Scaling Clinical Quality with AI

How Headspace is Elevating Measurement-Based Care with AI

When it comes to behavioral health organizations leading the way in innovative care models, Headspace is at the forefront. Scaling high-quality care isn’t easy—especially as membership and provider networks grow. At Brellium, we’ve had the opportunity to work closely with Headspace and see how they’re prioritizing clinical quality and culturally responsive care.

Recently, Zach Rosen, Co-Founder and CEO at Brellium, sat down with Kenli Urruty, Senior Director of Care Quality Assurance and Training at Headspace, to discuss:

• How AI helps Headspace maintain high measurement-based care (MBC) standards at scale

• Practical strategies for improving clinical quality and compliance

• Best practices for integrating AI while keeping care patient-centered

Here’s what we learned:

Measurement-Based Care: The Backbone of Quality Therapy

At the core of Headspace’s approach is measurement-based care (MBC)—a system that ensures therapy is effective by tracking patient progress with standardized tools like PHQ-9 and GAD-7. These assessments give therapists data-driven insights, helping them personalize care and improve outcomes.

For Kenli and her team, MBC isn’t just about compliance—it’s about making sure every Headspace member gets meaningful, effective care.

Prioritizing Culturally Responsive Care

Providing high-quality care means addressing cultural differences. Headspace has embedded culturally responsive care into its platform, from provider training to clinical documentation.

One key initiative? Dedicated fields in clinical notes that encourage therapists to consider and document cultural factors, ensuring care is tailored to each patient’s background and experiences.

Using AI to Scale Quality Without Overburdening Providers

As Headspace’s provider network grows, maintaining clinical quality becomes more complex. That’s where AI comes in.

Headspace leverages AI to analyze clinical data, identify trends in MBC adherence, and ensure cultural competency. AI tools review clinical notes, track provider performance, and deliver actionable feedback. With Brellium, Kenli’s team can assess MBC adherence at scale—scanning 100% of charts to ensure best practices are followed and treatment plans align with objective assessments.

AI-Powered Training: Helping Providers Improve Faster

Access to real-time data allows Headspace to provide weekly feedback to providers, offering clear, concise insights on their performance and areas for improvement. This approach leads to faster responsiveness to coaching and higher-quality care.

In addition to chart reviews, Headspace uses Virtual Sapiens—an AI-powered coaching tool—to help providers strengthen their communication skills. Through simulated patient interactions, providers receive real-time feedback on their virtual presence, helping them build stronger therapeutic connections.

Thoughtful and Ethical AI Implementation

Rolling out AI in healthcare requires careful planning. Kenli shared key lessons from Headspace’s AI adoption journey, including:

• Transparent communication about AI’s role

• Ethical AI development to ensure patient safety

• Collaboration with legal, compliance, and security teams

• Clear messaging to providers on how AI supports—not replaces—them

This measured approach has led to successful adoption, from AI-assisted documentation to provider coaching tools.

How to Select the Right AI Tools for Your Healthcare Team

Not all AI solutions are created equal. Headspace follows a rigorous selection process to ensure they choose the right tools by:

• Conducting thorough needs assessments

• Evaluating multiple vendors

• Involving cross-functional teams in decision-making

• Balancing cost, efficiency, and impact

This careful vetting process helps ensure AI tools genuinely improve provider effectiveness and patient care.

Lessons Learned & Early Wins

Like any new technology, AI adoption comes with learning curves. Headspace has identified a few key factors for success:

• Allocating sufficient resources for implementation

• Continuously refining AI models and quality assurance processes

• Maintaining clear, ongoing communication with all stakeholders

The results so far are encouraging—since October, MBC adherence at Headspace has increased by 23%, leading to measurable improvements in provider consistency and patient outcomes.

The Future of AI in Mental Health Care

AI’s role in mental health care is expanding, but it’s not about replacing human connection—it’s about enhancing it. Headspace’s experience shows that when implemented thoughtfully, AI can help providers deliver more personalized, effective, and culturally competent care.

With the demand for mental health services growing, AI-driven innovations like those pioneered by Headspace offer a glimpse into the future: one where high-quality, patient-centered care is scalable, efficient, and continuously improving.

Listen to the Full Conversation

Get all the insights by watching the webinar on-demand here.