Unlocking Value-Based Mental Health Care: Discover How AI Care Reduces Costs and Improves Outcomes
Mental health remains one of the most pressing—and expensive—challenges for employers and health plans. Traditional models rely on clinician time, making them costly and difficult to scale. Meanwhile, generic AI tools offer scalability without clinical oversight, leaving employees vulnerable. But what if you could combine the best of both worlds?
This whitepaper introduces AI Care—a revolutionary model from Sword that pairs the clinical rigor of licensed psychologists with the always-on, scalable power of AI. Discover how this breakthrough approach delivers measurable mental health outcomes at a fraction of the cost.
Inside the whitepaper
Learn how AI Care redefines mental health support by replacing outdated session-based care models with a value-based system tied to clinical outcomes. This report is essential reading for benefits leaders, health plan managers, and employers seeking sustainable mental health solutions.
What you’ll learn:
Why EAPs are failing: Even premium options drive negative ROI and can’t scale without high costs
The role of Phoenix AI: How Sword’s clinical AI bridges the gap between scalable and effective mental health care
How outcomes-based pricing works: Pay only when employees engage and get clinically better
Real-world results: AI Care extends clinician reach 3–5x, delivers evidence-based interventions, and adapts dynamically using biometric and contextual data
A new ROI model for mental health: Employers finally pay for results, not sessions
Get your copy of the “Unlocking Value-Based Mental Health” whitepaper
Learn how AI Care is shifting the mental health paradigm—empowering organizations to scale impact, cut costs, and drive real outcomes.
📩 Complete the form to receive the full report by email.
Companies like yours have trusted Mind to deliver impactful results
77%

saw a positive impact on their work performance
81%

experienced clinical improvement or recovery
88%

felt a mood lift after starting from a negative state
