Dr. Owen Solad
@ysolad
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CVP of Clinical AI at HCA.
Nashville, TN
Joined November 2012
Was a ton of fun joining @ysolad on the ReAligned Healthcare Podcast! Lots of spicy 🌶️ Health Tech takes in this one. We chat about: → Why EHR vendors should go “all-in” on AI Agents, and how Agents will make EHRs stickier (and make it harder for startups to disrupt them) →
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10/10 Want a deeper dive? Check out the full article where we explore each of these points in more detail—and share how we can build AI solutions that earn clinicians’ trust instead of eroding it.
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9/10 If implemented thoughtfully, LLMs can move from novelty to genuine clinical asset. But we have to address these limitations head-on to ensure AI truly lightens the load.
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8/10 - Human-AI Collaboration: AI should empower, not replace, clinicians by streamlining verification. - Continuous Oversight: Monitoring, updates, and ongoing training are crucial for safe, effective adoption.
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7/10 Moving Forward - Tailored AI: Healthcare-specific designs that reduce “prompt engineering” and improve accuracy. - Transparent Validation: Clinicians need to understand how AI arrives at its conclusions.
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6/10 Workflow Hurdles LLMs excel in flexible, open-ended tasks, but healthcare requires precision, consistency, and structured data. This mismatch can lead to patchwork solutions and unpredictable performance.
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5/10 Burnout Concerns AI is often touted as a remedy for burnout. Yet if it’s poorly integrated or frequently incorrect, clinicians end up verifying and correcting even more, adding mental strain instead of relieving it.
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4/10 Trust Erosion Even a single AI-driven mistake—like the wrong dosage—can compromise patient safety. Errors that go unnoticed fracture clinicians’ trust and force them to re-verify every recommendation, negating AI’s efficiency.
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3/10 Verification Overload LLMs might produce coherent summaries, but “coherent” doesn’t always mean correct. Manually double-checking AI-generated notes or recommendations becomes an extra task on an already packed schedule.
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2/10 But early adoption reveals a more complicated reality: verifying AI outputs, dealing with errors, and struggling with workflow integration can actually increase clinicians’ cognitive load. Here are four key considerations:
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1/10 Is AI Really Easing Clinician Workloads—or Adding More? Healthcare is rapidly embracing AI and Large Language Models (LLMs), hoping to reduce clinician workload.
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The Hidden Costs of Integrating AI into Clinical Workflows
linkedin.com
As healthcare rushes to embrace AI and Large Language Models (LLMs), we're discovering an uncomfortable truth: tools promised to reduce clinician workload are often creating unexpected new burdens....
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Today marks my #TabClosingDay on #iPhone! I've officially hit the 500 tab max in #Safari! Time to break up with the past and close pages I've saved for later but never returned to. Here's to a fresh start without the clutter! #DigitalDeclutter #TechLife
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Our co-founder & CEO Mike McSherry joins @ysolad for an insightful conversation about #digitalhealth and it's role in modernizing healthcare. Check out the full episode: https://t.co/u51iPuqry2
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Full Episode 1 . ReAligned Healthcare - Shaping the Future of AI in Healthcare: A Deep Dive with Dr. Brian Anderson YouTube: https://t.co/hXvZIvnZWM Apple Podcast: https://t.co/VhQfLQnxeY Spotify:
open.spotify.com
Podcast · Dr Yauheni Solad · Healthcare is changing fast, and ReAligned Healthcare is here to navigate that journey with you. We dive into real stories and practical insights on how to align health...
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(2/2) Key discussion points: -CHAI aims to create best practice frameworks for healthcare AI. -Diverse representation is crucial in AI validation and development. -Patients should be involved in AI model design from the start -AI tools must be evaluated for real-world
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(1/2) Our very first episode of #ReAlignedHealthcare is now live! In this episode we dive into the future of #healthcare #AI with Dr Brian Anderson @bandersmd , CEO of the Coalition for Healthcare AI ( #CHAI). From #clinical decision support to patient-centered AI models,
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OpenAI’s new 1o preview model dropped, and it feels like we’re entering a new era of automated agents. It’s more thorough in its analysis, harder to trick, and includes chain-of-thought reasoning out of the box. Not quite GPT-5 or AGI, but do we really need AGI? Pros: •
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