WEBINAR | Building the Data Foundation for AI in Behavioral Health: From Measurement to Action

Kathleen “Kathi” Cox, COO, Ambulatory & Virtual Channel, Texas Health Resources; David Bartley, Chief Solutions Officer; and Andy Flanagan, CEO, Iris Telehealth. As health systems race to modernize behavioral health, one truth has become clear: meaningful innovation — including AI — cannot happen without a strong data foundation. Behavioral health has long relied on narrative documentation and variable workflows, making it difficult to measure outcomes, predict risk, or design care models that scale. This session brings together health system, product, and executive leaders to explore how measurement-based care (MBC), structured data, and enterprise analytics create the conditions for smarter, more proactive behavioral health delivery.

Panelists will discuss how MBC serves as the behavioral health “source of truth,” why analytics is essential for revealing access gaps and outcome variation, and how AI can responsibly accelerate insight only when the underlying data is consistent, clinically relevant, and trustworthy. Drawing from real-world examples — including work underway at Texas Health Resources — this conversation will ground the future of behavioral health AI in practical steps organizations can take today to improve data quality, strengthen integration, and prepare for the next era of care.

Key Takeaways

1. Understand why behavioral health lags behind.
Leaders will learn why BH has historically lacked structured data, and how this gap limits everything from care coordination to predictive modeling.

2. Build the foundation before the future.
Discover how MBC and structured clinical inputs create the digital infrastructure required for high-quality analytics and safe, responsible AI.

3. Turn insight into operational change.
See how analytics can reveal access barriers, workflow variation, and avoidable costs — and how leading systems translate these signals into redesigned staffing, follow-up pathways, and service models.

4. Use AI where it drives real value.
Learn which AI use cases meaningfully augment clinical care (risk visibility, care prioritization, crisis prevention) — and which remain speculative or unsafe without better data.

5. Learn from systems doing the work today.
Hear how organizations like Texas Health Resources are modernizing behavioral health data and integrating MBC and analytics into operational decision-making.

6. Leave with a “start now” roadmap.
Walk away with concrete steps any system can take in the next 90 days to improve BH data quality, strengthen measurement, and prepare for AI-enabled care.