Breaking the Chains: AI, Addiction, and the Opioid Crisis | Mike Vasquez | #TGV537

In this powerful episode, we dive deep into the opioid crisis with Mike Vasquez in conversation with Naveen Samala on #TGV537 The Guiding Voice

Key Takeaways:

• Context Setting, TGV Introduction, and First rapid-fire round of random words
• Top 3 things that contributed to Mike’s success
• Mike reflects on the life experiences that have driven him to innovate, the importance of executing ideas, and how responding to challenges has been a cornerstone of his journey.
• Discover how AI is revolutionizing opioid addiction prevention, starting with the analysis of medical records to detect early indicators of dependency. Mike explains how targeting the root causes and doctors’ prescribing patterns can prevent addiction before it begins
• Mike opens up about how his son's battle with drug addiction inspired him to create one of the largest drug treatment programs in the U.S. He shares the pivotal moments that shaped his approach to addiction treatment.
• Running a large-scale treatment program has given Mike a unique perspective on addiction. He discusses the key lessons he’s learned and how they can be applied on a national scale.
• Mike emphasizes that the opioid crisis is a chemistry problem, not a character flaw. This distinction reshapes how we should approach both treatment and prevention, moving from judgment to science.
• Learn about the hidden indicators in medical records that could predict opioid dependency before it starts.
• Witty answers to second rapid-fire questions
• Trivia about the less-known facts about the Opioid crisis

About the guest:

Mike Vasquez, with a 32-year career in healthcare technology, founded the first EMR company with integrated AI to go public on NASDAQ. He and his wife also operated one of the nation's largest drug treatment and opioid detox programs. He recently authored The Untold Story of the Opioid Crisis in American Healthcare, revealing how early indicators of opioid dependency were hidden in medical records.

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