Millions of people struggle with behavioral health conditions that remain undiagnosed or undertreated. AI could help find those patients sooner and connect them to more effective care.
In this episode of the AI at ViVE series on The Beat, host Sandy Vance talks with Jeremy Kreyling, the SVP of Healthcare Informatics at NeuroFlow, about where artificial intelligence genuinely belongs in behavioral health and where clinical judgment and human connection remain irreplaceable.
Jeremy explains how NeuroFlow uses machine learning across claims, EHR, assessment, and engagement data to identify hidden behavioral health risk, and why models built around an organization's own population can provide greater confidence than a one-size-fits-all approach. They also discuss how AI can help reduce the number of patients lost between identification, referral, and effective treatment.
In this episode, they talk about:
How AI can identify behavioral health needs that haven't yet been diagnosed
Why healthcare AI models should reflect the populations they're designed to serve
How machine learning can make behavioral health outreach more targeted
Why finding a provider isn't enough if the patient doesn't stay in care
How AI agents can take administrative work off highly trained care managers
Why behavioral health still lags physical health in analytics and integration
How measurement-based care can identify when treatment isn't working
Why AI should create more meaningful patient care, not simply more patient volume
A Little About Jeremy:
Jeremy Kreyling is the SVP of Healthcare Informatics at NeuroFlow, the market leader in behavioral healthcare technology infrastructure, where he leads analytics strategy to integrate behavioral health into value-based care models. He brings more than 20 years of leadership in healthcare data and analytics, with prior roles at NOCD, ProgenyHealth, and Change Healthcare. Jeremy holds an MBA from the University of Arizona and a B.S. in Information Systems from Arizona State University.
