Jay Liew

We're trying a new thing: Company spotlights to help people better understand what various health IT vendors actually do 🔦 We’ll be cutting through the marketing speak to give short, to-the-point descriptions of a company’s product and/or service + some context on how they see their differentiators. Tag any companies in the comments you'd like to see us cover (and follow Elion if you want to stay in the loop!)

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Thinking about building your own custom software? If your company doesn’t sell software for a living, here’s why you shouldn’t: - Cost: You’ll pay for the “custom” part forever—and it’s never “done.” - Time: It’s a black hole for your resources and patience. - Security: Hackers, lawsuits, and ransom notes—oh my! - Availability: Affordable hosting = downtime at the worst moments. - Data Loss: Bugs can slowly delete your data, backups or not. - Maintenance: APIs break, tech evolves, and integrations become chaos. - Turnover: The dev who built it leaves. No one else understands it. - Tech Vanity: Your dev dreams up problems to solve to learn the latest coolest technology. If you do not handle Facebook's data volumes, your company doesn't need Hadoop. - ROI: Buyers and investors don’t care. They’ll rip it out and use Salesforce anyway. Moral of the story: Unless your business is software, don’t build it. Your future self will thank you. 

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7 RECOMMENDATIONS FOR HARDWARE STARTUPS It’s hard to understate the difficulty of the v1 launch of a consumer hardware company. I see a ton of new HW startups coming out. Here are some counterintuitive recommendations… 1) Target a small market for v1, not a large market. You need a small number of people to love your product and this will earn you the right to build a v2. You have to understand your v1 will not make you a successful company. Your v1 needs to prove that you deserve to exist in the first place. In the case of WHOOP, we started with pro athletes who were willing to put up with our v1’s short battery life and flaky Bluetooth connections because they so desperately needed Sleep and Recovery data. A broad consumer base would have rejected it. 2) Be great at no more than 1-3 things. If you try to be great at more than that, you will build a hardware that is mediocre at a wide range of things. You won’t think it’s going to be mediocre, but it will be. 3) You want a small launch. Not because you want small sales but because you want to have a steady ramp. It’s much better to do 100k of sales steadily over 12 months than in 2 months with a huge drop off. Keep in mind you need to manage your manufacturing partner and that there’s nothing better or more efficient than steady and predictable. 4) CAC is your enemy. Referrals are gold. The magic of referrals is that they signal high NPS and they help you grow steadily. Again everything that helps you have a steady HW manufacturing plan is good. Building a referral program for v1 launch is a good use of your time. 5) Be excellent at Software. Turns out being good at both Hardware and Software is a hard thing and something that your BIG COMPANY competitor is actually not great at (excluding Apple here). For some reason, there’s such a magnifying glass on the Hardware development, that Software is then overlooked by consumer startups. Don’t make that mistake. You can be great at both. 6) You should spend an unreasonable amount of time and money on ONE aspect of your Hardware. Could be the design or a technical breakthrough. But something needs to be SPECIAL. In our case, we designed a modular battery pack that allowed Whoop to be charged without taking it off your body. This made our development timeline much longer, but it allowed us to achieve something that no other wearable has achieved since: 24/7 health data. 7) Finally, have an unrealistic belief in yourself and your team. You are not supposed to succeed and the odds are stacked against you. Building hardware takes confidence and you’re up against big players who will rip you off. We went so far as to leave a message for our competitors on our circuit board (pictured below). In the case of Amazon and others, the prophecy came true. If you’re building consumer hardware, I wish you nothing but success. It will be super hard, but super rewarding. Good luck 👊🏼 #consumer #hardware #wearable #startup #whoop

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The Stacks. TLDR. (<2 min read) Sandboxes and bad questions We opened this month's Stacks by sharing our new AI integration, why Sandboxing is the right choice for our database and why this is the exact feature that you didn't ask for, but needed. Our second AI editorial shares the importance of asking good questions when doing research and how better search strategies won't completely save us. Fibro: Make a Decision Already Our fibromyalgia content gives you the confidence push you need to stop referring out these patients and making the freaking diagnosis yourself. Standards of care guidelines call for primary practitioners to just call it already. If you use the current guidelines, you're not likely to flag the wrong person. We promise. ED in men (and not the one you're thinking) Eating disorders are maybe more common in your patients with ovaries, but the consequences of missing it in the other 50% of your patients is just as serious. We share how to flag your patients with eating disorders regardless of gender, and why you need to be more careful looking at every patient that sits across from you. Thumbs down for B3 on cholesterol In our sneak peek content (typically for members' eyes only), we share the importance of clinically relevant outcomes for cholesterol care and how niacin doesn't fit the bill for cholesterol lowering. Hashimoto's and Ovarian Reserve A few important papers were published in the last 18 months creating even more of a concrete connection between HT and POI. If you're working with people TCC who have a positive TPO, you may want to read this ASAP. Want to read the whole thing? Click here: https://joom.ag/hStd

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There's now no shortage of options for open source model hosting providers. i.e., take your pick for Llama 3 (Groq, Together AI, Telnyx, etc.). Am I wrong in thinking the only real difference now between them is speed, cost and readability? If so, this space just became commoditized quickly and reminds me of the telco provider wars where its a race to the bottom on price. Which means the cost of running AI applications is approaching something close to zero... 🤯 #ai #cx

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Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

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🤯 Google Gemini 2.0 multimodal API ➡️ Game-Changer (Real Time RAG Clinical Assistants?) Implications for Healthcare👇 ⚡️ I would recommend checking out any number of videos demonstrating this tool. ⚕️ Implications for Healthcare > Building a real time RAG clinical copilot screen reading application that could provide real time decision support during chart review. > Listen to conversation real time and transcribe a visit between a patient and clinician > Provide real time decision support on prior authorization for what medications would be covered before selecting them > Would love to hear your use case 🤩 🔐Security and Compliance Issues > Screen scraping clauses within most EHR’s vendors, including Epic. > Is this technically screen scraping? > Would the API be classified as another user? > How would licensing work? > I see significant security and compliance concerns around this as well. > Does the API recognize that it’s looking at sensitive information and prevent it’s used in these scenarios? 🔑The World is Moving Fast > It’s getting harder to deny the potential usefulness of these agents and how they could accelerate knowledge acquisition and task completion. > Much of the implementation in healthcare will revolve around the mundane but important legal and security issues and enabling these tools. Would love to hear your thoughts. 🤩 #ai #medicalai #healthcareai #aiagents #llm #agenticai

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Emmett Shear called AI chatbots "highly dissociative agreeable neurotics." Never heard it put quite like that before. But he's right. Current AI tries so hard to please that it loses itself. Like a person with no backbone, agreeing with everything you say. Shear saw this problem and instead of just tweeting about it, he built Stem AI to solve it. That's what separates great founders from commentators. His career path is telling: - Built Twitch - Ran OpenAI during its darkest days - Now building tools for AI alignment Each step gave him deeper insights into the problems he's trying to solve now. Reminds me of why I back founders who've lived through the problems they're solving. They see angles others miss. --- Check out my Newsletter - TheFoundersCorner via the Link on my profile page or the Link at the top of the page ☝️ Jam Packed with Actionable Insights Every Week 💙

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“Over one million new articles are added to PubMed annually—about two per minute. No physician can keep up.” This highlights a challenge that PCPs face daily, managing countless chronic and acute conditions while adapting to ever-evolving clinical guidelines. We can’t expect our clinicians to act like machines. At RhythmX AI (https://rhythmx.ai), we address this by revolutionizing primary care with our generative AI-native precision care platform, developed collaboratively by our physicians, specialists, and AI teams. Our platform integrates advanced AI and longitudinal data to deliver patient-specific, evidence-based recommendations directly within clinical workflows. These hyper-personalized insights help physicians make informed decisions at the point of care, bridging the gap between research and practice. Trust is at our core. Our physicians rigorously validate every recommendation to ensure clinical-grade reliability, empowering clinicians to confidently rely on the insights we provide. #PioneerPrimaryCare #Hyper-PersonalizedCare #PCPBurnOut #HealthAI #ResponsibleAI

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Crazy to think how fast interfaces will evolve: Screens → Voice → Brainwaves https://lnkd.in/gd_V9YqG

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NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

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Though benchmarks for medical AI are still imperfect, there's no question that AI will continue to march towards diagnostic excellence. These trends will place greater emphasis on physician-scientists – those who advance medical knowledge via closed loop clinic/lab interfaces, which can then feed back into large-scale AI systems that serve entire populations. This was all part of the OG vision for the learning health system. source: https://lnkd.in/eh2RM72s

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John Snow Labs #Healthcare #NLP & #LLM 5.3 is now out! This release completely overhauls entity resolution - mapping medical entities in text to standard medical ontologies, in context - greatly improving accuracy. Full release notes: https://lnkd.in/gsiGF26i Install software: https://lnkd.in/gsTqkWKT #ai #generativeai #healthcareai #healthai #datascience #ontologies #medicalcoding

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Vishal Sachdev highlights the strategic integration of open source and proprietary tech in architecting tech stacks, developer ecosystems and resulting business models. The world class companies effectively balance value commoditization in open source and value capture in proprietary tech. This is the strategic challenge for companies operating in #blockchain and #AI. I believe those who operate at the intersection of blockchain *and* AI stand to win this strategic battle...

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🎬 Matt Damon and 🏈 Colin Kaepernick have invested in it, and so has 💰Andreessen Horowitz... Function Health's executive team has "engineered a first-of-its-kind approach that focuses on prevention and empowerment"—very much in line with what we aimed to achieve with Carebook when we started it over six years ago. Could they be the ones to fulfill their lead investors' prophecy (link at the bottom) and Dr. Eric Topol, MD's premonitions in his books "The Patient Will See You Now" and "Deep Medicine"? They have just secured a $50 million grant. Let's hope this is not another Theranos and that we are truly witnessing the rise of the next generation of direct-to-consumer health companies. These companies will be key players in the "transition from reactive to proactive" health care that the system—and we—desperately need. What do you think? Links: 📝 Investment round press release: https://lnkd.in/eUG87qkf 🔗 Andreessen Horowitz's prophecy: https://lnkd.in/eZATCf4f

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I’ve spoken to 50+ CMIOs/CIOs over the past year. Here are 5 thoughts about AI Scribes I'm hearing that many people are NOT talking about👇 1️⃣ Distribution matters more than Product. Most are just buying Nuance because it’s easier to buy. It’s a known entity. It’s an add-on to the Microsoft contract which means less internal hoops. Key question for who wins long-term: will the startups get distribution faster than the incumbent gets innovation? Will the EHRs eventually just release their own native AI scribes? (my belief is yes… which Oracle/Cerner has already done). 2️⃣ There is a clear “phenotype” for the clinicians who actually use AI Scribes: it’s clinicians who don’t care to have control over how their notes look and sound. But for those who really care about controlling the output? Those folks will keep writing their own notes. 3️⃣ The impact of AI scribes on medical education and trainees is very unclear. Some believe trainees should get exposure to AI scribes early on. Others worry that trainees who don’t learn to write notes are going to miss a critical skill that helps clinicians think. And what happens if there’s a cybersecurity incident and the EHR goes down? Will future clinicians not know how to document manually? 4️⃣ ROI is still hard to truly measure. Few see any improvement in “productivity” (i.e. more patients seen). The real value is in improving clinician well-being. However it’s hard to prove AI scribes are lowering staff churn since that is a lagging indicator many steps away and affected by many other factors. 5️⃣ The per clinician pricing model used by most vendors breaks down at scale. If I have clinicians who would only use the AI scribe for 30% of their patient encounters, am I going to buy a license for them? Probably not. What other less talked about learnings or insights are you hearing behind the scenes?

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🧩 One thing we've noticed with #VentureIQ technology is how well it can distill and communicate complex technical material 🧩 Whether it's a #deeptech venture, a novel #AI application, or even a patent submission, we can quickly distill the business and strategic implications for investors...and sometimes even the founders/PIs themselves! 🤯 Based on our work so far, the tech can handle a wide array of research areas and technological sectors -- anything from blockchain algorithms to vaccine development. It handles the nuance of the field, the specific terminology, and extracts the key concepts which matter for venture-building. 🤓 To demonstrate, I took a patent I helped write with Eric Laber on reinforcement learning. I ran it through #Ventureiq to build a commercial assessment (key differentiators, target markets). Then for fun I had #NotebookLM make a podcast out of it. I've uploaded the result it here (DM me if you'd like the actual report): https://lnkd.in/eiTejsny This is more than just automation or streamlining operations - it's about unlocking clarity and insight when it was previously unattainable, and the ability to do it over and over again no matter the sector or vertical. Interested in more info? DM me so we can chat.

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Get better at discovery with these posts: • Alex Newmann: I made a mistake during discovery that cost me millions in commission → https://buff.ly/3V5Jgdv • Mike Gallardo: You get 30 mins for discovery. → https://buff.ly/3V8t0sj • Monica Stewart: You should be doing this for most of your calls but you probably aren’t → https://buff.ly/3V7LWY4 • Pascal Hippmann 🧠 🐺: How to uncover urgency in your sales discovery → https://buff.ly/4bqqNhB • Adam Weekes: Some tips I do ahead of a discovery call and during → https://buff.ly/4bpJB0o • 🚀 📞 Sam Gillingham: Ok VS Great discovery calls → https://buff.ly/3V3BHUB • Dominic Blank: Very few salespeople do pain discovery → https://buff.ly/4bHNEVw • Martin Roth: If I could only focus on one part of the sales process to close deals faster → https://buff.ly/3V8dHzG • Amanda Long ⚡️: Your prospects don't care about your product → https://buff.ly/3V7T4Us • Laura Nelson: Easiest way to kick off a discovery call → https://buff.ly/3UDtl66 • Charles Muhlbauer: I closed a $65,000 deal by respectfully challenging my prospect → https://buff.ly/3K9qs6M • Phillip Kousz: Most salespeople ask questions. The great ones ask the right ones → https://buff.ly/3QRFS3x • 🎺 Rory Sadler: Discovery fatigue is on the rise → https://buff.ly/4bruHGO • Sandro Covi: Common Discovery Call Objections  → https://buff.ly/3wEpQTH • Gavin Kowalski: You need to ask uncomfortable questions → https://buff.ly/4bHNFc2 • Brian LaManna: After you finish discovery, ask: → https://buff.ly/4bo3JzL • Salman Mohiuddin: Took me 5 years to start doing this on my discovery calls → https://buff.ly/3QRFSAz • Tanveer Mostafa: If your discovery calls are starting to sound like FBI interrogations, try this → https://buff.ly/4bHNFsy • Kyle Asay: I talk a lot about bringing a point of view to discovery meetings. → https://buff.ly/3QRFSR5 • Nate Nasralla: Here's the 3-part discovery roadmap I use. → https://buff.ly/3RgaGv5 • Chris Orlob: 95% of the discovery questions salespeople ask piss off buyers → https://buff.ly/3ypvSYO • Ritchie Munro: Don’t talk about yourself on discovery calls!!! → https://buff.ly/3Ke7vje • Marcus A. Chan: Stop making this COMMON mistake on your discovery calls → https://buff.ly/3V8WXIs • Sean Gentry: Discovery calls are like a first date. → https://buff.ly/4brzsQE • Alper Yurder 🤝 interviews Patrick Trümpi: Be better at discovery with questions → https://buff.ly/3V4twr4 • Rich McDaniel jr. After 3 discovery calls over the past week - Something stood out to me → https://buff.ly/4bHNFJ4 • Mor Assouline: 4 things that transformed our disco that you can copy → https://buff.ly/3Kb7Svg • Matus Kucera: Without preparation, you will ask awkward questions → https://buff.ly/3KtXajH • Richard Smith: My first discovery call yesterday in some time. → https://buff.ly/4bHNFZA 📭 Join 2,000 sellers reading my daily newsletter: www.harishalkic.com

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AI is going to reduce the cost of code generation dramatically. Left unchecked, this will cause an exponential increase in cost of observability and root cause analysis. We believe that AI will come to the rescue, and Agent SREs will be a $100B category. The wrinkle is that there are already 50+ start-ups vying for the Agent SRE crown. If you have ideas on what will define the category winner, do ping me and/or Jaya Gupta https://lnkd.in/gyAnWarK

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