Lexi Lewtan

New post about product-led-growth in healthcare - Why OpenEvidence's approach is interesting - Heuristics for how to think of other opportunities in direct-to-employee tools - A look at other roles and tools they might need (CRCs, home aides, med lab techs, pharmacy) - Some of the open questions that will dictate this space going forward full post in the comments

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Today Salubrum (Backed by Forum Ventures) is introducing the Patient Intent Score. Healthcare growth has relied for too long on rough proxies like demographics, keywords, and broad targeting. We built The Patient Intent Score to replace guesswork with signal, using privacy-safe healthcare and consumer datasets including claims-based insights, medical propensity models, social determinants of health, and verified consumer records. The output is a single score that helps healthcare brands and providers prioritize the patients most likely to convert, and deploy spend with greater precision and accountability. The best part is, leads with the given score can be pipelined directly into the CRMs of healthcare brands, allowing them to make a difference immediately. This product reflects our broader shift toward building the demand intelligence layer for healthcare and the bet we are making on consumerization of healthcare. We’ll be sharing more on how it works, and where it’s already being applied, in the weeks ahead. Learn more: https://www.salubrum.com/

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Things are about to get a lot harder for AI startups targeting health systems. At Epic’s UGM this week, we should expect an announcement about an Epic ambient scribe. Add this to the list of 100’s of AI projects they’ve already previewed. Picking our head up we are going to see this motion across many agentic automation use cases that touch the Epic core platform. Whenever Epic wants to move to augment their user experience with AI, they will have the built-in advantage of a compound company. These benefits include: 🛠️ Shared tools, context, and capabilities 🧩 Complete leverage over integrations 🪶 Common user experience driving adoption friction to zero 💰 Bundling the product portfolio to reduce per unit price But all is not lost for startups that want to sell to hospitals. They can move out from the Epic-adjacent wedge into financial and operational automations that Epic is less likely to build. Finding service-heavy processes and manual tasks that can be replaced with agentic automation. My belief is that startups will need to cede the space occupied by the clinical user to Epic; it is simply too close to the core customer value Epic provides, and inconsistent with their product and commercial strategy, to give up this space to any other software company in the long run. Further advancing Epic’s advantage are ready-to-go integration capabilities of state of the art models and AI clients from the leading foundation model providers. These startups will need to be agile and move quickly into adjacent spaces, accepting revenue churn as Epic comes for their initial wedge. The good news is that there are big opportunities across the many thousands of non-clinical employees in health systems. Just don’t call it a SaaS business, or fall into the trap of thinking that these tools will displace the EMR.

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🤖 Hinge Health’s IPO is a milestone moment — and a flashpoint in PT. Their AI-driven, digital-first model promises efficiency and scale, slashing clinician time. For some, it’s the future: smart tech enabling clinics to do more with less. But the questions are impossible to ignore: Will digital PT commoditize care? Can we keep the human connection that drives outcomes? What’s lost when AI replaces hands-on treatment? I don’t have all the answers. But I know this: the future of PT should be shaped by clinicians, not just algorithms. The real question isn’t if AI will transform PT. It’s how we make sure that transformation serves patients — not just shareholders.

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Winning in HealthTech isn't about big bets. It's about small promises. Kept. Relentlessly. Everyone loves the "moonshot" story. The big launch. The game-changing feature. The pilot that makes headlines. But in reality? That’s not what wins in healthcare. What wins is trust. And trust isn’t built in leaps. It’s built in increments. Showing up when you said you would Fixing the bug before they need to ask Answering the phone six months after go-live Reducing two clicks today, and another two tomorrow None of those moments look like innovation. But together? They are adoption. Because NHS Trusts don’t buy hype. Clinicians don’t buy vision. They buy proof that you’ll still be there - solving problems. So the real question isn’t: "What’s our big bet?" It’s: "What’s the next small promise we can keep?" That's the work that scales. Quietly. Consistently. Permanently.

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The top 1% of healthtech operators aren’t just looking for comp. They want mission, ownership, and data-backed traction. Candidates ask: “Is this platform clinically validated?” “Does the company understand FDA, CPT, and payer dynamics?” “Are there real outcomes or just a slick demo?” If your team can speak to these things early in the process, you’ll win top talent.

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The Critical Hiring Decision Founders Overlook Female and digital health founders often scramble to recruit engineering talent, convinced a technical co-founder is their top priority. But what if the real key to success is the product leader? Here’s what I’ve learned from 20+ years building new products - hiring engineers before clarifying your product vision can mean wasted time, costly rework, and features that miss the mark. A product leader acts as your “product sherpa”, leading customer discovery, validating concepts, and translating insights into an actionable roadmap. This isn’t just project management - it’s the difference between a scalable product and one that fizzles out. They help ensure you know: - Who your true customer is - The problem you’re solving (and why it matters) - Whether your solution excites users and is clinically / commercially viable Yes, engineering is essential - but only after you’ve pressure-tested your assumptions. The product leader sets engineers up for success, minimizes burned cash, and aligns your commercial and regulatory realities. My advice: Before you hire that tech co-founder, ask, “Do we know what to build, and for whom?” Building the right thing *must* come before building the thing right (or building it fast). I am an executive operator, management consultant, and investor in FemTech. I use my 20+ years of experience in the software industry - combined with my Ph.D. in Anthropology - to drive innovation and growth in platform, software, and software-enabled services healthtech companies, with a focus on the business of women's health. Are you grappling with product direction? I’d love to hear how you’re approaching team building and product leadership in digital health or FemTech. Let’s connect! #Founders #Innovation #ProductStrategy #TeamBuilding

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Hot take: true healthtech innovation isn’t just adding more data...it’s removing confusion. We can measure almost everything... heart rate, HRV, hydration, glucose, cortisol, sleep...yet somehow, people are more confused than ever. More data doesn’t mean better outcomes; it often just means more noise. When building Drip, a hydration monitoring company, I worked closely with athletes, coaches, and performance directors. They loved the promise of real-time biometric feedback: sweat composition, sodium loss, recovery patterns: but every conversation led back to the same question: “Okay, sodium’s dipping. Now what?” That question says everything. The real opportunity in biometrics isn’t collection. It’s translation. Athletes didn’t want to know their sodium dropped 8%. They wanted to know what to do next: adjust their electrolytes, their ideal prescriptive hydration formulae, fuel differently, pace recovery, or optimize focus mid-game. And that’s the same challenge facing healthcare today. Clinicians don’t need more dashboards. They need decision clarity: signals that guide action, not just report symptoms. The next evolution of biometrics won’t come from better sensors or faster algorithms. It’ll come from the platforms that can turn data into clear direction.

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