Ganesh Padmanabhan

Excited to share our latest blog post on how EHR integration provides contextual data that improves AI decisions in healthcare! At Topology Health, we understand the critical role that contextual patient data plays in making informed clinical decisions. Our tools enable software developers to integrate their applications with Electronic Health Records (EHRs), ensuring that digital health apps powered by Generative AI (Gen AI) can access comprehensive and up-to-date patient information. A recent study published in NEJM AI, “GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines” by Ferber et al., demonstrated the importance of contextual data in enhancing AI performance. The study found that GPT-4, when augmented with retrieval-augmented generation (RAG) to pull in additional clinical information, provided correct responses in 84% of cases compared to 57% without RAG. With our solutions, healthcare providers can benefit from: • Improved Accuracy: Access to comprehensive patient data enables Gen AI models to provide precise diagnostic and treatment recommendations. • Enhanced Efficiency: Automated data retrieval reduces manual data entry and cross-referencing, allowing providers to focus more on patient care. • Up-to-Date Information: Continuous integration with EHRs ensures the latest patient information is always available. • Personalized Care: Contextual data allows Gen AI models to offer treatment plans tailored to individual patient needs, improving outcomes. Read our full blog post to learn more about how we’re advancing digital health apps by integrating Gen AI with contextual patient data. Check out our YouTube Playlist on interoperability (https://lnkd.in/gvudD85q) to discover how we can improve the accuracy of your next Gen AI health project. Josh L. David Kwan Niles Geminiuc PMP, ITIL #HealthcareInnovation #DigitalHealth #GenAI #EHRIntegration #PatientCare #AIinHealthcare #HealthcareTechnology #TopologyHealth

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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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🚀**Embracing AI in Market Access: Cutting-Edge Efficiency and Impact** In the fast-evolving healthcare landscape, time is of the essence. Market Access Leaders, let's dive into how AI is transforming our workflows by significantly boosting efficiency and strategic outcomes across critical touchpoints. ⏰ 1. **Market Analysis and Launch Strategy** - 🔍 Data crunching that took weeks is now achieved in days, allowing rapid, data-driven strategies. - 📊 Real-time insights enable agile responses to market dynamics. 2. **Value Shaping and Demonstration** - 📈 Generating robust evidence and impact models that previously took months now only take a fraction of the time. - 🧠 AI enhances value communication by identifying key differentiators quickly and accurately. 3. **Pricing and Reimbursement** - 💹 AI-driven models forecast pricing strategy implications, reducing analysis time from weeks to hours. - 🏦 Streamlined simulations and scenario planning optimize reimbursement pathways faster than ever. 4. **Stakeholder Engagement** - 🤝 Personalizing stakeholder outreach, previously manual and time-consuming, is now automated to deliver customized insights more efficiently. - 💬 Enhanced communication strategies through AI-driven data, fostering deeper and more meaningful engagements. 5. **Payor Communication** - 💼 Aligning and negotiating with payors is accelerated as AI synthesizes data and prepares negotiation models in record time. - 📜 Rapidly generating comprehensive dossiers and value propositions saves weeks in preparation. 6. **Market Entry and Execution** - 🌐 AI optimizes market entry strategies, cutting down research and planning phases from months to weeks. - 🎯 Targeting and execution are sharpened, ensuring swift go-to-market actions. 7. **Monitoring and Adaptation** - 📡 Real-time monitoring of market conditions and customer feedback enables immediate strategy adaptation. - 🔄 What used to require quarterly reviews is now handled instantaneously, ensuring constant alignment with market needs. 💡 AI doesn't just save time—it catalyzes smarter, more impactful decisions, fundamentally transforming our approach in Market Access. As we harness these tools, let's continue to innovate and lead with foresight and agility, driving better healthcare outcomes for all. 🌟✨ #MarketAccess #AI #HealthcareInnovation #Efficiency #Strategy #Leadership

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I am grateful to the Forbes Tech Council for featuring me in their latest expert panel on “Innovative Data Management.” AI is unlocking vast human potential in health plan operations by streamlining manual tasks, and provider data management serves as a compelling case study for this evolution. The downstream impacts of AI’s utilization in areas like roster automation, mining provider legal contracts and improving directory accuracy are profound and hold significant implications for the entire healthcare industry. As roster ingestion becomes fully automated and directory accuracy improves, we are witnessing firsthand how AI creates space for humans to engage in higher-order thinking. Generative AI is only going to accelerate this trend, and I am proud that HiLabs is part of that innovation

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Ai : Hunanity's greatest creation for Good and Bad. To secure ourself #zerotrustai In the journey, MS brings up VASA-1 will AI model which essentially spins a selfie image and turn it into a talking clip of you. All you have to do is upload a photo along with a voice note and let the AI model do the talking for you.

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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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🤓 𝗟𝗲𝘁'𝘀 𝗴𝗲𝘁 𝗻𝗲𝗿𝗱𝘆! Take a look at this Amazon Web Services (AWS) blog about "Project Heal" – an AI-powered toolkit designed for the early detection and mitigation of medical misinformation. The blog does an excellent job of explaining some the technical components of the system. Here are some highlights: 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝘆𝗶𝗻𝗴 𝗠𝗶𝘀𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 “One of the key functionalities of Project Heal will be the ability to classify and detect emerging misinformation before it has an opportunity to proliferate.” “Ingested information will be evaluated using trained ML models, enabling the tool to classify the likelihood of a statement having misleading content and allowing for categorization based on the statement’s entities and context.” “Misleading statements will then be evaluated by a subsystem to help score the severity of the threat to human health.” “The detection engine will be built using graph neural networks, which will be supported by a large, scalable, fully managed graph database (Amazon Neptune). Amazon Comprehend will be used to support keyword and entity extraction from content.” “A human feedback loop will consistently audit and improve the model using Amazon Augmented AI (Amazon A2I).” “Both detection and scoring will be supported by Amazon SageMaker, a fully managed service to prepare data and build, train, and deploy ML models for any use.” “Additionally, generative AI will support the summarization and grouping of related misinformation content.” 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗖𝗼𝘂𝗻𝘁𝗲𝗿 𝗠𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 “One of the most innovative features of Project Heal will be the platform’s ability to generate tailored communication responses to misleading information.” “To achieve this, Project Heal will use generative AI foundation models (FMs) through Amazon Bedrock, such as Amazon Titan.” “Supporting evidence, built from trusted sources of information, will be available to users via Retrieval-Augmented Generation (RAG), an approach that reduces some of the shortcomings of large language model (LLM)-based queries.” “Through this technique, Amazon Bedrock is able to generate more personalized messaging by combining trusted information and user preferences.” 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 This project illustrates the complexity of developing AI-driven solutions and the importance of integrating them thoughtfully to tackle real-world challenges such as medical misinformation. 🙂 With these insights, I hope I’ve satisfied your nerdy needs! https://lnkd.in/e3gzR7XG

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Will Generative AI Transform Healthcare? Uncover the Opportunities and Challenges. Generative AI is poised to transform healthcare, offering unprecedented capabilities in data management and patient interaction. As investments from both tech giants and innovative startups flood this emerging sector, the potential for groundbreaking developments is significant. However, this enthusiasm is tempered by considerable concerns. The readiness of AI systems, coupled with issues of bias and privacy, has prompted calls for a cautious approach. Healthcare professionals and industry advocates stress the importance of establishing stringent regulatory frameworks and conducting thorough testing to ensure that AI technologies are both safe and effective. For investors, the integration of AI into healthcare presents a unique dual opportunity: to lead the charge in technological innovation while addressing the ethical and practical challenges it brings. This careful balance of advancement and accountability is crucial in navigating the future landscape of healthcare technology. https://hubs.la/Q02t4NwV0 What are your views on AI’s role in modern healthcare? Join the conversation below and share your insights.

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Tushar Zade Outlines the Promise and Perils of Gen AI: Ethical Considerations Checkout: https://lnkd.in/gF7pmEyC Tushar Zade, CDO & CIO, Aurigene Pharmaceutical Services Ltd. Generative AI can transform healthcare by: ▶ Automating systems and enhancing clinical decision-making. ▶ Democratizing expertise through diagnostic support tools. ▶ Improving efficiency, equity, and effectiveness in healthcare delivery. However, integrating generative AI requires meticulous change management and risk mitigation strategies. Implementation science plays a crucial role in structured adoption programs. Anjali Sethi Joshi Puja Bhardwaj #heatlthcare #pharma #genAI #generativeAI #cio #ciso #cto #techleaders #ITsecurity #Cybersecurity #Infosec #DataProtection #AISecurity #ThreatIntelligence #Cybercrime #Cyberattacks #DigitalSecurity #Phishing #RiskManagement #DataBreach #Passwords #NetworkSecurity #CybersecurityAwareness #InformationSecurity #DataPrivacy #InfoSecurity #Hacking #CloudSecurity #Malware #Ransomware #AIinnovation #AItechnology #GenerativeAI #AIdevelopment #ArtificialIntelligence #AIresearch #FutureofAI

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One of the few women in South Asia - Ritu Mehrotra who is building and leading a deep tech company on a global scale and making waves through the US as we talk. If you haven't heard her story or the way she she is planning the momentum to dent and bring a vital change within the mental health space globally through GenAI and Deep tech then you should listen to her, for once. Its great to see her talk with some Awesome Starcast Leaders on this Talk Gen AI platform in San Fran. Happy and enlightened to work with an awesome, inspiring and wonderful founder - Ritu , who means and plans to bring a change by doing things as they should be done like with a gigantic leadership stance. We are the one of the first and finest companies with gold standard efficacy and intent detection in the Gen AI space when we talk about chatbots. If you are a healthcare system, payor or anyone in the employer category in the US wanting to bring in productivity, agility and create full blown support to mental health and well being then you should connect with us. I am sure it will be a joyride. Also please remember the coffee is on us :) Godspeed. United We Care Google NVIDIA Amazon Web Services (AWS) AstraZeneca Geodesic Capital Blitzscaling Ventures Civilization Ventures Everest Group Ritu Mehrotra Shumita Kakkar Sourav Banerjee #genAI #deeptech #AI #technology #mentalhealth #humanresources #healthcare #healthtech #artificialintellegence #hr

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Thanks to Datanami for sharing my thoughts. Gen AI is amazing, and we are in the golden age of AI development. For AI to be adopted by the masses, we need a robust way to identify and mitigate risks. A lot of work is being done through the open-source community and leading tech providers around AI safety. With regulations coming in, there is optimism for a responsible AI future.

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AI is revolutionizing healthcare, but at what cost? 1. **Unprecedented Innovation** Imagine a world where diseases are not just treated but predicted and prevented through AI. This is not a distant dream but today's reality. From diagnosing cancer earlier than ever to customizing patient care plans, AI's capabilities are nothing short of miraculous. 2. **The Ethical Dilemma** But with great power comes great responsibility. AI in healthcare raises critical ethical questions. Who is responsible when an AI system makes a mistake? How do we ensure patient data privacy and security in the age of digital health records? The answers are complex and demand our attention. 3. **Redefining Morality** Moreover, AI challenges our moral compass. The reliance on algorithms to make life-and-death decisions brings to light the delicate balance between technological advancements and human judgment. It asks us to redefine what it means to care and to heal. 4. **A Call to Action** This is where we come in. It's up to us to navigate these waters with wisdom and foresight. By engaging in informed debates, pushing for transparent policies, and advocating for ethical AI use, we can harness AI's potential without compromising our humanity. Let's not shy away from these challenges. Instead, let's inspire change and lead the way in creating a healthcare future that is not only technologically advanced but also ethically sound and morally just. Engage below with your thoughts on how we can achieve this balance. Your insight could light the way forward.

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AI just got smarter with less. Here's how. Interested in AI minus the data feast? GDPL is the key. Think AI in healthcare had peaked? Meet GDPL. Introducing a health platform that thrives on minimal data. Thanks to GDPL, this isn't just possible; it's happening. Here’s what it looks like: 1. Connect dots between text and images; 2. Start smart with just a few data points; 3. Stay within privacy bounds, effortlessly; 4. Scale up without weighing down on resources. Sounds impossible? It's not with GDPL. Imagine getting accurate health insights without data deluge. That's closer than you think. Right, who needs another data-hungry tool? Here's to making AI lean yet powerful. Now, think bigger—GDPL doesn’t just streamline; it overturns our data expectations. Reactions? Ideas? Could lean data be the pivot we needed in tech? Let's chat. #LeanAI #HealthInnovation #GDPL

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Excellent report from General Dynamics Information Technology John P. Curran regarding adoption and trends of AI in the Govt. I used Meta to generate summary below and included link to original article. Main AI use cases are chat and creating/updating computer code. Note also the 23% failure rate of AI projects due to user expectation misalignment. This can be overcome with thorough protoyping and establishing use case metrics. - Successful AI project completion timelines are narrowing, with an average of 14 months from pilot to production. - Future demand for AI projects is building around chat and coding-related functions. - 54% of surveyed AI experts said projects currently under development involve improving computer code. - 59% of projects involve "research and understanding", which may relate to creating chat functions. - 23% of AI projects fail due to misalignment between AI solutions and user expectations. - Partnership efforts are required for successful AI projects. Data diversity is crucial, and incorporating diverse data sources can lead to better AI outcomes. Trends: - AI adoption is increasing across the Federal government, with a growing focus on chat and coding-related functions. - Agencies are leveraging cloud-native services, tooling, and other capabilities to accelerate AI project development. - There is a growing interest in translating older code to newer languages, such as Python or cloud-based analytic tools. - Domain-specific chat functionality is becoming a priority, with many agencies planning to implement chat services in the next two years. Statistics: - 325 AI experts surveyed across the Federal government - 14 months: average time from pilot to production for AI projects - 54%: percentage of projects currently under development that involve improving computer code - 59%: percentage of projects that involve "research and understanding" - 23%: percentage of AI projects that fail due to misalignment between AI solutions and user expectations. Bill Casola John Grim Leslie Anne McIndoe Kim Giffin Michael S. Freeman III Tobias Quaranta https://lnkd.in/eqvmAVFG

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

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Exciting News: How AI is Revolutionizing Healthcare I'm thrilled to share how artificial intelligence (AI) is reshaping the landscape of healthcare, transforming diagnostics, patient care, and treatment strategies. As a mathematician, software development expert and spending last 20 years building Healthcare IT service, I'm excited about these groundbreaking developments that are improving patient outcomes and enhancing the efficiency of our healthcare systems. Let's delve into some fascinating examples of how AI is making a tangible impact: Enhanced Diagnostics: AI-powered imaging systems are now assisting radiologists in identifying intricate patterns and abnormalities within medical images, significantly boosting diagnostic accuracy and ultimately improving patient care. Personalized Patient Care: AI algorithms are enabling healthcare providers to swiftly identify critical patient messages, allowing for prompt responses and prioritized care, which is crucial in urgent medical scenarios. Predictive Analytics: AI-driven models are revolutionizing patient management by forecasting readmissions and identifying risk factors through comprehensive data analysis. This proactive approach leads to better interventions and outcomes for patients. Virtual Healthcare Assistants: AI-powered virtual assistants and chatbots are revolutionizing patient experiences by providing personalized healthcare advice and answering patient queries promptly, ensuring enhanced communication and satisfaction. Medication Management: AI systems are optimizing medication adherence by analyzing patient data, identifying usage patterns, and delivering personalized reminders and recommendations, thus improving overall treatment effectiveness. In conclusion, we stand at the forefront of a technological revolution in healthcare. AI and ML technologies are harnessing vast patient data to personalize care, streamline diagnostics, and optimize treatment plans. This convergence of technology and human touch is revolutionizing patient engagement and empowering healthcare professionals to focus more on what truly matters—providing exceptional care. We are witnessing the dawn of a new era where AI seamlessly integrates with healthcare, promising endless possibilities for improving patient outcomes and transforming our approach to wellness. Together, we are exploring the limitless potential of AI in healthcare, and the future looks incredibly promising! 🌱 #HealthcareInnovation #AIRevolution

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