How Acclaim Autism cut patient onboarding from six months to four days with Appian

Acclaim Autism, a Philadelphia-based provider of specialist mental healthcare services, has used low-code process automation to dramatically reduce the time it takes to onboard vulnerable patients - cutting waiting times from up to six months to under a week.

Insurance rejection rates have also fallen from 80% of all initial applications to just 5%, while overall patient intake has grown from 3 to 47. That's a factor of 15.

The organization's Founder and President, Jamie Turner, says the transformation has changed what's possible:

We have finally gone from Google Sheets to something that's empowering my team and building a stronger, more sustainable future for us as an organization. Our increasing use of AI through this system is starting to do what we always wanted to do - humanize autism care.

A non-linear path to autism care

Turner is a former Wall Street finance IT professional who set up Acclaim Autism to create customized Applied Behavior Analysis (ABA) therapy plans for families seeking support with Autism Spectrum Disorder (ASD). The model is built around tailoring treatment to each child's unique needs, tracking measurable progress, and equipping parents to actively participate in their child's journey.

He came to it through an unconventional route:

I was in financial services and then decided I wanted to do something more socially minded and went to business school - but then did the opposite of what every business school graduate does. I went directly to work for a nonprofit in New York City.

After helping nonprofits become more efficient and supporting students with disabilities into college, Turner moved to Pennsylvania and encountered the chronic waitlists facing families seeking behavioural therapy for children with autism. He decided to do something about it.

A square peg in a round hole

Connecting an ASD specialist with a family carries significant administrative and compliance overhead. With legacy systems, that meant significant delays - and delays in early intervention carry real consequences for children's development and family wellbeing.

Turner's search for a technology fix took several wrong turns:

First we tried software built for this sector, but it was very narrow in scope. It checked the box on the medical basics - treatment plans, session notes - but it didn't have any of the operational things you'd expect, like schedule optimization or patient intake. I had a history with Salesforce, so I tried using that, but it was a square peg in a round hole. Then we tried full-stack development in Java, did a security audit, and it was just horrible.

Eventually, Turner turned to Appian's process automation platform, on the recommendation of a friend's IT consulting contact. An initial project delivered in a couple of months that a year of prior effort had failed to produce - at lower cost.

Three weeks to AI-powered document processing

A custom staff onboarding and training solution was the first build, automating the collection of clearances, qualifications, and licences required for new practitioners. From there, Turner worked with Appian's Customer Success team to layer in AI-powered document processing across the entire patient onboarding workflow.

It took three weeks.

The result is what Turner calls an autism diagnosis generative AI extraction application — a system that pulls diagnosis details, medical notes, and patient information directly from intake files, eliminating manual data re-entry and the errors that come with it. All processing is automatically HIPAA compliant.

Turner says:

It's just better than what I had before. It's increasing access to care. It simplifies operational complexity. And it helps us move faster. One of my favourite things is that once you build a process in Appian, the dashboard is just there. I can log into a portal, click, and see exactly how many people are in each stage of intake, where the gaps are, and nice charts on top of it.

Agentic AI is next

Turner's immediate focus is applying agentic AI to scheduling - one of the more complex operational challenges the organisation faces. Matching staff members to patients across a dispersed community involves more than 20 variables, including parent preferences and geography, with the goals of improving patient outcomes and maximizing billable hours.

He's characteristically understated about the ambition:

It's almost like we're making AI useful here.