Freddy Abnousi, MD, MBA, MSc

With $8.2B invested into digital health so far, 2024 is shaping up to be a $10-12B annualized investment year. This would make funding comparable to 2023. I suspect with continued venture investment, boards will require exits. One thing I’m particularly interested in is terms of recent acquisitions and growth equity rounds. What type of downside protection (e.g., structure) is not disclosed to media that protects the investor? Will funds who have specifically raised capital to buy stakes in VC-backed companies at significant discounts? And what revenue/EBITDA multiples are looking like for M&A in 2024/2025? Every founder has to spin their story in a positive light when speaking to media. I know this because PR agencies reach out to me about their clients (p.s. I don’t consider myself a member of the media). The private health tech market feels like it could significantly rip or drop in the next 12-24 months. Therefore, my general advice to early-stage founders is to assume you will not raise capital beyond your seed round. If the investor market heats up in 2025, you will have the option to raise a series A at that time if you’ve built a strong business. If you’ve already raised series c or beyond, then one of your goals will be to time the exit (M&A or IPO) appropriately so that you garner an attractive multiple. There are few private health tech startups that have founder board control, can reinvest positive cash flows, and stay private forever. So by necessity of the structure, exits are desired. - - - - Image Credit: Rock Health

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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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Key trends and takeaways from HLTH 2024: 1. AI's Evolving Role: AI dominated discussions, with executives acknowledging its potential while also recognizing challenges in accuracy and clinician trust. There's a shift towards focusing on the practical applications of AI, with a call for partnerships to ensure reliable testing and implementation. 2. Infrastructure Focus: Beyond the glitzy AI tools, there's a growing emphasis on the "picks and shovels" needed for healthcare data accessibility, including infrastructure, compliance, and interoperability. Executives believe that improving data flow will facilitate the effective use of AI. 3. Middlemen Under Scrutiny: Pharmacy benefits managers (PBMs) faced criticism for their role in rising drug costs, with discussions highlighting misaligned incentives and the need for transparency in healthcare pricing. 4. Women's Health Spotlight: Women's health initiatives gained attention, particularly following government funding announcements and personal stories shared by advocates. However, investment in women's health remains minimal compared to overall healthcare funding. 5. Interest in Psychedelic Medicine: The potential of psychedelics for treating mental health conditions garnered significant interest, with discussions about funding and research moving forward, especially within the Veterans Affairs system. Executive Takeaways: Leaders from various sectors emphasized the importance of consumer-focused strategies, collaboration, and the need for responsible AI usage. There was a consensus on the critical role of addressing healthcare accessibility, improving patient experiences, and fostering innovations that genuinely enhance care delivery.

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Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Clinician Assistant • Suki is partnering with Zoom to generate clinical notes as well as other integrated AI features.    • Net Health announced it would integrate AI ambient scribe Tali AI. • Aidoc is partnering with NVIDIA to create a guideline to accelerate AI adoption, the Blueprint for Resilient Integration and Deployment of Guided Excellence (BRIDGE). 📞 AI Contact Center • Authenticx released a new AI assistant that helps users uncover business insights from their data. • Infinitus Systems, Inc. closed a $51.5M Series C with participation from Memorial Hermann Health System and others.   🏥 AI Facility Management • Cognosos, Inc. acquired Cox Prosight. 🌡️ AI Remote Patient Monitoring and Triage • Biofourmis has merged with CopilotIQ. 📈 Data & Analytics • Avandra Imaging launched out of stealth; it plans to sell to biopharma, medical research, and AI companies. • Gradient Health acquired DataAppraisal. • HealthEx launched and announced $14M in Seed and Series A funding. 📋 Patient Administration • Notable released Flow Builder, which allows users to develop their own AI agents for operational workflows within EHRs and other systems. • MIla Health Inc released its flagship AI solution of the same name, and announced implementations at provider organizations including Aayu Clinic, MIMIT Health, and Best Practices Inpatient Care. 🔁 RCM • Oracle’s patient accounting solution has been integrated with FinThrive, which enables improved functionality, performance, and a unified login experience. • Blue Shield of California and Salesforce are partnering on a solution that promises to give patients prior auth responses within the course of their appointment. ⚙️ Clinical Operations • Dyania Health closed a $10M Series A, including participation from Cleveland Clinic Ventures. 📱 Patient Support & Communications • Psych Hub released a new solution designed to support patients seeking mental health support, directing them to both non-clinical and clinical resources, thereby assisting behavioral health providers to reach new patients. • Mayo Clinic has partnered with Lin Health to support patients after treatment for fibromyalgia.   🍎 VBC • WellSky acquired Bonafide Health, allowing it to provide a more integrated platform for providers to run their businesses. 🧠 Food For Thought • 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘁𝗼 𝗸𝗲𝗲𝗽 𝘂𝗽 𝘄𝗶𝘁𝗵 𝗮𝗹𝗹 𝘁𝗵𝗲 𝗔𝗜 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀? Andrew Hines, CTO at Canvas Medical, shared a breakdown (plus some hot takes) on all of the many entities currently vying for market share in the AI governance space https://lnkd.in/eMNpXw4t --- Want the latest healthcare tech news delivered to your inbox each week? Sign up here 📬 👉 https://lnkd.in/e2UJ3ckP

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To follow up on Dede Ainbinder recent post: If we use the current EHR data to build a AI clinical decision support tool it is unlikely to capture our clinical decision making process. EHR's are inherently designed to maximize billing and minimize risk of litigation. A clinical decision support tool based on this data will likely do the same. There is a large sunken cost fallacy here where we continue to hang on to garbage data. Just because the data gathering and storage process was capital and time intensive doesnt mean the data is valuable. To truly capture the clinical decision process we need to capture data which sits outside billing and litigation. Its hard to imagine a future where this data isnt used for billing and cannot be called upon during litigation. #EHR #healthdata #clinicaldecision

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Big healthcare AI news yesterday as Tempus AI received the first-ever CPT code for an algorithm-only analysis of previously-sequenced, laboratory developed test transcriptomic data. TLDR: AI molecular phenotyping has a new business model. The PurISTSM test uses RNA profiling to identify molecular subtypes in patients with advanced pancreatic ductal adenocarcinoma, which may inform first-line therapy selection and management. While I'm skeptical that payer balance sheets are going to solve the "who pays for healthcare AI" problem, the reimbursement pipes for clinical AI are maturing. I am surprised to see more progress for usage-based vs. performance-based models given the many challenges with FFS economics. It will be interesting to see how payers assess the "medical necessity" and therefore reimbursability of clinical AI as these models come to market. https://t.co/eLe18SmihV

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🚀 Big tech giants 𝐀𝐦𝐚𝐳𝐨𝐧, 𝐆𝐨𝐨𝐠𝐥𝐞, 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭, 𝐚𝐧𝐝 𝐍𝐯𝐢𝐝𝐢𝐚 are diving into the $11T healthcare market, each with unique strategies. 2024 Big Tech in Healthcare Report delves into their partnerships, strategic investments, and product launches. #HealthcareInnovation #BigTech #Amazon #Google #Microsoft #Nvidia #HealthTech #FutureOfHealthcare

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Latest #PitchBook data for #HealthIT investment shows a preference for later stage investment across healthIT that pre-dates the VC downturn and current interest rate environment. Even though healthIT has been more resilient to current headwinds compared to health care services and PPM plays, perhaps it speaks to a need to scale solutions and points of saturation for new entrants. "...In the lower middle market, we are beginning to see more examples of a phenomenon we have anticipated for a while: PE firms and PE-backed companies acquiring VC-backed startups. Lower-middle-market PE firms perennially complain that VC- backed companies expect too-high multiples, and these firms would generally prefer to invest on a multiple of EBITDA rather than a multiple of revenue..." Speaks to a convergence that would allow PE-VC M&A on both ends of the startup spectrum. #HealthcareVC #healthcarePE

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Excited for our webinar tonight: Investing in Healthcare Startups: An Angel Perspective When: tonight at 6 pm eastern Where: https://lnkd.in/dPUFNbhe We had had a couple last minute changes and I am very excited to have an engaging conversation with Thanasi Tsiodras, Dr. Shaun Kuan and Regina Prasad, MS. We will be discussing: 1. How to start investing in startups 2. Lessons from your winners and losers 3. What are some red flags in a startup 4. What is your diligence process like? 5. How do you get deal flow?  6. What is the ideal angel investor archetype? Who should invest in startups and who shouldn't. #healthcare #venturecapital #angelinvesting

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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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#transcript Fadi Micaelian, CEO of Sparkdit, and Dr. Andrew Fang, Chief of Orthopedics at Kaiser South San Francisco, have joined together to help patients and doctors make better treatment decisions by considering trade-offs and patient preferences. The platform can help reduce bias and incorporate patient desires into care while considering comorbidities and other factors that may affect treatment decisions. Fadi and Andrew agree that collaboration between technologists and clinicians is essential for advancing the use of AI in healthcare to augment human intelligence and improve outcomes. #MedAI #AI #DigitalHealth #MedTech #Orthopedics #PatientDecisions #DecisionIntelligence sparkdit.com https://lnkd.in/geBPctUH

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Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Products • Emergency Services, Inc. + Augmedix Go ED: The emergency medicine group adopted the new ED-specific AI ambient scribe solution.    • Color + OpenAI: The cancer care tech company partnered with the GenAI giant to build a AI clinical decision support copilot.    • AKASA Medical Coding: The AI RCM vendor known for its AI prior auth tool AKASA Authorization Advisor released a new GenAI medical coding solution.    • Humata Health: The AI prior auth for providers vendor closed a $25M funding round led by healthcare org-backed funds The Blue Venture Fund and LRVHealth.    • Wellsheet + Concord Hospital Health System: The AI-enabled Smart EHR UI solution announced a client-reported 16.3% reduction in length of stay and 40% decrease in time spent in the EHR via its partnership with Concord Health, as well as the release of its new LLM-generated handoff summary feature. 🍎 VBC • BSIM Healthcare Services + Innovaccer: The healthcare provider—embarking on its first VBC contract—adopted the pop health data platform for analytics. 🩼 Durable Medical Equipment • Parachute Health: The DME e-prescribing and ordering solution released an update that integrates prior authorization. ⚕️Clinical Staffing • Matchwell + Indiana Hospital Association: The clinical staffing service partnered with the hospital member org to create the first state-wide resource pool for temp staffing. 🧑‍💻 Virtual and At-Home Care • Deaconess Health System + KeyCare: The Indiana-based health system adopted the Epic-based virtual care provider for around-the-clock virtual urgent care.    • UC Davis Health + Current Health: The health system launched a new RPM program for high blood pressure in partnership with Best Buy Health’s care-at-home platform. 🧠 Food for Thought: We came across a couple of good resources this past week on the current state of healthcare data information exchange: • This article in Health Affairs from Deven McGraw and Tina Grande provides a comprehensive overview of some of the patient health data abuses we've seen come to light in recent months, as well as a number of policy recommendations to improve the health information exchange landscape for all participants. https://lnkd.in/e_GEQ-JJ  (h/t Alya Sulaiman)    • This episode of Health Gorilla's InteropTalk gives a helpful update on the current state of affairs, with a particular focus on the state of trust across the health data exchange community and how the definition of "Treatment" may evolve. https://lnkd.in/e96AJG-5 (h/t Brendan Keeler) --- Reminder: you can sign up for the Elion Briefing to get the latest healthcare tech news delivered to your inbox each week 📬 👉 https://lnkd.in/e2UJ3ckP

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If everybody's a patient, here's a few tips on how you can use gen AI for medical visits. Gems from patient advocacy rockstars Grace Vinton and Grace Cordovano, PhD, BCPA: 💡 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬: Our time with specialists is limited. Use AI tools to prepare for medical visits. Describe your problem and as AI: "What should I ask my clinician?" This can help you make the most of your time with healthcare professionals. 💡 𝐑𝐞𝐟𝐢𝐧𝐞 𝐏𝐫𝐨𝐦𝐩𝐭𝐬: Developing good prompts is crucial for getting the most useful responses from AI. Be specific, and don’t hesitate to refine and adjust prompts to get better results. For example, if you are using AI to interact with an insurance to appeal a denied claim, make sure to describe the situation and your goal (convincing appeal) to AI. 💡 𝐂𝐡𝐞𝐜𝐤 𝐟𝐨𝐫 𝐄𝐫𝐫𝐨𝐫𝐬: AI can help patients review their medical records for potential errors or duplications, which can improve the accuracy of their care. 🎥 Watch the interview on Youtube : https://lnkd.in/dJrdC5dN 🎧 Tune in: https://lnkd.in/drydfZmz This interview was recorded at the Digital Health & AI Innovation Summit (DHAI). See the recap of key insights in the Faces Of Digital Health Podcast newsletter: https://lnkd.in/dpV9AXyF #patients #healthcare #generativeAI #patientsuseAI

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Have some time over the Labor Day weekend? Learn about how AI is being used in Healthcare... not in theory, but in one of the best run health systems: Virtual Health Tarun Kapoor is one of the best guests we have on Health:Further. Here are the topics we cover: -- Partnership between Virtua Health and Woebot, focusing on their application in acute care and behavioral health. -- Challenges of handling increased patient volume and acuity due to demographic shifts. -- Integration of AI in healthcare settings, particularly in mental health with digital therapeutics, and the implications of large language models (LLMs) in the medical field.  -- Impact of AI on healthcare workforce dynamics, the concept of "smart rooms" in hospitals, and the evolving role of clinicians in an AI-augmented environment. Please check it out here https://hubs.la/Q02Nd-8K0 Tune in now to explore AI's transformative role in healthcare!

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#Niche-focused #ventures in #HealthTech, such as those #targeting #underserved areas or #specific health conditions, offer several benefits. They can address gaps in the #healthcare system, provide specialized care, and #potentially integrate into broader #ecosystems for comprehensive healthcare solutions. For example, #JVEDTECHMedovation, could explore #partnerships with #niche #HealthTech startups to enhance offerings in healthcare #innovation.

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🚀 Breaking Ground in ER Decision-Making! A recent study by Icahn School of Medicine at Mount Sinai reveals how generative AI is poised to revolutionize emergency room admissions. Surpassing traditional models, this AI can predict hospital admissions with minimal data training, potentially transforming high-pressure decision environments. Dive into the findings published in the Journal of the American Medical Informatics Association and see how AI is set to enhance, not replace, the human touch in healthcare. 🏥🤖 #HealthTech #AIinHealthcare #EmergencyMedicine

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It's been unique to go through medical training during the era of AI. Studying for board exams with language models, using AI literature search products to answer tough questions during rounds, and trying every agent-based patient simulator to strengthen diagnostic skills. There's a new physician stack forming around us thanks to AI. If you're interested in these topics and AI in medical education more broadly, please join me for the 10th Annual Conference, Precision Medicine 2024: Education in the AI Era at Harvard Medical School—a hybrid in-person/virtual event, on October 1st. Together with James Diao and Shivangi Goel, MD, MBA, we'll be talking about non-linear paths through medical training. Plus, you'll get to hear Dr. Isaac Kohane debate Dr. Pete Szolovits, on the topic of “Should GPT-4 Write my Thesis." Register here: https://lnkd.in/gSddDA63

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How can we build the right infrastructure to support remote patient monitoring (RPM) for all kinds of patients in all kinds of settings? Check out this new paper in NEJM Catalyst by recent medical school graduate, Nina Singh, MD on why and how to create the core capabilities within a health system , taking a different, more scalable approach -- "the focus is on building tools and workflows for practices that are interested in using RPM rather than on building centralized RPM programs for specific high-need populations." Practical tips include a #playbook for RPM and much more: "Exploratory features include a playbook detailing best practices for RPM use across NYULH, a device loaner program, direct Bluetooth pairing (as opposed to Bluetooth syncing and transmission of data through data aggregator pathways), systematic offboarding workflows for inactive or stable patients, and small centralized programs being developed among groups of practices with similar patient populations and needs (e.g., primary care faculty group practices managing hypertension). Super proud of Dr Singh's work during her year off with Nader Mherabi and others in informatics at NYULANGONEMEDICALCENTER. She's heading to University of California, San Francisco Department of Medicine to start her internship--what a star! Talmadge E. King, Jr. MD Robert Wachter Atul Butte https://lnkd.in/e-VpGie7

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