How Brad's AI health coach helps him reach fitness goals

In today's AI landscape, there’s a lot of hype. But trust and credibility aren’t built on hype. They’re built on real-world proof. At Laguna, we've grown by 1,000% this year by focusing on what matters: solving real problems for care teams. Our latest case study shows a multi-state health plan cutting documentation time by 66%—giving care managers time back for what they do best: caring for members. Our Q3 update shares how we're earning trust through progressive adoption, proven results, and a commitment to the whole package—not just flashy features. Read the full update 👉 https://lnkd.in/eVwGyVyJ

The more I hear stories about AI, the more I am convinced, it is not here to take away the human touch. It is here to enable it. In this Full Circle episode, I spoke with Rachel Keller from Powervox about Abby, their AI receptionist who never misses a call, books appointments, and gives teams more time to focus on people, not paperwork. When tech takes care of the routine, humans finally get to do what they do best; care. If we really want to move towards a system that delivers #ValueBasedCare, outcomes rather than interventions, then we have to enable our staff to deliver just that -"CARE", not health administration. Full episode coming soon.

We made a mistake. ☔ We thought every hospital wanted the newest AI tool on the market. Turns out, most just want the ones that actually work. At Frontline Rural Health, we learned that it’s not about chasing trends. It’s about helping hospitals cut through the noise and find tools that save time, lower costs, and make life easier for their teams. Sometimes you learn the most from getting it wrong first.

I was dead wrong about what health departments want from AI. 𝗪𝗵𝗮𝘁 𝗜 𝘁𝗵𝗼𝘂𝗴𝗵𝘁 𝘁𝗵𝗲𝘆'𝗱 𝘄𝗮𝗻𝘁: Fancy algorithms and cutting-edge features. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗮𝗻𝘁: AI they can trust with their community's data. Huge difference. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 "𝘁𝗿𝘂𝘀𝘁𝘄𝗼𝗿𝘁𝗵𝘆 𝗔𝗜" 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗲𝗮𝗻𝘀: - Built on proven infrastructure (why 𝗣𝗛𝟯𝟲𝟬 𝗿𝘂𝗻𝘀 𝗼𝗻 𝗔𝗪𝗦) - Transparent about data handling and security - Designed by people who understand public health workflows - Supported by a team they can actually reach 𝗧𝗵𝗲 𝗵𝗼𝗻𝗲𝘀𝘁 𝘁𝗿𝘂𝘁𝗵: Most health departments have been burned by flashy tech promises before. They don't want the newest thing. They want the 𝗿𝗶𝗴𝗵𝘁 𝘁𝗵𝗶𝗻𝗴. We're building for the long term at 𝗙&𝗧 𝗟𝗮𝗯𝘀. Boring infrastructure that enables exciting outcomes. 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗶𝗻 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝗶𝘀 𝘂𝘀𝗲𝗹𝗲𝘀𝘀 if health departments can't trust it with patient data. What makes AI "trustworthy" in your book? -- Hi! I'm Jefferson the CEO of F&T Labs. I'm a former CIO of a local health department and I've taken that experience and funneled into the tools that we're building at F&T Labs. I envision a future where America's public health systems have the tools that they need to do their work. (I'm also a proud husband, dad, and bow-tie aficionado)

Ready to revolutionize your health conversations? Today, we’re diving into some groundbreaking research on a “wayfinding” AI agent that’s set to change the game for health interactions. Imagine an AI that not only understands your health queries but also guides you through them like a trusty GPS for your well-being. Curious how this Gemini-based innovation can transform your health dialogues? Check out the full insights in our latest blog post! Your health journey just got a major upgrade. Read more here: https://ift.tt/9idt1Mw.

💥💥💥 The Anatomy of a Personal Health Agent — by A. Ali Heydari, Ken Gu, Vidya Srinivas, Hong Yu, and colleagues from Microsoft Research, Google Health, and University of Washington. This work proposes a multi-agent architecture for personalized, everyday health support. The Personal Health Agent (PHA) integrates three sub-agents—data science, domain expert, and health coach—coordinated by an orchestrator that ensures consistency and contextual reasoning. Grounded in user research and evaluated over 10 benchmark tasks with more than 7,000 annotations and 1,100 hours of expert and user testing, PHA demonstrates how AI can move beyond static health apps toward adaptive, evidence-based, and behaviorally aware companions. It represents a major step toward truly human-centric health AI, bridging data, understanding, and action in one intelligent system. 👉 https://lnkd.in/dVAu846y #machinelearning

Personal Health Agents (PHA) will transform healthcare - They will bring human experts in the loop - And will be super personalized! Google Research Ali Heydari, PhD just published a paper on PHA which lays a strong foundation for this vision (link in comments) But the real question is when users will start feeling safe enough to trust AI with their personal health?

Imagine if every patient truly understood their health. With seca AI Health Insights, complex body composition metrics become clear, relatable, and actionable—helping patients stay engaged, motivated, and on track. Because healthcare should be easy to navigate. 🔗 Learn more now → https://hubs.li/Q03CbDsZ0

Our panelists Ben Isenhour, Chase Parsons, Joe Diver FACHDM and Matthew Butler moderated by Sue Schade have just concluded discussing Challenges and Considerations for Implementing AI Within the Enterprise. Main themes include: - How AI adoption and implementation differs between larger health systems and community hospitals - How can we best measure ROI of AI implementation? - Ambient listening has allowed Boston Children’s physicians to be able to have dinner with their families and has saved Signature Health a million dollars a year - Public callout for major academics and innovators to partner with more community hospitals! Thank you all for sharing your thoughts on AI and how it is being implemented in your work and health systems.

AI is no longer science fiction in health care, it’s already here. From diagnostic support to automated documentation, AI tools are being sold to hospitals at a dizzying pace. But here’s the problem: