For the first time, AI will be applied en masse to business-centric functions in the autism treatment industry. Beyond that more advanced trend, healthcare providers should expect AI to become an increasingly common part of clinical operations.
Multiple industry leaders told Autism Business News that clinicians will never be replaced by AI. But ultimately, they are essential to perfecting treatment planning and tracking, refining clinical decision-making, reducing administrative burden, increasing access to treatment, and improving diagnostic accuracy. Many people think that it can be a tool.
“I think the clinical efficiencies are going to be huge,” Brett Blevins, CEO and founder of Commonwealth Autism Care, told ABN while attending the 2024 Autism Investor Summit in Los Angeles. Told. “When you think about the practical applications of AI and what we do every day, I think clinical efficiency is probably at the top of the list.”
Commonwealth Autism Care provides in-facility, in-home and in-school services, including applied behavior analysis, and has seven locations in Georgia, Kentucky, North Carolina and Virginia, according to its website. It is operated.
Several powerful forces place a strain on autism treatment providers to be as efficient as possible, both individually and collectively. The widespread adoption of AI in business and society at large, combined with industry needs, makes its adoption inevitable, if not mandatory.
The most common use case for AI in autism treatment is to facilitate data collection, summarization, and analysis. Registered Behavioral Technicians (RBTs) and Board Certified Behavior Analysts (BCBAs) are required to collect data such as patient observations, documentation of goal progress, and tracking of specific interventions for clinical and reimbursement purposes. A speculative example would be a video- or audio-enabled AI-powered device that could observe the session and generate notes and other insights for the clinician to review and approve.
This may reduce the administrative burden and facilitate the management of the clinical burden of BCBAs. BCBAs are tasked with developing, implementing, supervising, and reevaluating treatment plans for patients with autism. They also supervise the RBT who administers the intervention according to the treatment plan. In addition to that, they often have to take on some administrative duties.
The already scarce supply of BCBAs is exacerbated by the enormous daily burdens they face, making support for BCBAs a critical need for autism treatment organizations to address.
“Think about what we do now: You create a treatment plan, provide a service, collect data, and then the BCBA needs to evaluate all that data and take different actions. to determine if there is and what needs to be done,” said Rob Marsh, CEO of Chatsworth. 360 Behavioral Health, a California-based autism treatment provider, told ABN. “A lot of that can be automated. … AI looks at all the variables that BCBAs never have time to look at in the time it takes to review cases and create progress reports and actually find the problem. I have no choice but to believe that it has the potential to help. I work to motivate my clients.”
360 Behavioral Health operates 21 locations in California and has two active locations in Nebraska.
Ultimately, the increasing amount of better clinical data enabled by AI could improve our understanding of the care provided at the patient, individual, and industry levels. AI-powered systems have the potential to improve quality of care by supporting treatment plan adherence and intervention fidelity. It also has the potential to identify the best approach for specific phenotypes of autism and related comorbidities.
“The B.C.B.A. [so many] It's very important for our students that AI can guide them in the right direction and find out what concerns they need to be aware of,” said Davidson, CEO and Founder of Chorus Software Solutions, a human services provider and Encore Support said Mordechai Meisels, Founder and Chief Clinical Officer of Services. he told ABN. “This could help us find RBTs, kids who need help, find kids who are doing well and nudge everyone towards mastery.”
Clinical AI tools can also expand access to diagnosis and identify interventions that patients require based on their specific combination of needs. According to Marsh, standardized assessments can be performed using his AI and other inputs, and combined with numerous other high-volume data sources to assess needs and customize treatment plans. .
“The BCBA may see [a need] And while they might narrow down their repertoire of things they've done in the past to five or six things, the AI will do hundreds of different things that it can consider,” Marsh said. “That way, the treatment plan can be more nuanced than what we originally had in mind and may be better applied and adopted by the client.”
Getting things right on the treatment front impacts the final outcome and the immediate experience of patients and families. According to Neil Hattangadi, his CEO at San Diego-based Cortica, this is especially true when he seeks to integrate multiple care types into one environment.
His company operates a clinic with several specialties related to autism treatment and also cares for people with other neurobifurcations. Cortica's homegrown technology system, called Axon, uses AI to assist with complex scheduling and “phenotypic matching between clinician and patient.” The latter tool helps distribute the workload of clinicians and ensures an even distribution of patients with different clinical difficulties.
“We're leveraging this very large dataset that spans metabolic data, genetic data, imaging data, long-term behavioral outcomes, and medical changes,” Hattangadi said. “Using that data, and based on this child's initial testing, test results, and genetic variation, we think they fit this phenotype of autism. So we're using this type of therapy. The most responsive.
Such insights are on the horizon, but are not yet a reality today, he added. Achieving these types of clinical insights would eliminate trial and error when it comes to medication management, especially in the context of other treatments.
