For revenue cycle teams in behavioral health, eligibility issues rarely present as isolated tasks. A single denial can set off a chain of manual work: verifying coverage across payer portals, coordinating updates across multiple systems, and reaching out to members—all while trying to prevent delays in billing and preserve a positive member experience.
At Headspace, these workflows were both operationally critical and highly complex. This meant they were also incredibly hard to automate—until the team discovered high-precision AI agents for behavioral health trained on exactly these processes.
Nancy Le, who leads pre-access and member billing, describes success in her role as a balance between operational efficiency and member experience. Her team is responsible for ensuring that coverage is verified accurately upfront and that members are billed correctly and in a timely manner. When that process breaks down, the impact is felt on both sides.
“We want to submit the claim, have it process, and bill the member in a timely manner,” she said. “If claims keep denying, that delays everything—and we don’t want members getting billed months later for something they thought was already resolved.”
Before introducing automation, termed eligibility workflows followed a familiar but labor-intensive path. If a member’s insurance had terminated and the issue was not caught in advance, the claim would return as denied or rejected. From there, Nancy’s team would manually verify eligibility, log into payer portals, confirm coverage status, reach out to the member for updated insurance information, and update multiple internal systems accordingly.
All of this work was handled by a small team, often navigating several systems to complete a single case. “We have multiple systems where we store information,” Nancy explained. “You have to go here to pull one piece of data, then go somewhere else, and then go somewhere else again to email the member.”
The complexity wasn’t just in the number of systems, but in the coordination between them. Headspace operates with a combination of external payer portals and internal platforms. Certain actions must happen in a precise order, and exceptions are common due to payer-specific rules or unique contractual nuances.
“The order was very important,” Nancy said. “You had to do this step first before that step. And there were so many different scenarios—we had flowcharts with arrows going everywhere.”
To address this, Headspace began working with Magical to automate the termed eligibility workflow. Rather than focusing on a single task, the approach was to replicate the full process—from identifying terminated coverage to updating systems and initiating member communication.
Today, much of that workflow is handled end to end with over 95% accuracy by AI agents using the Magical platform. The system verifies eligibility, performs insurance discovery to identify alternative coverage, updates internal records, and sends outreach to members when needed. Nancy noted that this comprehensive approach was a key differentiator compared to other tools the team had evaluated.
“We had looked at solutions that could verify insurance or tell us the denial reason,” she said. “But we would still have to go in and do all the work ourselves afterward. This was the first time we saw something that could actually handle the full process from start to finish.”
Nancy described an initial skepticism toward AI more broadly, driven by uncertainty around how it would handle detailed, real-world workflows. What changed that perspective was visibility into how the system operated and the ability to iterate quickly.
“Seeing the test runs step by step really helped,” she said. “You can see exactly what it’s doing at each point. And when we asked for changes—even very small ones—they were implemented quickly. That made it easier to trust the process.”
In some cases, the system has also surfaced opportunities that would have been difficult to capture manually. Because insurance discovery is performed as part of the workflow, it has already identified alternative coverage that prevented unnecessary outreach and billing.
“There was a scenario where it found new insurance for us,” Nancy said. “That saved an outreach, avoided a confusing bill for the member, and saved work for our team.”
While initial volumes for termed eligibility have been lower than expected due to upstream improvements in denial prevention, the accuracy and transparency of the Magical platform has led Headspace to explore expanding automation into adjacent workflows, particularly those tied to member communications.
“We’re excited to expand,” Nancy said. “It’s essentially building on what’s already working for us today.”
For teams evaluating similar approaches, Nancy emphasizes the importance of maintaining an open mindset and taking advantage of structured testing before deployment.
“Don’t feel overwhelmed by it,” she said. “There are ways to test, validate, and make sure it’s doing what you want before you fully roll it out. Once you see it in action, it changes how you think about what’s possible.”
As healthcare organizations continue to navigate increasingly complex operational environments, the ability to coordinate workflows across systems—and do so consistently—remains a persistent challenge. For Headspace, the shift has not simply been about reducing manual effort, but about building a more reliable and scalable way to execute critical processes that directly impact both revenue and the member experience.
To learn more about how Magical applies agentic AI to automate administrative workflows across healthcare operations, visit getmagical.com.
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