
Reimagining Healthcare in the Age of AI with Sheila Phicil
Building AI Boston... · 30:31 · 8,205 plays
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What happens when we introduce powerful new technology into a system that was already failing the people it was built to serve? That is the question at the heart of this conversation. In this episode of Building AI Boston, we sit down with Sheila Phicil, social change futurist, founder of Phicil-itate Change™, and author of the forthcoming book Remembering How to Care: Reimagining Healthcare in the Age of AI, to explore what it will truly take to redesign healthcare from the bottom up. Sheila's journey began in Port-au-Prince, Haiti, where a volunteer medical mission taught her that even the best-intentioned care can miss the mark entirely. That lesson, listen first and design second, has driven nearly two decades of work inside institutions like Dana-Farber Cancer Institute and Boston Medical Center, and now sits at the core of everything she builds. We dive into: Why the U.S. spends the most on healthcare yet ranks last in life expectancy among developed nations and what structural forces are really driving that gap How the SEEDS of Innovation™ Framework helps health systems get to the root of the problem instead of repeating the same costly mistakes The Listen Phirst™ platform: using voice AI to collect lived experience at scale and center the voices of the most vulnerable in innovation decisions Why digital health fails at a 98% rate and what that tells us about introducing AI into already strained systems The coming collision of rising unemployment, shrinking healthcare coverage, and burnt-out providers Data sovereignty and why your health data is an extension of your identity, belongs to you, and should be treated as the asset it is Why the people experiencing the most pain are the ones best positioned to drive the innovation Sheila also shares her open letter to the CEO of Anthropic on how current AI models risk undermining human sovereignty and makes the case that the most urgent question of our time is not how to build smarter AI, but who gets to decide what health, safety, and care actually mean. This i
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