"First-of-its-kind study on ambient AI in healthcare published in JAMA"

THIS IS GOLD. Only by working with our partners and doing the rigorous work of science can we safely and responsibly deploy & scale ambient AI. Don't like reading studies? Here's the TLDR: Abridge significantly reduces burnout, reduces cognitive load, and increases undivided attention given to patients. 👀 the article in Matt's comments

A new peer-reviewed multicenter study in JAMA marks a major milestone for ambient AI in healthcare. The research validates the real impact clinicians experience daily—reduced documentation burdens, restored empathy, and renewed focus on patients. This breakthrough highlights how Abridge and its health system partners are responsibly advancing technology that transforms care for both clinicians and patients.

Loved reading this new JAMA article on how ambient AI technology like Abridge is helping reduce administrative strain without compromising clinical accuracy. As someone deeply focused on clinician well-being and innovation in healthcare, I truly believe this is the kind of change we need to decrease burnout. Excited to see how thoughtful AI implementation can help improve the healthcare system. Definitely worth a read! #wellbeing #nursewellbeing #aiinhealtcare

🚀 𝗧𝗵𝗼𝘂𝘀𝗮𝗻𝗱𝘀 𝗼𝗳 𝗵𝗼𝘂𝗿𝘀 𝗼𝗳 𝘄𝗼𝗿𝗸 𝗯𝘆 𝗰𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 𝗮𝗻𝗱 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 → 𝗼𝗻𝗲 𝗔𝗜 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺, 𝗯𝗮𝗰𝗸𝗲𝗱 𝗯𝘆 𝘀𝗰𝗶𝗲𝗻𝗰𝗲, 𝘁𝗵𝗮𝘁 𝘀𝗰𝗮𝗹𝗲𝘀 𝗳𝗼𝗿 𝟮𝟬𝟬+ 𝗵𝗲𝗮𝗹𝘁𝗵 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. Since 2018, Abridge has been developing an AI platform that seamlessly handles healthcare workflows so clinicians can focus on what’s most important: caring for patients. But over the past few months, the pace of change has accelerated: ✅ Unprecedented adoption and scale of AI in clinical workflows ✅ Ending hallucinations in clinical documentation  ✅ Rapid implementation of AI at pediatric and specialty health systems This is just the very beginning of a decades-long journey to invent a more human and intelligent healthcare system, with clinicians and scientists leading the way. Read more about the latest advances: https://lnkd.in/eQYcu__T

AI is advancing faster than ever—and under-resourced health care organizations are struggling to keep pace. That’s why Daniel Kortsch and I are launching the Safety Net AI Alliance. It’s not a financial venture for us—it’s about impact. Our goal: help safety net hospitals and clinics implement AI in ways that improve patient safety, reduce staff burnout, and drive equity. By pooling resources, sharing talent, and lowering costs, we can accelerate safe and effective AI adoption where it’s needed most. It's still in concept phase - Thoughts? https://lnkd.in/gsGvSYVv

A panel moderated by Kelly H. Zou, Head, Global Medical Analytics and Real-World Evidence, Viatris; Founder, AI4Purpose Contributed an article "Health Tech and Artificial Intelligence in Life Sciences" "Transforming academic research into real-world health tech demands more than innovation— it requires interdisciplinary collaboration, regulatory savvy and the entrepreneurial grit to bridge science with scalable impact" says A panel moderated by Kelly H. Zou. Read More : https://lnkd.in/gVKevg54 #DigitalHealth #StartupJourney #DataPrivacy #PatientCare #PredictiveAnalytics #RegulatoryCompliance #HealthOutcomes #EntrepreneurialMindset #PatientAdvocacy #BusinessStrategy #HealthcareChallenges #CareerOpportunities

AI is changing healthcare at breakneck speed. But here's the question we posed to 30 national healthcare leaders: How can we ensure that AI improves diagnosis? This past September, UCSF CODEX brought together diverse voices that rarely sit at the same table—from hospital C-suite executives to patient advocates, AI developers to medical malpractice insurers, and from payers to academic researchers. The goal was ambitious yet focused: to develop and refine practical metrics that capture AI technologies’ impact on diagnostic excellence and to chart a path for implementing these measures in real-world practice. The real work is just beginning. The CODEX team is now synthesizing these insights into an actionable measurement framework alongside continued feedback from the meeting participants. Participating health systems will then pilot these metrics at their institutions, providing crucial data to refine our approach. Watch the 2-minute video to hear directly from participants about why this work matters and what comes next. 👇 Full story about the meeting, key takeaways, and details on what we're building: https://lnkd.in/gF8JU6NQ Sumant Ranji Ben Rosner, MD, PhD Anjana Sharma Julia Adler-Milstein Sara Murray, MD, MAS Andrew Auerbach Aaron Tabacco, PhD Jason Cunningham Andrea Downing Ethan Goh, MD Zachary Lipton Craig Umscheid M.D., M.S. Susan Brown Sheridan Kevin Wang, M.D. Kevin Watson, MD, FAAP

"AI can diagnose just as well as doctors - maybe even better - so we have to rethink what it really means to be a doctor." "I'm ideologically opposed to AI. It's a planet-destroying plagiarism machine." "Having an AI scribe in clinic has changed my life. I actually feel on top of my work for once." "I hate ChatGPT - everyone's relying on it, but you can't trust anything it says." These are a few things I've heard from colleagues about artificial intelligence. The AI era in medicine is here - so wherever you fall on this spectrum of opinions (and everyone has an opinion!), it's clear that AI is already changing how care is delivered. Like any technology, AI isn't "good" or "bad"; the challenge is to ensure that AI actually improves care. AI has tremendous potential for improving diagnosis. As we heard from David Larson, AI is already widely used in radiology and there is growing evidence of its effectiveness. But we don't yet know if AI can help improve outcomes that matter to patients. Can AI prevent patients from undergoing never-ending diagnostic odysseys? Will AI help reduce overutilization of CT scans and MRI's - or cause clinicians to order more and more tests of limited value? Will patients trust an AI-enhanced system of care, or will automation make them feel even more disregarded? Thanks to our amazing participants, we tackled all of these questions at the Action Incubator. We're committed to leveraging their expertise to define the measures that will be used to answer these questions, ensuring that AI reaches its potential to help patients. Stay tuned for more information as this process unfolds; we'd love to hear everyone's thoughts!

Excited to share that our manuscript, “Use of an AI Scribe and Electronic Health Record Efficiency,” co-authored with several of my former colleagues at the University of Chicago Medicine, was just published in JAMA Network Open. In this propensity-matched cohort analysis, we found that clinicians using an AI scribe spent ~8.5% less total time in the EHR and ~16.5% less time in notes per appointment compared to covariate-balanced controls, with the largest gains among female clinicians, PCPs, and medical subspecialists. Grateful to my amazing mentors at UoC for helping to get this across the finish line. https://lnkd.in/gEzna6BA