Indu Navar

Great insights from David Bell, Ph.D. on the challenges of #AI in #healthcare documentation. The complexity and nuance unique to each healthcare market and the fact healthcare typically has few rote tasks but a lot of paperwork, makes it critical that #AItools be developed by experts in the industry with deep understanding of the most appropriate problems for AI enhanced tools to solve.

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AI Scenarios - 3 Positive Futures + 12 Predictions from legendary VC, Vinod Khosla. Scenario ➊ Equitable & Advanced Healthcare and Education With AI enabling free access to expertise (#1), healthcare and education become universally accessible. AI-driven primary care ensures early diagnosis and proactive health management (#6), reducing disease incidence and increasing life expectancy. Free, high-quality education democratizes learning opportunities, fostering a more knowledgeable and skilled global population. This results in a healthier, more educated society capable of addressing complex global challenges. Scenario ➋ A Greener & More Meaningful Future Robots performing most labor (#2) and AI-driven automation reduce human drudgery, allowing people to engage in more meaningful and creative pursuits (#4). Advances in protein production and environmentally friendly fertilizers (#7) lead to sustainable agriculture, reducing the ecological footprint and ensuring food security. Clean energy solutions like fusion power and advanced geothermal (#10) drastically cut carbon emissions, addressing climate change and ensuring a sustainable future. Scenario ➌ Enhanced Mobility & Resource Efficiency Advanced public transit systems replace private cars in cities (#8), reducing traffic congestion, pollution, and urban sprawl. High-speed travel innovations (#9) make global travel more efficient, fostering international collaboration and cultural exchange. New technologies for resource extraction (#11) ensure efficient utilization of critical materials like lithium and copper, supporting sustainable technological advancements. Vinod’s 12 Predictions: #1 Most expertise enabled by AI will be free #2 Most labor will become free with robots performing more work than the entire human labor force #3 Programming will be near free, and computing will become as ubiquitous and invisible as electricity #4 The level of creativity in music, entertainment, and design will dramatically increase with the help of AI #5 Internet access will mostly be by intelligent agents performing tasks for humans #6 Medicine will shift from reactive sick care to proactive healthcare, with early detection and prevention of diseases becoming standard practice. #7 We will have new food and fertilizer.  #8 Cars could be displaced in cities. #9 Flying will be faster.  #10 There will be clean, dispatchable electric power. Fusion power and advanced geothermal energy will replace coal and natural gas plants, providing clean and sustainable energy solutions. #11 New technologies will allow us to access and utilize resources like lithium and copper more efficiently, mitigating concerns about resource scarcity. #12 Entrepreneurs will develop carbon solutions for all major emitter categories, significantly advancing efforts to combat climate change. https://lnkd.in/gphmDnH3 #AIFuture #TechOptimism #AIForGood

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M13 is thrilled to lead the Series A in RadiantGraph which is focused on personalization in healthcare. As we continue to invest in early stage companies it is abundantly clear that the biggest indicator of success is the founder and his or her ability to execute on their vision of the future. In this case, I don't think I have seen much better founder-market fit than we have with founder and CEO Anmol Madan. We believe in his vision: in order to have the best patient outcomes in healthcare we need increased personalization and AI is the breakthrough to get us there. Anmol started one of the pioneering digital health companies Ginger and then was Chief Data Scientist at Livongo and Teledoc. He has done it all: early stage, late stage, public company. And he deeply understands the nuances of the healthcare market from all angles which is critical for success. While there are many players building healthcare-specific AI capabilities, RadiantGraph is focused on outcomes: The platform is already processing personalization models for more than 3.5 million people, despite launching just last year. Excited to partner with my friend Adam D'Augelli and the great True Ventures again and Morgan Blumberg who has been deep in the healthcare market with me for many years. More on our thesis in the blog below. https://lnkd.in/evDDmC3W

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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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There appears to be a "Superstar Effect" in healthcare and biomedical AI research right now. A small percentage of publications are truly novel methods contributions that introduce new capabilities or behaviors; whereas a majority of contemporary research publications are focused on implementation science to assess the real-world utility and performance of new methods. This dynamic seems different from prior eras, when the translational infrastructure for implementing novel AI methods was largely unavailable. While methods research often captures more attention and accolades, both types of research are critical – novel methods without implementation science lacks translatability; implementation science without novel methods lacks novelty. If our goal is to invent and deploy transformative AI methods in real-world healthcare and life sciences contexts, we ought to continue encouraging and rewarding stellar implementation science work alongside breakthrough methods development.

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HCPCS and healthcare policies, and Claude.ai Today, while diving deep into the newly dropped HCPCS file with its historical data, I noticed something intriguing: distinct patterns in code additions across different time periods. I saw clusters of psychiatry codes in 2012-2014, DME codes around 2002, and, recently, a surge in drug-related codes. Curious about these patterns, I turned to Claude (an AI assistant by Anthropic) for insights. The response was enlightening - these weren't random clusters but rather footprints of major healthcare policy changes! What I learned: - The 2010-2014 psychiatry code surge? It aligned perfectly with the Mental Health Parity Act and ACA implementation - The 2002 DME code expansion? Connected to Medicare Modernization Act (MMA) which included major DME reforms - Today's increase in drug codes? Reflects our boom in specialty pharmaceuticals and gene therapies It's fascinating how something as technical and administrative as coding systems can tell such a rich story about healthcare evolution. Sometimes the patterns we see in data are like whispers from the past, telling us about pivotal moments in healthcare policy. This experience reminded me that there are no random patterns in healthcare data - just stories waiting to be understood. #HealthcareAnalytics #DataDiscovery #HealthcarePolicy #AI #HCPCS #Healthcare #DataStorytelling Has AI helped you uncover any interesting patterns in your work? Would love to hear your stories! The chart below shows the number of HCPCS codes that were added over the years.

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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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Arvind Narayanan and Sayash Kapoor's book AI Snake Oil reminded me of Epic's epic fail in sepsis prediction, but also renews the question of why health systems continue to believe the 'Epic will do it' mantra. The fact that it didn't work is fine. A lot of things don't work out of the gate. Sepsis is a leading cause of death and this was a starting point for many data science efforts. The fact that Epic was claiming high efficacy of the model when in fact it was barely better than random guesses is certainly more problematic (and we owe some gratitude to UMich for doing the validation work). What's even more unforgiveable, though, was Epic using financial incentives to drive adoption of a useless and potentially harmful prediction model. Why harmful? Well if it's not much better than random guesses then it's likely some patients were miscategorized. Did Epic know and make false claims? Or were they incompetent in their evaluations? Neither is great. This behavior would normally erode trust. But the 'Epic will do it' argument continues, as Epic continues to consume so much of IS budgets while health systems are essentially held hostage to a vendor that may do some things well, but not most things. Addressing both financial and outcome challenges in health systems will require a more informed approach to identifying best in class and a rejection of the 'Epic will do it' mindset. That's still a big hill to climb.

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On the latest Vital Signs, Nikhil Krishnan and I had a ton of fun chatting with Dr. Atul Butte. Dr. Butte is a professor at UCSF, Chief Data Scientist of the UC Health System, and a co-founder of numerous healthcare startups. In our conversation, Dr. Butte shared his takes on healthcare AI opportunities, challenges, hype, and more. A few highlights: ⚡LLM use cases, data, and growth LLMs are already helping to solve the “drudgery” aspects of medicine, such as automatically drafting SOAP notes and patient messages. Dr. Butte is excited about future applications of LLMs that can help with the “cognitive” aspects: with difficult medical problems an LLM might be able to stitch together disparate pieces of information to generate a potentially novel solution. He also highlights that prompt engineering will become the next “coding language” as so much of leveraging LLMs is knowing how to ask them the right questions. Additionally, Dr. Butte discusses how since 2016, UCSF has been working to deidentify its patient notes so that it can query them to answer clinical questions and ultimately enhance patient care. ⚡The need for patient-facing AI As patients are increasingly able to access their own personal health data in nicely structured formats (e.g., EHRs, wearables), they desperately need more tools to help them interpret and make decisions using that data. Dr. Butte understands that these patient-facing solutions are challenging to get funded but he emphasizes that patients need to receive the benefits of AI sooner. ⚡Evaluating healthcare AI solutions In order to evaluate AI solutions, we often put human specialists – such as medical professionals – on the same tasks. Understanding that specialists often disagree with each other, a significant challenge arises in figuring out how we can evaluate AI considering we can’t just “average” all the specialist opinions. Dr. Butte points out that there still remains a massive gap in AI evaluation tools. Additionally, there are so many organizations that want to have a say in how healthcare AI is regulated that the landscape is uncertain. An awesome conversation with an expert on all things AI meets healthcare. Listen to the full episode below: Spotify: https://spoti.fi/3Wr3643 Apple: https://apple.co/4acwgan

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AI in healthcare isn't just a trend - it's a necessity we're building at Innovaccer. At the Dubai AI & Web3 Festival, I'll be sharing our real-world experiences: the breakthroughs, the setbacks, and the ethical dilemmas we face as the tech industry. The truth is, implementing AI in healthcare is complex. We've seen it dramatically improve patient outcomes, but we've also encountered challenges with data privacy and equitable access. I'm looking forward to honest discussions about these issues. If you're at the festival, let's cut through the hype together. I'm eager to hear about your experiences - good and bad - with AI in your field. What's working? What isn't? What keeps you up at night? No sales pitch, no silver bullets - just real talk about the future of AI in healthcare and beyond. Hope to see you there. #TechinInnovation #Innovaccer #AI #PowerofAI #FutureTech #Healthcare #Healthtech

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Don’t mess with the Aunties! Great discussion at The Tech Aunties Podcast -Thank you again! Autonomize AI is leading this transformation of healthcare operations with #AI and we can’t wait to share more stories of how AI can lower the cost of care, improve the quality of care and transform the patient experience! Every industry, and especially healthcare has the opportunity to completely reimagine its business models with AI.

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#FoundersX@TECH WEEK by a16z The Healthcare panel hosted by our partner Leo (Naiwen) Cui, Ph.D., with Vijay Vaswani, co-founder & CEO of Omniscope, sharing his insights on decoding and indexing our immune system with high throughput single-cell TCR sequencing technology, is highly informative. What Vijay and Omniscope team is building with their ImmuneGPT will help transform our healthcare from current sickcare model to true healthcare with preventive power. US healthcare cost is 18% GDP and the current model is not sustainable. Omniscope brings us hope by turning our immune cells into most powerful medicines to safeguard our health. Go Omniscope! Please save our healthcare system! 😎 FoundersX Ventures Omniscope Tom Kosnik Leo (Naiwen) Cui, Ph.D. Sizi Chen Yvonne Jiang Andi White, PhD Holger Heyn Lynnette Ang Matthew S. Stanford University Graduate School of Business Stanford Directors'​ College Harvard Business School University of California, Berkeley, Haas School of Business #AI #GenAI #AIagents #startup #techbio #venturecapital #healthcare #ImmuneGPT

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In case you missed it, last week the Coalition for Health AI (CHAI) released a draft consensus framework for responsible health AI. Developed with input from over 100 contributors representing a diverse network of healthcare stakeholders, this guide proposes actionable evaluation criteria throughout the AI lifecycle—from identifying use cases to deployment and monitoring. Key examples outlined in the guide include: • Predictive EHR Risk Use Case (Pediatric Asthma Exacerbation) • Imaging Diagnostic Use Case (Mammography) • Generative AI Use Case (EHR Query and Extraction) • Claims-Based Outpatient Use Case (Care Management) • Clinical Ops & Administration Use Case (Prior Authorization with Medical Coding) • Genomics Use Case (Precision Oncology with Genomic Markers) The draft framework is open for public review and comment for the next sixty days. Please use this form to submit feedback: https://lnkd.in/eYughFYC Kudos to the CHAI team led by CEO & President, Dr. Brian Anderson, MD, on this milestone. Eric Horvitz Jennifer Goldsack John Halamka, M.D., M.S. Michael Pencina Micky Tripathi Nigam Shah Suchi Saria Troy Tazbaz #healthcare #ai #artificialintelligence #generativeai https://lnkd.in/e7VRv6fD

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India’s entrepreneurial ecosystem is thriving, offering vast, untapped potential. Unlike China, where state control has tightened, India provides a more supportive environment for startups. Here are some key points: ✅ 1. Growing Startup Ecosystem India’s startup ecosystem is the third-largest globally, with over 100 unicorns valued at around $340 billion. The surge in new businesses is driven by a young population, increasing internet use, and supportive government policies. ✅ 2. Untapped Markets The “Bharat” segment, referring to the population outside major cities, presents significant opportunities. Startups are targeting this segment with affordable products and services, bridging the digital divide between urban and rural areas. ✅ 3. Deep Science and Technology India’s deep science tech ecosystem is ripe for investment. Innovations in biotechnology, renewable energy, and advanced manufacturing are set to make global impacts. 📶 Recent Examples ✅ Zepto: Offers 10-minute grocery delivery, quickly becoming a unicorn. Jio Financial Services: Partnered with BlackRock to revolutionize wealth management in India. ✅ Semiconductor Startups: AGNIT Semiconductors and Aura Semiconductor are leading the industry. ✅ Dharaksha Ecosolutions: Converts crop residue into biodegradable packaging. ✅ Digital Payments and Agri-tech: Paytm and Ninjacart are transforming digital transactions and agricultural supply chains. India’s startup ecosystem is not just about financial growth but also societal progress. With the right investments, India is poised to become a global leader in innovation and entrepreneurship. if you are looking for corporate venture strategies or you are a startup or innovation looking for corporate ventures funding in India. Feel free to connect. [email protected]

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🤫A small secret: We will probably be announcing the first open-source implementation of FHIRConnect - a specification for bi-directional openEHR to FHIR mapping that was initially proposed by Better. So far there has been no open-source engine that was able to consume FHIRConnect mappings. Gasper Andrejc started openFHIR in his spare time, and announced it this year to be an enterprise grade openEHR-FHIR engine that is compliant with the FHIRConnect specification. In this very year, HiGHmed under the guidance of Severin Kohler, Stefan Becker and Dirk Roßbach strengthened the partnership with Medblocks to develop an openEHR-FHIR mapping engine based on FHIRConnect. As a part of this effort, Medblocks has acquired the openFHIR codebase from Gasper Andrejc. (Why do the same work twice? Right?) We’re all now working really hard to make this production ready by testing it with the MII FHIR Profiles. Severin Kohler is hard at work enhancing the FHIRConnect spec to support multiple edge cases, and Gasper Andrejc is also hard at work making the openFHIR engine work with these requirements! Bostjan Lah from Better and Jake Smolka has also contributed to these efforts by sharing the learning’s from their own implementation of the FHIRConnect engine. There’s still a long way to go here, but this project is exciting from multiple angles. An existence of an engine that can handle bidirectional transforms from FHIR to openEHR is needed out in the open before archetype and template level mappings for different FHIR profiles can be authored and shared. I love that the community has put together a lot of resources into this problem, and if we have good results with this new enjone, it’ll probably be due to years of work that came before it. We will be open sourcing the engine in January next year - before I step on stage to talk at the HiGHmed openEHR symposium! If you’d like to help in some way, or have a need for openEHR-FHIR mappings, do let me know in the comments below 👇 (or DM me) If you want to meet up and say hi in person, I’ll see you in Berlin!

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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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Had a fascinating discussion with Vinod Khosla, the visionary founder of Sun Microsystems and Khosla Ventures. His insights on entrepreneurship and innovation were truly inspiring. Grateful for the opportunity to learn from a true pioneer in both tech and healthcare industries. Having closely followed Vinod's insights, I've distilled his top 10 timeless pieces of advice for entrepreneurs: 1. Embrace Failure: He advises entrepreneurs to see failure not as the end, but as a stepping stone to success. Learning from mistakes is crucial for growth. 2. Think Big: Vinod encourages entrepreneurs to think beyond the status quo and aim for ambitious goals. Bold visions drive innovation and transformation. 3. Focus on Impact: He emphasizes the importance of creating products and solutions that have a meaningful impact on people's lives and society as a whole. 4. Iterate Rapidly: Vinod advocates for a culture of rapid iteration and experimentation. Iterate quickly, learn fast, and adapt to changing market dynamics. 5. Stay Resilient: Entrepreneurship is a rollercoaster ride, and Khosla stresses the need for resilience. Persevere through challenges and setbacks to ultimately achieve success. 6. Surround Yourself with Great Talent: Building a strong team is paramount. Khosla advises entrepreneurs to surround themselves with talented individuals who complement their skills and share their passion. 7. Stay Customer-Centric: Always keep the customer at the forefront of decision-making. Understanding their needs and pain points is essential for creating valuable solutions. 8. Embrace Disruption: He encourages entrepreneurs to disrupt existing industries and challenge conventional wisdom. Disruption creates opportunities for innovation and growth. 9. Be Adaptable: In today's fast-paced world, adaptability is key. Vinod advises entrepreneurs to remain agile and flexible in response to market changes and emerging trends. 10. Never Stop Learning: Continuous learning is essential for staying ahead in the ever-evolving business landscape. Vinod encourages entrepreneurs to be curious, seek knowledge, and never stop growing. Thank you Vinod for sharing your wisdom! #VinodKhosla Vinod Khosla #TechInnovation #technology #healthcare #VC #investment #MachineLearning #deeplearning #Entrepreneurship American Association for Precision Medicine (AAPM) #aapmhealth #aapm_health #artificialintellience #ai #news

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Platforms are in, point solutions are out as the healthcare AI category leaders come into focus this year. In conversation with Shelby Livingston at Endpoints News, I shared: 1. Why AI-first companies have the greatest platform potential and heuristics for identifying these opportunities at the earliest stages, 2. How companies are differentiating through co-development and distribution partnerships with leading healthcare institutions, and 3. Strategies for navigating anemic budgets and constrained market sizes through business model innovation such as AI-enabled services and pay for performance models Read more here: https://lnkd.in/dJWqv_zC Bessemer Venture Partners Abridge #healthcare #lifesciences #AI #generativeAI

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Unmet social needs could increase risk of costly surgical complications by 2-3x in some circumstances. Assessing and addressing SDOH factors is thus becoming mandatory under a variety of VBC payment models and episodic bundled payments. RevelAi Health's use of LLM-powered voice AI for specialty care management, social needs screening and closed loop social referrals with integration with Findhelp is delightfully seamless and groundbreaking at the same time - with far reaching applications. Very proud to share this simple demo and contribute to this work pioneered by Christian Pean M.D., M.S. and Hadi Javeed.

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