Kyna Fong, PhD

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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Hot off the pre-print server – an important publication authored by the Atropos Health team exploring the performance of LLMs in answering real-world clinical questions. LLMs excel at summarizing and contextualizing existing literature, but cannot generate de novo medical evidence today. The results of the study highlight performance gains attributable to injecting novel, relevant, and up-to-date medical evidence into LLM-powered clinical decision support systems. Brigham Hyde Nigam Shah Saurabh Gombar Read the study here: https://lnkd.in/ecuU25Pq

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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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I always read Rock Health's digital health reports as they give a nice pulse feel for what is happening in the digital health funding space. Good to see funds raised in H1 2024 suggest funding in the sector seems to have stabilized at 2023 levels. Its no surprise the report emphasis the buzz around AI and healthcare. There have been clear winners in the AI healthcare space in the area of process and cost optimization and the R&D space. The impact of AI in care and treatment delivery is so far much more muted. One area I never though to look at AI was in its ability to bring together multiple healthcare pathways and digital point solutions into a more holistic seamless experience for the patient. There is a clear trend away from using stand alone digital health point solutions, and could AI address this challenge by facilitating the efficient integration of disparate processes and technology into unified care programs. I can see this happening with digital health products but this will be more tricky to do with physical care programs involving clinicians and HCPs. “AI is helping us combine multiple parts of the care journey, across all care experiences. In the future, benefits navigation, clinical guidance, and care delivery won’t be delivered by separate companies or point solutions within the healthcare ecosystem. They’ll be features that work in concert within products, and they’ll happen in under 90 seconds for a seamless patient experience.” – Glen Tullman, CEO, Transcarent and Managing Partner, 7wireVentures

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Investing in companies that fill needs for health systems is a growth opportunity for both delivery organizations and start ups. Some systems are jumping in on their own, while others are partnering with like minded systems or with established VC firms. While this is a relatively new trend for systems, health plans have been making similar bets for many years. The benefits of becoming a health system portfolio company can be significant. Data, access to beta populations and real world coaching can be extremely beneficial. Start ups get mentoring and systems have to ability to leverage outside resources to solve relevant business problems. I predict a booming business!

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Q1 2024 was big for Digital Health, with $1.1B in VC funding. A large portion came from major deals like $126M for Transcarent and $108.8M for Rightway. Despite some challenges in digital therapeutics and telehealth, investments in general care ($213.4M) and health benefits navigation ($140.7M) are promising. Read what’s driving the shift from direct-to-consumer to enterprise telehealth models here: https://ow.ly/uNYs50SpXGh

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🚀 𝐔𝐧𝐥𝐨𝐜𝐤 𝐒𝐭𝐚𝐫𝐭𝐮𝐩 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐰𝐢𝐭𝐡 𝐔𝐫𝐢 𝐋𝐞𝐯𝐢𝐧𝐞’𝐬 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬! 🚀 Uri Levine, co-founder of Waze and Moovit, shares invaluable lessons in his book “𝘍𝘢𝘭𝘭 𝘪𝘯 𝘓𝘰𝘷𝘦 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘗𝘳𝘰𝘣𝘭𝘦𝘮, 𝘕𝘰𝘵 𝘵𝘩𝘦 𝘚𝘰𝘭𝘶𝘵𝘪𝘰𝘯.” Key takeaways include the importance of passionately embracing the problem you’re solving, the necessity of customer retention as a marker of product-market fit, and the strategic use of pitch deck slides. He also emphasises the significance of hiring and firing correctly and observing user behaviour to refine your product. These principles can transform your startup journey, ensuring resilience and growth. 🌟 #StartupSuccess #Entrepreneurship #ProductMarketFit #Innovation

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We just released our H1 2024 Digital Health funding summary. https://lnkd.in/gAZY64tp H1 2024 Digital Health funding closed at $12.4 billion invested across 719 deals. - Q2 2024 is outperforming Q1 by 34% ($7.1 billion vs. $5.3 billion)! - Digital Health funding has shown sustained investor confidence. Funding in Digital Health in North America is up 14% to $9.5 billion. Investment in Europe is down 10% to $1.52 billion, while funding in APAC dropped by 7% to $1.17 billion. - Partnerships in Digital Health are flat in the first half at 1,730. NVIDIA has been the strongest corporate partner in H1 2024 - 96 M&As, 8 bankruptcies, 5 delistings from the stock market and 3 relistings from NASDAQ to OTC market. The full report will be available for free next week. Feel free to comment and share if you want to receive a copy prior to our official launch 😊 Galen Growth | Insights You Can Trust #funding #digitalhealth #investments #partnerships

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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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Should you sell your practice to a VC-Backed MSO Startup? tldr: - new VC-backed startups are forming MSOs to roll up independent practices - The pitch is physicians receive early liquidity from the roll up and are granted ESOP shares that can be used for retirement - Problem is the use of ESOPs with independent practices is not a new concept and has been used with large group practices since the 70's - ESOPs do not grant full ownership and in order for the shares to be valuable for retirement, the practice needs to run the same if not better post roll up - With VC-backed startups being primarily operated by non-physicians with zero clinical or medical practice experience, the likelihood of this happening is extremely low making the entire concept high risk However, there is a viable path for continued ownership + liquidity for independent practices. Check out the article to learn more. ⤵ https://lnkd.in/gmGvTYwP

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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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Here’s your recap of last week’s health IT news 🗞️ Notable partnerships 🤝 • Kaiser Permanente is partnering with Innovaccer to incorporate their population health and AI platform        • MUSC Health adopted Andor Health’s ThinkAndor in its ED to reduce wait times and “leaving without being seen” rates.      • Upperline Health is partnering with Navina to improve clinical workflows and improve the accuracy of patient histories.      • Intermountain Health deployed Nuance Communications DAX Copilot, an AI ambient scribe solution.      • PA Clinical Network is partnering with Guidehealth  to improve outcomes and care quality while improving physician efficiency. Product updates 👩🏼‍💻 • Klara released a new patient intake product for athenahealth users.    • Baton Health released Universal Primary Source, giving users access to hundreds of primary source databases for credentialing.    • InsightRX released InsightRX Apollo, an analytics platform that provides insights on clinical performance.    • Trovo Health launched with a $15 million seed round, offering specialty-specific AI combined with clinical experts to help practices add to their capabilities.    • Quest Diagnostics acquired PathAI’s diagnostic laboratory business (PathAI Diagnostics) to improve Quest’s oncology services.    • GenHealth.ai launched its Automated Prior Authorization platform.    • Veradigm® is partnering with MedEvolve, Inc. to make its suite of products available to Veradigm users.    • CLEW announced availability of a newly FDA-approved patient surveillance platform. Posts we enjoyed 👏 • Eric Topol, MD shared a compelling piece reviewing the state of research on AI in medicine. In short, we’re seeing our first early evidence that AI can reduce mortality as a research endpoint, but large language models are not yet the panacea we’ve been promised. (https://lnkd.in/e6hK-4mr)    • Nikhil Krishnan of Out-Of-Pocket shared a deep dive on Stedi, which converts X12 EDI into JSON-based APIs for healthcare tech developers. (https://lnkd.in/eqS9V5zW) --- Want to stay in the loop? Sign up for the Elion Briefing to get all of the latest healthcare tech news delivered to your inbox 📬 👉 https://bit.ly/49z6AUW

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I just read an article put out by TechCrunch on the most exciting digital health startups of 2024 and not surprisingly, over half of them were AI companies. Also not surprising, all of the AI companies were focused on reducing administrative friction (automating prior auth, medical coding automation and review, etc). This isn’t surprising because these tasks play to AI’s current strengths, there are lower regulatory barriers, decent data standardization, etc. Most of us working in this space have predicted that AI for healthcare administration is where we’ll see earliest adoption. I’m grappling with something on the back-end of this though. We point to the high administrative costs of healthcare (up to 30% of total industry spend) time and time again as a reason why healthcare (usually via premiums) is so expensive for patients/consumers. The promise of all of these AI-for-healthcare-administration start-ups is that they will reduce these costs. But who will they reduce costs for? The value props always end at the health systems and the carriers — helping them generate more revenue and avoid inappropriate spend through automated, high-accuracy processes. We wave our hands and say that this will somehow flow down to consumers, but how? Someone needs to govern these applications of AI in healthcare and how they will ultimately change affordability for consumers. One hot take is that it’s time to reconsider the Medical Loss Ratio. If AI is going to chip away at the 20% of premiums that carriers are allowed to spend on administrative costs, maybe we should tighten the limit on premium allocation to these types of costs. AI is exploding in healthcare right now and without some governance over who is paying for it and who is benefitting, we run the risk of actually continuing to drive up healthcare costs for consumers despite increasing systemic efficiencies. Nikhil Krishnan's recent post on AI in healthcare enlightened me about how this is happening in AI for clinical applications already — somehow the CPT code price tag for an AI read of a medical image is more expensive than a human radiologist reading the same image. We don’t yet live in a world where AI replaces radiologists, so I can only imagine that not only is a more expensive CPT code being applied when an AI read is performed, but that it is applied in addition to the human read. The potential here is tremendous — for good — but let’s make sure we’re using AI to do what we say we’re doing. #healthcareAI #healthcareaffordability

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Excellent post Emily Lindemer, PhD! The industry is spending a lot of time and energy on clinical applications of AI, and understandably so given the massive potential. But the near term opportunities, for all the reasons you point out, is in administrative applications. These are far less 'sexy' use cases for AI, but represent massive cost burdens and all of that value is flowing to the payers and providers (as well as the AI companies themselves) with little to no evidence that it is, or will, trickle down to consumers.

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Elon Musk’s xAI, Which Just Raised $6 Billion, Has Significant Potential To Disrupt Healthcare via Sai Balasubramanian, M.D., J.D. "The most obvious entry point for xAI is similar to what other large, technology companies are attempting to do: increase task automation, improve clinical workflows, and optimize clinician productivity. Technology giants are investing billions of dollars creating and training foundation models so that they can easily fit into the day-to-day healthcare workflow."

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Interesting to see that digital health funding is relatively stable. Only a couple of exits. I think we'll see more deals like DarioHealth's acquisition of Twill, since from a sales perspective, the narrower the solution, the more difficult it is to acquire customers. This difficulty has nothing to do with the quality of offerings, but with the data management and administrative complexity on the customer side.

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There’s a category we believe is getting too little attention when it comes to AI in healthcare: AI Clinical Documentation Integrity (CDI) While AI ambient scribes and autonomous medical coding solutions have gotten most of the headlines, we’d argue AI CDI is equally impactful when it comes to quality care and accurate billing. For those not familiar, CDI ensures that clinical documentation accurately reflects the patient’s medical condition and the provided care. Historically, CDI was a manual slog, with dedicated CDI specialists, nurses, and physicians painstakingly reviewing patient charts to ensure documentation supported the care delivered. Today, AI is reshaping this landscape. While it’s still early days, we’re seeing three primary approaches to applying AI when it comes to CDI: 1️⃣ CDI + ambient scribing—an AI ambient scribe and AI CDI assistant operate simultaneously, generating comprehensive notes and surfacing codes for physicians to review at the point of care. (Example: Ambience Healthcare’s AutoCDI) 2️⃣ CDI via real-time diagnosis support—diagnoses are suggested at the point of care based on clinical data in the EHR, with auto-generated documentation and audit trails. (Example: Regard) 3️⃣ CDI via an AI-driven post-coding / pre-billing review—the entire patient record is ingested and analyzed against what is coded, surfacing suggestions for revenue and quality opportunities. (Example: SmarterDx) To date, AI in healthcare has focused primarily on administrative use cases. With the increasing adoption of AI CDI solutions, we’re now taking a step closer to clinical workflows. As clinicians get used to AI-suggested diagnoses in the CDI context, it will be interesting to see if this increases their comfort with AI playing a role in clinical care. Here's our working list of AI CDI vendors. Anyone else we should add? • Ambience Healthcare • Brellium • Iodine Software • Regard • Semantic Health • SmarterDx   

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The defining factor isn't the size of the company or startup, but the vision behind it. Take OpenAI, for instance. Until last year, unless you were deeply embedded in the ML world as a computer scientist/researcher, or enthusiast, you might not have heard of OpenAI. Meanwhile, a giant like Google was dominating the scene. However, a few years back, OpenAI was just another small startup. Now, it's rapidly evolving into a major player in big tech. Why and how? It comes down to having a long-term vision, a clear roadmap, patience, and solid execution (i.e., talents). Even in the context of healthcare AI, where (small) startups face many challenges (including the ones mentioned), the principles of vision and strategy apply. While merging with big tech might seem like an option for some, if a startup truly believes in its vision and has a well-defined path to achieve it, it should forge ahead independently. The landscape is evolving, and like OpenAI, today’s small startups with clear, visionary goals could be tomorrow's industry leaders. #starup #founders #openai #google #healthcare

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