Paul Cushman

According to the latest report from Rock Health, digital health funding in Q1 2024 totaled $2.7B across 133 deals, with an average deal size of $20.6M. It doesn’t break out device-related investments, but most medtech founders I meet these days are being forced to chase tiny rounds made up of endless little checks, consuming valuable energy and slowing their progress. The benign neglect of this next generation of diagnostic and therapeutic devices is counterproductive and surprising. Given the crowded virtual care market, for example, I’d expect more such companies would be exploring medtech partnerships, investments or acquisitions that'd enable them to back integrate into delivering sharper, and more differentiated, care and treatment. This is exactly what Maverix Medical is doing from the other direction: Reshaping the entire spectrum of lung cancer care by integrating more advanced + effective approaches to screening and diagnosis, treatment and therapeutics, and even palliative care…And putting it all to work through an innovative provider network. One notable investor exception is Y Combinator partner Surbhi Sarna who’s been backing great teams like Selera Medical (YC W24) doing foundational work on ambitious medical devices. Without more support from investors like her, we’re going to increasingly find ourselves stuck with the incremental portfolio improvements from the incumbents, and that’s just not nearly enough.

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One key reason why healthcare technologies like AI, telemedicine, and software haven't structurally lowered costs or broadened access for the entire system after decades of experimentation (excluding pure financial engineering of payer contracts) is the limited reimagining of care delivery itself, with asynchronous telemedicine as a notable exception. In the same way that digitizing physical paper failed to exploit the capabilities of a new computing medium for word processing, hosting physician-patient conversations over video does not transform the actual delivery of care. In telemedicine, many of the inputs (e.g., clinician time) and therefore cost structures of in-person care persist, which is why we often see fee-for-service telemedicine companies struggle to achieve economies of scale (i.e., growing revenues non-linearly while growing costs linearly). Until we deconstruct the components of a medical appointment – from intake and triage, to chart review, history gathering, physical examination, diagnosis, treatment, and follow-up – we will continue to be underwhelmed by the disconnect between the transformative potential of these technologies and real-world patient/system impact. I’m optimistic that the emergence of reliable and performant medical AI agents will urge us to perform this deconstruction and redesign the fundamental building blocks of a “medical appointment.”

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As the discourse shifts from models to compound AI systems / agents, we need better AI benchmarks to evaluate multi-modal and multi-step task performance, especially in healthcare and life sciences. When we wrote the first paper demonstrating ChatGPT's performance on the USMLE, we chose the US Medical Licensing Exam as a benchmark for accessibility, speed, and ease. This benchmark was never intended to represent AI model performance on real-world clinical tasks. Today, I still see so many research teams and startups using benchmarks (like the USMLE) that are ill-fitted for assessing the true clinical or scientific performance and utility of the models they are developing for real-world contexts. Benchmark development may be seen as a "less sexy" area of research, but it is of paramount importance. Years after the rise of the transformer, we still lack adequate benchmarks for so many single-step tasks in biomedicine. With compound AI systems (i.e., architectures that integrate multiple AI models to perform complex tasks) emerging, we need new benchmarks for agentic behaviors. I'd argue that developing an agent with novel capabilities without at least proposing a companion benchmark (if an industry standard does not yet exist) may hinder the adoption of said agent, especially for high-stakes workflows. Designing more benchmarks that capture/simulate real-world clinical and scientific workflows will help us mitigate the major discrepancies observed between in silico and in vivo performance and better support safe + effective deployment of AI in biomedicine. There are already brilliant people focused here, and we need more. DMs are open if you're researching or working in this area of multi-step/multi-modal benchmarking in healthcare and life sciences! #healthcare #ai #artificialintelligence #generativeai

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LSI, BioscienceLA, #LAMedtechweek, and HLTH's #VIVE2024 event resulted in the latest GTM Pulse Check offering which has successfully taken off! As a result, I am continuing on last week's series of go-to-market tactics. Crafting a robust marketing plan is essential for medical device companies to thrive in today's world. By leveraging these outlined sections, organizations can optimize their marketing strategies and drive meaningful impact.This post takes a step back to outline the sections of a marketing plan. Read on 👇🏼 Looking for support in creating your marketing plan?  Let’s chat! Book here 👉🏼 https://lnkd.in/g92YA9WK _____ P.S. Enjoying my content? ⚕️Follow me for more tips on medical device commercialization and repost to share with your network. #medtech #gotomarket #medicaldevices #healthcareinnovation

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As I look at startups and incumbents using LLMs / Generative AI in healthcare for provider workflows, I am trying to separate fact from hope, and current reality vs. the likely progress in the next 1 - 2 years. Wondering if there is anything in the "it's real now" bucket or in the "it seems very likely in the near future" one. 3 basic questions I am hoping to be educated on - if you have a strong POV, I'd love to learn from you: 1. Ambient listening: Given Nuance knows a thing or two about dictation and speech, and Msft about LLMs, what is the real angle for new players? Is it fewer hallucinations? More accuracy? More actionable guidance based on proprietary medical knowledge? I am assuming everything needs to go back into the EHR so why is this not simply Epic + Msft wins? I get the stuff about elephants not dancing and such but still...? 2. Clinical Diagnoses: Is this for real? Will we really see a day that my historical and real time medical record, (not just the stuff I see on Apple Health) can be fed into a fine-tuned model with all the RAG, RLHF bells and whistles, and out will pop helpful guidance for medication or other meaningful interventions I should take? And for all the talk of "provider in the loop / AI is an assistant not the agent" talk, is there a real example of this capability saving time / $$ / creating access? (Outside of radiology - I get how images are a well solved problem) 3. Workflow integration: Related to 2. above, has anyone seen a real LLM use case integrated into nursing / clinician / other provider workflow in a useful way that doesn't make them use multiple screens, create new and often artificial alerts and triggers, and add burden as opposed to take it away? Above all, are hospitals and staff buying into / trusting any specific use cases? I'd love to see a case study or two of real ROI delivered with ecstatic users who would hate to lose the capability. This is revealing my ignorance, so please educate me on the reality and near term (1 - 2 year) potential, I get all the stuff about the long term promise. Thanks in advance!

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Open-source models can be a strong lever for increasing gross margins at AI companies, especially those that have hefty cost structures (e.g., enterprise sales orgs that require significant customer success or implementation resources, as well forward-deployed AI services models common in healthcare and life sciences). One of many reasons to maintain a modular and flexible stack with minimal dependencies when building an AI company. More on the importance of flexibility in the AI stack: https://lnkd.in/eA2XATRk h/t Delip Rao #ai #artificialintelligence #generativeai #healthcare

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I used to spend 2-3 hours daily on clinic notes, skeptical of AI scribe technology. However, I've been impressed with RevMaxx, a generative AI company transforming clinical note creation by listening to physician-patient conversations and automatically generating comprehensive notes. This platform accurately captures crucial ICD-10 codes, saving valuable time ⏰. 👉 Why This Matters: - **Reducing Physician Burnout:** Administrative tasks are a significant contributor to physician burnout. Innovations like RevMaxx alleviate this burden effectively. #HealthcareInnovation #AI #PhysicianBurnout #MedicalDocumentation #HealthTech #RevMaxx

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Read how StoryMD is transforming patient engagement by changing the way patients view their health data, empowering them to understand, engage, and take control.

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Someone broke down the estimated costs of a $15 Sweetgreen salad. We need these cost graphics for digital health to help future founders. Especially for tech enabled cos, the greatest cost is labor. Much of the "value created" is in curation and matching, which means the gross margins are reliant on labor arbitrage. Pay someone $X, but get paid $X+Y% per visit. The challenge with healthcare is that you often have to pick one of two business models: 1. specialized, low volume, high cost, low gross margin % 2. generalized, high volume, low cost, high gross margin % I'd argue founders should focus on gross dollars over the lifetime of the customer (like Jeff Bezos said) instead of gross margin %'s, but we'll leave that for another time. The challenge is that many tech enabled digital health cos have high cost of goods sold (labor) and moderately high product, design, and engineering budgets. We must build differentiated solutions for patients, clinicians, and clients to be out incumbents, however, we're realizing that companies are ultimately valued on the discounted value of future cash flows. And the high SG&A costs at most organizations might be inbalanced to the unit economics of the business. I'm not sure the math pencils out for everyone unfortunately. But I'm just a guy on the internet, would love to hear the thoughts from those smarter than I (you!). Comment below. --- p.s. I have no idea the accuracy of the graphic and not an investor in Sweetgreen. Cheers. Credit: David Crowther

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One of my proudest moments as an investor occurred today as Alaffia Health announced its series A because it shows how the Aperture® Venture Capital vision of multi-level, multi-generational #impactinvesting is succeeding in the marketplace. Here’s the model in its most basic form : ✅As diverse fund managers with meaningful capital to allocate, we are changing the VC landscape every day just by doing our day jobs. ✅As Black/Brown investors with ~40 years experience collectively, Aperture GPs have access to talent /excellence that others do not, so our portfolio *organically* is more inclusive by race, gender and geography even while optimizing for financial outcomes (all about the alpha). ✅Our most successful portco’s are using financial #innovation to solve market problems that impact underrepresented demographics and underserved communities. Alaffia Health is a shining example of the impact portion of our overall fund thesis, and we couldn’t be prouder of TJ Ademiluyi and Adun Akanni, MPH, PMP - the dynamic brother-sister founder duo whose vision we have steadfastly supported on their journey. Congratulations to TJ and Adun from William Crowder and myself, as well as the whole Aperture team- Marjorie King Philip McKenzie Yves Louis-Jacques Tanvi Lal Michelle Dhansinghani Lisha Bell Katie Kelly Amy Chung Cindy Chong, CFA Brian Fernandes-Halloran Monroe France Jayden Pantel Darren Herman Evan Wladis Neal Triplett Thomas Scriven Peter Ammon Irina Bit-Babik Tim Milanich Rob Rahbari

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What's been buzzing the loudest for me so far at HLTH Europe? AI, AI, and more AI, of course. Tongues have been tripping over those two letters in every conversation I've had today or listened to including these nuggets: Michael Chen of Nuclera: "Generative AI is wonderful because it brings more hypothesis-making power for the drug discovery researcher. But ultimately, we all know that AI isn't enough. You need to have the power at the wet lab benchtop in order to validate experiments and bring hits and needs into the local trials, and the quality of these hits and leads that ultimately will help the success rates in phase one through three clinical trials, which often don't rear their ugly heads until it's too late." Geraldine O'Keeffe of EQT Group: "There's a huge amount of investment opportunity in terms of AI-, SaaS-based models, in big data and machine learning in diagnostics, precision medicine...delivering the care you need better, faster, cheaper." Karen DeSalvo of Google: "The availability of AI, the ubiquity of it, and just how quickly that is moving [has been significant in the evolution of AI in healthcare]. What I'm hoping now is the next generation of this technology allows us to democratise access to health for people all over the world, and that we can start to do that much more quickly and much more equitably." Chiara Bucciarelli-Ducci, MD, PhD and Marlies Schijven on clinicians' take on AI: "To scale up adoption of AI in healthcare, industry needs to demonstrate to clinicians and healthcare providers the cost effectiveness and cost efficiencies that the technology can bring at every level." "More often than not, we find that AI does work and helps to bring efficiencies, but it also carries risk. So it needs to be properly explained." Agree, disagree, or follow-up comments on these soundbites? #genAI #AIeverywhere #medicalAI #healthtech #digitalhealth #AIfordrugdiscovery #techbio #healthAI #clinicalAI

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🚨 Attention all Health Tech Founders, Product Leaders, and Investors! 🚨 ​ The ZIRP era is long gone. ​ Growing revenue and attracting investment in healthcare has shifted dramatically. The new currency for healthcare product companies is undeniable proof that your product delivers results. So what should every healthcare product company have on their roadmap? ​ 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐞𝐯𝐢𝐝𝐞𝐧𝐜𝐞 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐨𝐫 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐭𝐫𝐢𝐚𝐥𝐬. ​ The problem is that this process has previously been super confusing to understand. If you wing it, it’s costly to execute. And usually it’s usually explained in a way that's dry and overly academic. ​ We believe generating evidence is so important for healthcare companies that we have boiled down the 80/20 of what you need to know, into a highly actionable 1-hour workshop called ​ 𝐒𝐚𝐥𝐞𝐬-𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 ​ 🔬 This workshop will teach you: How to generate the RIGHT type of evidence How to do it COST-EFFECTIVELY How to leverage this evidence to boost your revenue ​ 🎤 Leading the workshop is 𝐃𝐫. Ben Rosner, MD, PhD—a luminary in digital health research who has successfully applied these strategies in real-world product companies. Dr. Rosner not only understands the science but also the practical business implications, making him the perfect guide. ​ 💡Don't miss this opportunity to learn how to generate evidence that creates revenue. 📆 Date: May 2, 2024 ​ ​ Time: 3PM ET💡 ​ Spaces are limited, Get your spot today 👇🏻

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Every time a brand name investor posts a rallying cry “time to fix the $4T US healthcare”, many entrepreneurs will pay attention. There will be a rush into healthcare, primarily in the HIT field, and, lately, AI. What is important to understand, is that US healthcare is not a $4T market in terms of TAM/SAM calculations. It is a thousand small markets, none of which would be attractive to VC capital by itself, if thoroughly understood and well-defined. There are hundreds of incumbents in this highly fractured environment and some of them had monopolised their niches. While this is true, the Healthcare overall expense of 20% of GDP is unsustainable. How do you look at this spend empirically, sans the hype? Look for horizontal underlying patterns and market-spanning issues and yes, one has to be on top of regulatory space and learn it as well as their technology. All these are huge barriers to entry, but absolutely required for developing long term healthcare solutions. One of these horizontal and paramount issues is amount of healthcare data produced, collected and exchanged. Of total amount of data produced in the world now, 30% is generated by healthcare. 97% was of that data is unused. That should be a rallying cry.

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Perfect example of solving for a customer pain point, not innovation for #innovation’s sake. The founder is a physician and hated spending >2 hours daily on summarizing his day’s work, as required by law. This burden is driving people to leave the profession. So, Abridge built an AI app to automate a lot of this admin work and summarization for docs and hospitals. Now, Abridge is blowing up, rightfully so! Their customers (doctors) call the product ‘magical’ and ‘life changing’. A few lessons here: - Starting from real customer pain points is a winning strategy - #AI can add enromous value by solving mundane problems (not every AI application needs to be futuristic) - People with deep experience in an industry can leverage not just their expertise, but their tactical experience with daily hassles, to build great ventures Congrats Shiv and team Abridge, well deserved! 237 Ventures MENA FINTECH ASSOCIATION https://lnkd.in/dwE6nfKJ

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