Mahesh Venkataramani

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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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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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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Achieving #blockbuster status requires going beyond #clinicaltrial success. Real-world factors like patient acceptance, #prescriberbehavior and integration in healthcare systems are crucial. Implementation science can help identify and overcome these barriers, boosting product value. #realworldevidence #rwd #pharma #valuestrategy #healthcare #access #marketaccess #drugdevelopment #biopharma #heor #healtheconomics #medicalaffairs #commercial #reimbursement #evidence #rwe #hcp Becaris Publishing Limited The Evidence Base®

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Building the largest network of Payer Patient Access API FHIR servers has not been easy. Our November update to our network report is out! Flexpa has worked tirelessly with top vendors like Health Samurai | Aidbox FHIR® Platform and forward-thinking payers like Maryland Physicians Care to test and evaluate hundreds of CMS Payer Patient Access API implementations. More than anyone else. Our latest report covers the significant progress by vendors and payers since our last update, regulatory changes ahead, and opportunities for growth. 🚀 Improved API Implementations: Significant progress in Patient Access APIs through better collaboration between payers and vendors, making data access smoother. ⚖️ Regulatory Changes Ahead: New regs like CMS-0057-F and governance frameworks like TEFCA will bring more complexity on patient consent and payer data exchange. The CMS 9115 APIs have lessons here. 💡 Opportunities for Growth: There are still gaps in coverage and authorization workflows, presenting key opportunities for improving the Patient Access API ecosystem, and the opportunity of Individual Access Services.

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31 lines of code. That is all it takes with the Canvas SDK to modify the order of medications AND add an annotation to highlight a preferred medication to providers. 31 lines of code and about 2 minutes. Check out this step by step put together by Andrew Duane - https://lnkd.in/gV3YGdEF

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As a leader, managing burnout is one of the most important things you can do for your team to ensure they are able to show up and be successful in all facets of their lives. You have the power to make the workplace more human and a place where people thrive. Come join me in a discussion on managing and mitigating burnout and learn more May 9 at 10 AM PT. RSVP here https://lu.ma/mqlv984g

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5 ways Amazon is using AI to help transform health care An insightful read by Amazon's Chief Medical Officer Sunita Mishra MD, MBA on how the company is investing in AI to improve the health care industry for patients and providers. They focus on following topics: 1. Empowering more efficient, connected health visits using generative AI to automatically create clinical notes from doctor-patient conversations. 2. Making filling and processing prescriptions easier 3. Providing accurate prescription medications 4. Reaching more people through strategic partnerships 5. Identifying new medications faster This is an interesting one ! Bringing a new drug to market is time and cost intensive, taking an average of 10 to 15 years. One way Amazon Web Services (AWS) helps life sciences organizations accelerate this process is by providing researchers with access to the latest #generativeAI advancements so they can find the answers they need faster. Recently, AWS announced a collaboration with EvolutionaryScale, a frontier AI research lab dedicated to advancing biological research and life sciences. With this collaboration, EvolutionaryScale’s ESM3, a frontier, state-of-the-art language model family, will be made available on AWS, enabling researchers and scientists to create entirely new proteins that have not existed in nature, potentially reducing the cost and time of bringing new therapeutics to market by years and millions of dollars. Here is teh read: https://lnkd.in/efxsaig6 #drugdiscovery #aiihealthcare #genAI #patientexperiene #digitalhealth

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24,000 Doctors, 40 Hospitals, 1 AI: Kaiser's Bold Move to Revolutionize Patient Care Kaiser Permanente has just made a groundbreaking move in healthcare technology. Here's why this partnership with Abridge is turning heads: 🔹 Massive Rollout: Abridge's AI-powered medical note-taking app will be available to over 24,000 doctors across 40 hospitals and 600+ medical offices. 🔹 Efficiency Boost: The technology aims to reduce administrative tasks, allowing doctors to focus more on patient care. 🔹 Cutting-Edge Tech: Abridge's AI converts patient-clinician conversations into structured clinical notes in real-time, integrating seamlessly with EHRs. 🔹 Privacy First: The implementation includes rigorous testing and quality checks, with patient consent required. 🔹 Multilingual Capability: The tool is available in 14+ languages and over 50 specialties. 🔹 Industry Trend: This partnership signifies a growing adoption of AI in clinical documentation across major health systems. Key Takeaway: Kaiser Permanente's large-scale adoption of Abridge's AI technology could set a new standard for how healthcare systems leverage AI to improve patient care and clinician efficiency. #AIinHealthcare #HealthTech #FutureOfMedicine #HealthcareInnovation

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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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🔄 The "Cattle vs Pets" revolution changed how we managed servers. Now, get ready for the next big shift - the same could happen to CODE itself! 🐈 Today, we treat our code like pets: • Carefully crafted functions & classes • Lovingly maintained codebases • Hours spent "healing" broken code • Years of accumulated tech debt 🐄 Tomorrow, code will be like cattle: • Generated on-demand • Used once, then disposed • No maintenance needed • Zero tech debt accumulation 🚀 What enables this transformation? • Advanced AI & LLMs • Better specification languages • Automated testing & validation • Enhanced code generation 💡 Think about it: Just as DevOps engineers shifted from nursing individual servers to managing fleets, developers will move from writing code to architecting solutions. 🔮 The future developer: • Describes outcomes, not implementations • Focuses on system design • Treats code as a disposable resource • Architects solutions at a higher level ⚠️ The road ahead isn't smooth: • Ensuring consistency across generated solutions • Managing complex business logic • Debugging transient code • Building trust in generated solutions • Handling state & persistence But remember - these are the same doubts we had about disposable servers a decade ago! 🤔 Ready for this shift? The first wave is already hitting data processing and API integration spaces. Your code isn't your pet. It's time to let go. #FutureOfCoding #SoftwareEngineering #AI #TechTrends #Innovation #Programming #GenerativeAI #AgenticAI 💭 Share your thoughts below! 👍 Like if this resonated 🔁 Repost to spread the word

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For the success of AI in healthcare - the key factors are Privacy/Safety of patient data, Accuracy of AI in the clinical domain and most importantly ability of AI to assist and save time of clinicians in time intensive data collation/analysis tasks. At Cardea Health Inc. , we put these principles into action by creating super accurate clinical domain trained large AI models, ability to run this AI on the Edge/ VPC guaranteeing safety and privacy and turbo-charging clinicians with AI analysis to accelerate clinical trials. To learn more, please listen to my talk at the recent Edge AI conference. https://lnkd.in/gfj63cSv

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Reduction in the treatment cost would fuel adoption of AI & High-tech. Especially the for notifiable diseases, with better coverage with mass scale screening programs would get more fund allocation, leading to faster eradication of long standing diseases like TB.    Anti-TB drug: Half the dosage works, Malathy Iyer / Sep 10, 2024 Ahead of the rollout of the shortest treatment for drug-resistant TB, called BPaL, Indian doctors published a study proving that halving the dose of one of the three drugs in the six-month treatment worked just as well. The BPaL regimen includes bedaquiline, pretomanid, and linezolid, with/without moxifloxacin (BPaLM). It's already prescribed in 40 countries with a daily dosage of antibiotic linezolid at 1,200mg.   However, a three-year-long Indian clinical trial, which preceded the Indian govt's approval, found Indian patients doing well with a daily dose of 600mg. Terming it a "revolutionary outcome", Dr Vikas Oswal, who led the Mumbai leg of the trial that began in Aug 2021, said, "Lower dosages of linezolid are also as effective as high dosage, which was 1,200mg in the global Zenix trial. The Indian study proves that 600mg to 300mg is also as effective as 1,200mg." The study, published in ‘Clinical Infectious Diseases' on Monday, provides the first look at BPaL's India trial led by ICMR's National Institute for Research in Tuberculosis in Chennai. Of the 403 patients with drug-resistant TB enrolled in the trial, 352 were cured while one patient with COPD died. Eleven patients had a recurrence while 19 others had serious adverse reactions in the 12-month follow-up. "In conclusion, our results show that 600mg of linezolid daily for a limited period (9–13 weeks) followed by structured dose reduction to 300mg for the rest of the treatment period, in combination with bedaquiline and pretomanid, is non-inferior to the standard 600mg linezolid dose for 26 weeks,'' the study concluded.  Read the full article: https://lnkd.in/dmJRvPXP #AI #ArtificialIntelligence #DeepLearning #genAI #healthcare #technology #COPD #TB #tuberculosis #NTEP (national TB elimination programme) India Ministry of Health and Family Welfare, Government of India

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I caught up with Nikhil Krishnan of Out-Of-Pocket fame to get his take on the current state of the healthcare technology landscape. Up first: his current thoughts on what's exciting and where there are opportunities for AI-driven solutions. First half of our Q&A below 👇 — 🟩 Q: When it comes to AI, what area are you most excited about? Patient-facing solutions. The AI admin and back-office space has quickly become saturated, and incumbents will always have a distribution advantage here. Which is why I’m actually surprised there aren’t more players entering the patient-facing side. I think the traditional healthcare companies are too nervous to actually go into that patient-facing area. And it's also a regulatory gray area, so if you actually get traction, you can probably help shape how this stuff ultimately gets regulated. So right now, I think the greatest opportunities are for patient-facing AI tools. 🟩 Q: Where specifically do you see whitespace to develop patient-facing solutions? I think there’s still a lot of opportunity on the patient-facing automation side. A great example actually is automated lab results. There has been an issue for the last 3 years that lab results automatically get released to patients, and they see something's abnormal. The patient is trying to figure out what’s going on and what they've read. Part of this is explaining the result, which is something Elaborate is working on, and in some cases, actually, it's really useful for the patient to get that notice immediately with follow up steps, like scheduling a diagnostic. Another area is AI that can autonomously make workflow changes within a local system. Viz.ai is working on this automated workflow piece in the context of situations like stroke care coordination and hypertrophic cardiomyopathy. This requires not only automation in terms of being able to understand the context of multiple datasets, but also make the requisite workflow changes (e.g. escalating to the right specialist). These are particularly useful in time-sensitive areas. 🟩 Q: Any other AI whitespaces? Monitoring of AI models. It's one of the areas that's still unknown in terms of how do you give peace of mind to your buyer? It becomes really hard to actually evaluate and audit a lot of these AI algorithms without some form of data exhaust. I feel like one of the things that will happen is you'll see some form of certifying entity emerge that can give a gold standard to hospitals. 🟩 Q: What else are you thinking about as it pertains to AI? I do think that pricing is one of the more interesting components here. For example, in AI scribing, how you think about pricing changes a lot depending on if you’ve got a human in the loop or not. Because adding a human layer goes from near zero marginal cost to cost scaling linearly. One of the ways that companies will be able to differentiate themselves — especially from incumbents — is the pricing model.

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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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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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AI technologies and more specifically Large Language Models (LLMs) like ChatGPT are the divas of clinical tech talk. While important, they are only part of a broad spectrum of emerging technologies poised to transform clinical care and doctor-patient relationships. These include #wearables and patient #monitors based on advanced physiological sensors that are similarly changing healthcare and fitness landscapes by synergistically exploiting AI to advance physician-centered patient specific care. Our research and use-case investigations suggest that advanced monitors, including wearables, can help counter AI intrusions into doctor-patient relationships. Specifically, monitoring augments subjective patient symptoms with quantitative physiological data that arm doctors with near realtime measurements, trends, and contextual insights. They also help patients by making symptoms and asymptomatic conditions easier to understand and visualize. Patients in this paradigm become collaborating partners instead passive passengers on the healthcare-AI express. What about AI? The best monitoring systems have been using AI technologies including machine learning and deep learning to improve measurement accuracy, precision, and quality. LLMs like ChatGPT performed best in our evaluations by exploiting post-training monitoring data and research to produce more up to date and insightful responses. We frequently use Retrieval Augmented Generation (RAG) techniques to make ChatGPT aware and direct it to use data and information produced after its last training corpus update. RAG is indispensable in many LLM based applications, so I’ve included a link to a video presenation by IBM research scientist Marina Danilevsky. In summary, continuous and episodic physiological monitoring based on advanced #sensors are poised to transform clinical care by arming doctors, patients, and LLMs with unprecedented data and information. We noted in our investigations that ChatGPT produced more accurate and insightful answers when it had access to baseline and near realtime monitoring data sets and up to date research. Our investigations and use case evaluations suggest that monitoring technologies and data centered strategies can improve ChatGPT performance while empowering clinicians and strengthening doctor-patient collaboration and trust. Overall, monitoring technologies currently empower clinicians and doctor-patient collaboration more than LLMs like ChatGPT. This is an evolving field of research driven by continuous technological innovations so stay tuned and reach out if you have questions. https://lnkd.in/ge-MQBeU #llm #chatgpt #rag #retrievalaugmentedgeneration #healthcareinnovation #healthcareai #wearablemonitors #remotepatientmonitoring #doctorpatientrelationship

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