Alexander Singh

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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Day Zero dispatchs from HLTH Europe For an event that hadn't officially opened yet, there was a huge amount going on yesterday. Adequately fueled and hydrated, your intrepid avatar launched into the festivities. What were some of the highlights? HealthXL hosted an event looking at AI in Lifesciences and Pharma. They always draw a huge crowd on leaders from industry, and today was no different. Chandana Fitzgerald started with the news that they have merged with HLTH, becoming HLTH Community! Robert Garber led with a VC summary of the state of AI, and how to avoid the infamous Trough of Disillusionment. With 82% of companies looking to implement AI governance, it's a good time for those looking to make AI safe and effective. Unsurprisingly, hes also seeing greatest focus on the less complex, administrative side of healthcare. Cristina Ortega Duran gave a lively presentation on their successes with Evinova, which integrates trail data into a single platform. Stephen R. called on the audience to follow 3 rules: focus on the customer problem first, learn quickly, and follow the evidence. Yes, he's turning everyone into Product Managers. During the panel we had great questions about bias and representation from Aahuti Rai, which underlined the role that synthetic data and telehealth have to play in filling gaps and reducing the burden of trial participation. The final workshop focussed on priority use cases for AI in Drug R&D and Clinical Development. Some suggestions included Ambient Documentation for researchers, summarisation of content translation to ease recruitment, and the identification of novel outcome metrics. Shout out to whoever arranged the "AI in the NHS" session, because it was packed to overflowing. If anyone was lucky enough to find a seat, let me know how it went because I couldn't get in! After hours we had the customary networking events, including a brilliant get together from AlbionVC (thank you Molly Gilmartin!) where I finally got my chance to meet with Hello Self's Charles Wells, reconnect with AI legend and founder of Newton's Tree Haris Shuaib, Kelly Klifa & Alexandra (Sasha) T. from Ally Health, Samantha Fay from SiSU Health UK, and welcome back Dr. James Somauroo & Jessica Somauroo after their 5 week honeymoon. Given how hard they work, the absolutely deserved it. For all the clenched teeth and hair pulling that our industry can cause, today showed that the determination and drive to fix the problems we face are still there. HLTH really does know how to bring all the boys (and girls) to the yard! ALSO: Shout out to my former Babylon colleagues Nirvana Luckraj Darshak Sanghavi and Tom Breeze. The network remains *strong* #hltheurope #healthcare #innovation #digitalhealth #healthtech

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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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Bank holiday fun: This is a nice post from Graham Walker, MD describing a heuristic that can be used to expand the range of diagnoses a clinician might consider when reviewing a patient, and how it could be used as an LLM use case. Given the incredible flexibility of LLMs and the ease with which this can be tested, I've created a GPT (ChatGPT plus sub required) that allows you to try it out. https://lnkd.in/eBAWsfn5 If you do not have access to GPTs, you can find the prompt itself on github: https://lnkd.in/erdVqcFs DISCLAIMER: Do NOT use this in a clinical setting! It's just a fun demonstration of how role-based instruction prompting can deliver helpful results. I'd love to hear your feedback. #ArtificialIntelligence #DigitalHealth #GenerativeAI #GENAI #LLM #Prompting #PromptEngineering #ClinicalPromptEngineering #GPT #GPTs #ChatGPT

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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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A top down approach to tech adoption will never work in healthcare. While this approach may work in other industries where liability lies on the executive team, in healthcare liability more often rests with the clinician. At the end of the day we answer to the medical authorities which regulate our medical license. To be blunt, while painful its okay if I get fired because I do not adopt new technologies adopted by my institution. Its not okay and my entire medical career is in jeopardy if I do not follow the regulations set out by my provincial college of physician and surgeons. Part of which is to maintain the standard of care and follow a patient centric approach to healthcare. Technology adoption in healthcare requires a bottoms up approach to adoption. You need buy-in from clinicians and payors. Both are necessary and neither alone is sufficient. I realize I did not say you need buy-in from patients but I am assuming this is a given. #healthcare #technology #innovation #physician

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He fell in love with an AI chatbot; then built a $24M healthtech company from scratch. Now clinicians are champions of his product. (but he had 0 healthcare experience) Alex LeBrun, Nabla's CEO, shared a fascinating story with me: Years ago, he was captivated by an AI chatbot named Cybele. This "online dating" experience sparked his passion for AI. Here's how he did it with 0 prior experience and connections in healthcare: 1. Started his own clinic ↳ Invested heavily in creating a learning environment ↳ Operated in two countries to avoid cultural bias ↳ Observed real healthcare processes firsthand ↳ Gained credibility in the industry 2. Found the right partners ↳ Looked beyond conventional healthcare thinking ↳ Avoided those fixated on minor EMR improvements ↳ Built relationships with forward-thinking clinicians 3. Learned from your mistakes ↳ Acknowledged the high cost of building a clinic ↳ Used the experience to gather crucial initial data ↳ Leveraged it to build a strong clinician network 4. Focused on distribution, not just product ↳ Made the product accessible to individual doctors ↳ Let users become product champions 5. Made his product frictionless ↳ Created a simple Chrome extension ↳ Enabled instant trial without complex setup ↳ Designed for overwhelmed, time-poor doctors ↳ Prioritized ease of use over feature overload 6. Adapted the product to individual needs ↳ Recognized diverse documentation styles ↳ Allowed personalization down to user level 7. Built a community of champions ↳ Focused on delighting individual users ↳ Encouraged word-of-mouth promotion ↳ Leveraged doctors' influence in hospitals The key to disrupting healthcare as an outsider is to stay curious, adapt quickly, and solve real problems. PS: Are you an outsider with a bold idea for healthcare? Your fresh perspective might be exactly what the industry needs. Listen to the fascinating episode here: Apple Podcasts: https://hubs.la/Q02CS_300 Spotify Podcasts: https://hubs.la/Q02CTkn60 #CharmHealth #DigitalHealthDisruptors #HealthcareAI #ClinicalAI #AIInnovation #Nabla #AlexLebrun #HealthcareRevolution #Podcast #AIChampions #MedicalTechnology

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🚨 Google's Med-Gemini is shaking up healthcare AI! Forget GPT-4, this new model is crushing medical benchmarks. 🩺🧠 What got me excited... Beating Doctors (Respectfully): Med-Gemini outperforms human experts on tasks like summarizing medical information and simplifying complex diagnoses. This has huge potential for patient education and empowering healthcare providers. Tackling the USMLE: Setting a new high score (91.1%) on the MedQA-USMLE benchmark means this AI is packed with validated medical knowledge. It's Not Just Q&A: Med-Gemini is multi-modal, outperforming GPT-4 on tasks that involve images and various data types – just like the real world of medicine. The Big Picture: This is more than just cool tech. Med-Gemini could: Support Doctors: Help them process information faster and make more informed decisions, especially with complex cases. Empower Patients: Clear, reliable summaries of their conditions in easy-to-understand language is a gamechanger for patient empowerment. Transform Med Training: Imagine AI tools that help medical students grasp difficult concepts more easily. This is just the beginning. Google AI is consistently pushing boundaries in healthcare, as seen with AMIE earlier this year. The future of medicine is data-driven, with AI acting as a powerful assistant! Question for you: Where do you see the biggest potential for Med-Gemini to improve healthcare? #HealthcareInnovation #AIinMedicine #MedGemini #GoogleAI #FutureofHealth Research Paper: https://lnkd.in/dMEAnfXh Video Credit: TheAIGRID

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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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“I think it’s important that the person running the organization understands the core thing that the company does.” - Ben Horowitz, GP at a16z. Totally agree, but we actually see the opposite in healthcare. As care delivery businesses are mostly run by people who have no prior knowledge of how to deliver care. The ACA actually prohibits doctors from even owning hospitals. Which completely enables MBAs and other non-clinical administrators to influence how patients receive care. Call me old fashioned but shouldn’t the people trained and licensed to provide care delivery services be the ones running businesses where care services are delivered? 🤷🏽‍♂️ —— Hi, I'm Dr. Mario Amaro, Founder and CEO Ease and I talk about all things starting, running, and growing a private practice with AI. Follow me, if you are planning to start one or are currently figuring out creative ways to grow one. #PrivatePractice #SelfEmployment #HealthcareOnLinkedin #HeyEase #IndependentTogether

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I’m a lucky man - I get to talk about stuff I care about with people that feel the same way, but know how to approach it in fresh ways. This chat with Josh Au Yeung goes into another area I think needs much more attention - leadership. NHS providers recently published data showing only around a third of NHS CEOs have a clinical background: https://lnkd.in/givrbU_p . When evidence suggests that expert leadership is strongly associated with better outcomes, happier staff, and greater productivity, we need to work harder to identify, train, and support current and future clinical leaders. The burning question is this: when it comes to #HealthTech , who is the expert leader? A technologist? A clinician? A hybrid? Would love to hear your thoughts on this (and the podcast, naturally)

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Here's another fundraising roundup spotted in the healthcare wilds 👀 🍃 ✅ Sword Health raised $130M (new valuation: $3B). ✅ Better Health raised $14M to deploy expanded payer partnerships. ✅ Alzheon, Inc. | Preserving Future Memories raised $100M Series E to advance development & commercialization of its Alzheimer’s treatment. ✅ CinRx Pharma raised $73M to continue its ‘Hub and Spoke’ model of startup formation. ✅ Eko Health raised $41M to scale AI-driven heart and lung disease detection. ✅ Anterior (formerly Co:Helm) secured a $20M Series A to unlock admin efficiencies for healthcare payers. ✅ SamaCare raised a $17M Series B to scale specialty drug prior authorization platform. ✅ Plenful secured a $17M Series A to streamline pharmacy operations with AI-powered workflow automation. ✅ Grayce raised a $10.4M Series A led by Maveron amid a caregiving crisis. ✅ Stitch Employment Services Inc PEO raised $8.75M in seed to support independent medical practices. ✅ Posterity Health secured $6M to fund expansion and technology acceleration. ✅ Sware raised $6M to accelerate life sciences innovation by automating FDA-mandated software validation. ✅ Keragon raised $3M, as announced in @Conno Christou’s LinkedIn post. As always, if I missed you, let me know! Each week I'm spotlighting an innovative healthcare startup. I just ask for a bit more transparency. All submissions are confidential; submit yours here 👇 https://lnkd.in/gF4DrAvJ Looking for more fundraising roundups, alongside healthcare industry news and analysis? Subscribe to Hospitalogy, my 2x/week newsletter. Join 33K+ other investors, executives, and healthcare professionals: https://lnkd.in/g-nfRqW6

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AI has made good design more important than ever in healthcare. Why? Model performance alone doesn't guarantee clinical or financial value. A model can deliver a robust insight/output (accurate, reliable, complete, etc.), but clinician-users may struggle to use or integrate it in a real-world setting. As healthcare AI systems become compound machines, these design challenges amplify. Here are two simple examples: • AI systems that do not integrate seamlessly into EHRs, PACS, or pathology viewers may drive differential rates of utilization/adoption across sites, privileging some patients to a given algorithm without an underlying clinical rationale. • Imagine a model recommending a "next best step in care" that is not provided at a given hospital (e.g., due to lacking resources or infrastructure). Contextual and human factors can have a greater influence on whether the value of an AI intervention is realized, even when in silico model performance is compelling. There are two near-term opportunities here: 1) Healthcare AI model benchmarking should encompass "end-to-end systems," capturing nuances around system design, user experience, workflow integration, and ease of adoption. 2) It's time for AI-first design in healthcare – made possible by revisiting the unique capabilities of AI systems and their emergent behaviors to interrogate what new user experiences are possible. What are your favorite examples of AI-first design? #healthcare #ai #artificialintelligence #generativeai

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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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Last week, I had an eye-opening conversation with a major 500-provider+ healthcare system. What I discovered is nothing short of revolutionary. Here's how they're thinking about unleashing the power of AI: 1. Transforming Patient Care Delivery: AI-driven tools are creating personalized treatment plans for that are more effective and timely. 2. Supercharging Operational Efficiency: From predictive maintenance of medical equipment to smarter staff scheduling, AI is slashing their staffing inefficiencies. 3. Revolutionizing Clinical Documentation: Natural language processing and AI is automating documentation, letting healthcare professionals focus more on their patients and less on their paperwork. You can test our AI here for free here: 👇 https://lnkd.in/e_PxQ6bM 4. Slashing Administrative Overhead: Administrative tasks such as appeals management are now in the hands of companies like Crosby Health, freeing up valuable time for their overburdened healthcare staff. 5. Boosting Collaborative Intelligence: AI is enhancing collaboration among their healthcare teams, ensuring all healthcare staff is on the same page with the latest data and insights. AI is not only elevating the quality of care but also ensuring healthcare organizations operate like a well-oiled machine.

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I came across an area where there's a whitespace for AI in healthcare. I'm often asked this question by VCs and entrepreneurs, and recently was introduced to a compelling opportunity: Quality Reporting In my recent conversation with M. Ragan Hart of MultiCare Capital Partners, she flagged this as a category where there aren’t many good options yet. Take clinical registries as an example. MultiCare employs highly skilled specialty-focused nurses to translate unstructured data from EHR-based clinical notes into clinical registry reporting documentation. This is tedious, time-consuming work, conducted by experienced and well-trained (not to mention expensive) clinical team members. This is a significant opportunity. Demonstrating success in this low-risk yet culturally important workflow can create the organizational trust a gen AI-driven vendor needs to get buy-in for higher-value / higher-risk use cases. What do you think? Is quality reporting a compelling opportunity for AI? What are the other underexplored areas for gen AI in healthcare? Graham Walker, MD, Spencer Dorn, Anuradhika A., Matthew Sakumoto, Morgan Jeffries, Lynn Simon, Adam Carewe MD, Anil Saldanha

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Lots of great candidates are looking for their next healthcare startup role right now, after many rounds of layoffs in the industry. For clinicians and researchers looking for your next role, deciding whether to join a company's advisory board, or considering a collaboration with a startup, it's crucial to understand business viability. And, it's simpler than you think. Here are four essential questions to ask leaders at any new company you're getting to know. Scroll through the images to get more details about each question, and examples of 🚫 and ✅ answers to listen for: 1️⃣ What problem does your company solve? 2️⃣ Who is your primary customer? 3️⃣ What have you learned about market demand for your solution? 4️⃣ What will be your biggest challenges in the market? 💡Top Tip: Listen for clarity, precision, and a healthy dose of pragmatism. And remember, clinical or technological innovation alone is not a business model. Vague or unsophisticated answers to these simple questions are a big red flag that a startup will struggle, and a likely indicator that the company will feel unfocused and lack direction. -- I'm Clare Kennedy Purvis, Psy.D. I host a free, online community for women clinicians and scientists, WELL, and we offer a mini-MBA program for industry leaders in our community. Learn more and join us at www.well-women.org.

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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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🎙️A wide ranging discussion in the The Economist babbage podcast covering a wide range of healthcare benefits and concerns in healthcare artificial intelligence dealing with types of models including neural networks, generative AI, validation, monitoring and medical device regulation led by Alok Jha and Natasha Loder with contributions from myself, Pranav Rajpurkar, Peter Kecskemethy and the inimitable Dr Hugh Harvey I've always wondered how to pronounce Peter's surname, I think that's been nailed here 😊 #AI #healthcare #generativeAI https://lnkd.in/eJ__H_e6

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