Matthew Weinberg’s Post

Excited to share that today we're launching Hello Patient out of stealth with our $6.3M seed round led by 8VC, Bling Capital, and Max Ventures! For the last 7 months, we've been heads down building complex infrastructure and tooling to support real time AI agents that we deploy on behalf of healthcare practices. Our agents can call and text patients just like practice staff do, taking on full workflows like scheduling, reminders, and rescheduling. Having run consumer and growth product teams at Carbon Health for 3.5 years, we're building what I wished we had back then. No matter how many self-service tools we built, patients still called and texted in. I looked at almost every contact platform on the market, and none actually resolved calls or required tons of work from us to do so. We're live today with customers and already seeing the impact of automating all this patient-facing communication work. Our agents have incredibly low latency and sound remarkably human. They handle complex edge cases and actually get stuff done vs just routing calls around. Full article here: https://lnkd.in/ddSAHc6z

🤖What I learnt meeting with >30 healthtech start-ups this year. A common theme is that ~ half of them pitched interesting ideas that unfortunately would not be a product I'd want to use as a clinician or would fit in meaningfully into a clinical work flow... 😐There was also a recurring theme of overly ambitious #LLM use cases - It was really painful hearing a number of companies (that raised a modest amount £££) tell me how they'd like to revolutionise healthcare by creating a model to diagnose any undifferentiated patient, spanning over all specialties. It quickly became painfully clear the person had no understanding of EHRs, no understanding of the scientific literature, and no medical knowledge. Can we just perfect #LLMs for administrative tasks before jumping into clinical decision making? 👨🏻⚕️It was also interesting to see majority had no clinicians in the founding team, and even when they did, there was a clear gap between clinical and technical expertise so the two sides were not communicating and sharing ideas effectively. It's a message repeated over and over again by y-combinator and other VCs alike - 1. Build a product that users want 2. For healthcare I think tech needs to be developed use case by use case. Instead of jumping on an ambitious undifferentiated model for all patients. (similar to what Dr. Zhong Wei Khor recently alluded to) 3. The core team should consist of a subject matter specialist to direct the development in a meaningful way, but I'd go as far as saying that the clinician needs to have at least an intermediate level of understanding of data systems, AI models and limitations. (this is something both Keith Grimes and I have explored, and will be an incoming episode on the Dev and doc podcast 👀) 4. My personal opinion, but we can't just keep building healthcare products based on a closed source proprietary model, to me this is not future proofing our technology, and puts the power of people's health into the hands of one big for-profit conglomerate. ----------- Hello 👋 we run a Substack and Podcast, Dev and Doc: AI for healthcare. This includes latest news, education, and deep dives in AI for healthcare! 👨🏻⚕️🤖 Substack- https://lnkd.in/e_XV4Pbz YT - https://lnkd.in/ey3mxsuN Spotify - https://lnkd.in/ecFRTN9R

I couldn’t be prouder of our team and the transformative impact this will have on the care we provide. Starting today, 4,000 clinicians across all specialties and care settings will have access to Abridge’s enterprise-grade AI platform for clinical documentation. This innovation is not just a technological advancement; it’s a significant leap forward in how we provide care. Corewell Health is proud to be among the first to implement Abridge’s generative AI technology so our patients can get the best care possible from clinicians who are more focused than ever on what matters most: connecting with and caring for patients.   Here’s what this means for our patients and communities:   ✅ Clinicians will now spend less time on after-hours documentation, allowing them to give undivided attention to patients. ✅ 90% of our clinicians reported a significant increase in patient interaction. ✅ We expect an 85% increase in clinician satisfaction at work.   Together, we are making healthcare better for everyone. Learn more about this incredible journey in the Becker's Healthcare article: https://lnkd.in/ey_CxZ56   #HealthcareInnovation #DigitalTransformation #AIinHealthcare #CorewellHealth

🤖What I learnt meeting with >30 healthtech start-ups this year. A common theme is that ~ half of them pitched interesting ideas that unfortunately would not be a product I'd want to use as a clinician or would fit in meaningfully into a clinical work flow... 😐There was also a recurring theme of overly ambitious #LLM use cases - It was really painful hearing a number of companies (that raised a modest amount £££) tell me how they'd like to revolutionise healthcare by creating a model to diagnose any undifferentiated patient, spanning over all specialties. It quickly became painfully clear the person had no understanding of EHRs, no understanding of the scientific literature, and no medical knowledge. Can we just perfect #LLMs for administrative tasks before jumping into clinical decision making? 👨🏻⚕️It was also interesting to see majority had no clinicians in the founding team, and even when they did, there was a clear gap between clinical and technical expertise so the two sides were not communicating and sharing ideas effectively. It's a message repeated over and over again by y-combinator and other VCs alike - 1. Build a product that users want 2. For healthcare I think tech needs to be developed use case by use case. Instead of jumping on an ambitious undifferentiated model for all patients. (similar to what Dr. Zhong Wei Khor recently alluded to) 3. The core team should consist of a subject matter specialist to direct the development in a meaningful way, but I'd go as far as saying that the clinician needs to have at least an intermediate level of understanding of data systems, AI models and limitations. (this is something both Keith Grimes and I have explored, and will be an incoming episode on the Dev and doc podcast 👀) 4. My personal opinion, but we can't just keep building healthcare products based on a closed source proprietary model, to me this is not future proofing our technology, and puts the power of people's health into the hands of one big for-profit conglomerate. ----------- Hello 👋 we run a Substack and Podcast, Dev and Doc: AI for healthcare. This includes latest news, education, and deep dives in AI for healthcare! 👨🏻⚕️🤖 Substack- https://lnkd.in/e_XV4Pbz YT - https://lnkd.in/ey3mxsuN Spotify - https://lnkd.in/ecFRTN9R

🤖What I learnt meeting with >30 healthtech start-ups this year. A common theme is that ~ half of them pitched interesting ideas that unfortunately would not be a product I'd want to use as a clinician or would fit in meaningfully into a clinical work flow... 😐There was also a recurring theme of overly ambitious #LLM use cases - It was really painful hearing a number of companies (that raised a modest amount £££) tell me how they'd like to revolutionise healthcare by creating a model to diagnose any undifferentiated patient, spanning over all specialties. It quickly became painfully clear the person had no understanding of EHRs, no understanding of the scientific literature, and no medical knowledge. Can we just perfect #LLMs for administrative tasks before jumping into clinical decision making? 👨🏻⚕️It was also interesting to see majority had no clinicians in the founding team, and even when they did, there was a clear gap between clinical and technical expertise so the two sides were not communicating and sharing ideas effectively. It's a message repeated over and over again by y-combinator and other VCs alike - 1. Build a product that users want 2. For healthcare I think tech needs to be developed use case by use case. Instead of jumping on an ambitious undifferentiated model for all patients. (similar to what Dr. Zhong Wei Khor recently alluded to) 3. The core team should consist of a subject matter specialist to direct the development in a meaningful way, but I'd go as far as saying that the clinician needs to have at least an intermediate level of understanding of data systems, AI models and limitations. (this is something both Keith Grimes and I have explored, and will be an incoming episode on the Dev and doc podcast 👀) 4. My personal opinion, but we can't just keep building healthcare products based on a closed source proprietary model, to me this is not future proofing our technology, and puts the power of people's health into the hands of one big for-profit conglomerate. ----------- Hello 👋 we run a Substack and Podcast, Dev and Doc: AI for healthcare. This includes latest news, education, and deep dives in AI for healthcare! 👨🏻⚕️🤖 Substack- https://lnkd.in/e_XV4Pbz YT - https://lnkd.in/ey3mxsuN Spotify - https://lnkd.in/ecFRTN9R

🤖What I learnt meeting with >30 healthtech start-ups this year. A common theme is that ~ half of them pitched interesting ideas that unfortunately would not be a product I'd want to use as a clinician or would fit in meaningfully into a clinical work flow... 😐There was also a recurring theme of overly ambitious #LLM use cases - It was really painful hearing a number of companies (that raised a modest amount £££) tell me how they'd like to revolutionise healthcare by creating a model to diagnose any undifferentiated patient, spanning over all specialties. It quickly became painfully clear the person had no understanding of EHRs, no understanding of the scientific literature, and no medical knowledge. Can we just perfect #LLMs for administrative tasks before jumping into clinical decision making? 👨🏻⚕️It was also interesting to see majority had no clinicians in the founding team, and even when they did, there was a clear gap between clinical and technical expertise so the two sides were not communicating and sharing ideas effectively. It's a message repeated over and over again by y-combinator and other VCs alike - 1. Build a product that users want 2. For healthcare I think tech needs to be developed use case by use case. Instead of jumping on an ambitious undifferentiated model for all patients. (similar to what Dr. Zhong Wei Khor recently alluded to) 3. The core team should consist of a subject matter specialist to direct the development in a meaningful way, but I'd go as far as saying that the clinician needs to have at least an intermediate level of understanding of data systems, AI models and limitations. (this is something both Keith Grimes and I have explored, and will be an incoming episode on the Dev and doc podcast 👀) 4. My personal opinion, but we can't just keep building healthcare products based on a closed source proprietary model, to me this is not future proofing our technology, and puts the power of people's health into the hands of one big for-profit conglomerate. ----------- Hello 👋 we run a Substack and Podcast, Dev and Doc: AI for healthcare. This includes latest news, education, and deep dives in AI for healthcare! 👨🏻⚕️🤖 Substack- https://lnkd.in/e_XV4Pbz YT - https://lnkd.in/ey3mxsuN Spotify - https://lnkd.in/ecFRTN9R