate and diagnose children, and ensure wider, more equitable and timely access to care across primary and specialty care settings.

AI is able to assess thousands of human traits and features – including a variety of verbal and movement indicators – to identify the most predictive traits that point toward autism. AI can make the diagnosis journey more efficient and equitable by reducing time-intensive evaluations that are often unnecessary for many children and ensure a more consistent approach to diagnosis across all demographics.

AI can therefore make healthcare more accessible, affordable, and effective, but only if it is developed responsibly – AI systems are only as good as the data they are built upon. Diversity is crucial to  account for differences in gender, race, ethnicity and socio-economic backgrounds.  Programmers should constantly test and correct algorithms to ensure data is up-to-date and truly representative of all demographics. In practical terms, that means employing coders from a diverse range of backgrounds, and encouraging them to feed new, diverse samples into their code. This is extremely important in the diagnosis of autism for many reasons, not the least of which is that girls with autism show different traits than boys and on average are diagnosed  1.5 years later than boys.

While AI can undoubtedly address inherent limitations, create efficient, safe and life altering solutions, it must also go hand in hand with the human touch. AI serves to support physicians so they can provide answers faster and more effectively based on massive amounts of data and allows them to spend their time more productively with their patients.

The Road Ahead

Emerging innovations reveal a promising future in the diagnosis of autism, but only if optimized and utilized across appropriate settings. Policies that support reimbursement and access to AI-driven solutions approved by the FDA must be adopted. When responsibly trained on unbiased, population representative data, AI can empower more providers to confidently evaluate, diagnose, and manage children within primary care.

The goal is clear – we need to provide every child with the opportunity for early intervention. The medical community is in unanimous agreement.  The data is unequivocal.  The technology exists.  It’s time to buck the status quo and call on our policymakers, healthcare leaders, insurers, and technologists to prioritize and address the issues surrounding the diagnosis of children with developmental delay and autism. We can actively do better.

Dr. Sharief Taraman is Chief Executive Officer of Cognoa, a leading pediatric behavioral health and data company developing AI-based technologies to enable early and equitable diagnosis and care for children living with developmental and behavioral health conditions. Dr. Taraman previously served as Cognoa’s Chief Medical Officer and brings nearly two decades of clinical specialization in neurodevelopmental conditions, clinical informatics, and healthcare innovation.