Vibhuti Ranjan Singh

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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HLTH Europe is coming to a close today, and that is a good time summarize. 1. We are in an age of AI buzz at the max, almost every panel had AI mentioned multiple times. 2. Medical dat is key, but making it available and integrating with it is still not a simple task in every country, and in order to unlock the value, there is still a road ahead - Standardization, API's and also process. 3. Start ups have amazing concepts and tools, but because of point number two I think it will be time for platform integration approaches as it will become an issue managing so many elements in to health system IT infrastructure. EHR vendors have already started, but it needs to get easier, better and more user friendly. 4. Healthcare is a glocal system - while the research and human body is mostly the same across the global, the nuance and local systems are very different, and maybe in devices, medicines you can usually stick to a product, when it comes to digital and AI solutions the amount of customization, and business models might kill you on the road to commercial viability. Share you thoughts about AI, data and local variabilities

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We're witnessing the next wave of leading companies being within healthcare services & being more "involved" than just writing good code. 🌊 I fundamentally believe it's equally important, understanding on the highest granularity, how healthcare is delivered at the core (in the clinics, hospitals & with doctors). A couple years ago there was a massive focus on digital health, this has shifted to healthcare services, experiencing exponential growth atm, due to the following. 📈 1. Operating within the very heterogenous market of healthcare (in particular primary care) requires to understand and interpret operational nuances (those closest to the problem/market will win) and solve for those very operational topics 🩸 2. Pain to innovate is highest amongst those delivering care, as there is no "continuing as before" (patients jump between services/providers based on availability, that's why digital health only takes off v slowly), doctors can't transition away but are "stuck" with their daily operational problems 😵‍💫 3. Willingness to pay is present, (see point 2.) and doctors can afford to pay for these services (shift from paying for "non-available" staff to software) and lastly it's the largest market (DACH €+550B primary care spend) with everyone being a user at one point, opening up multiple cross revenue streams 💸 4. No payor/insurance fights but a "simple" B2B customer (non of the DTx/reimbursement complexities) and a market with unparalleled retention rates (check CompuGroup revenues over the past 10 years, €1.2B in 2023) ⚔️ 5. Less regulation and increasing emphasis/relaxation on data usage (check Patient Data Protection Act or European Health Data Space) as well as enforced digitalisation throughout the upcoming years (every restaurant needs a modern POS, whilst doctors can still operate pen & paper...) ⚖️ Personally I am very bullish, that within the next 5 years, we'll see the N26/Revolut transformation happening in European healthcare - no flying cars - just better operations, services & experiences on top of existing core medical services. 💯 Thanks for the Eterno Health mention Thomas Hagemeijer - V cool overview! 🙌🏽

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Agreed! AI especially #GenAI is not like any other technology adoption paradigm we have seen. “We’re finding that AI requires a paradigm shift, It’s not like a traditional #technology deployment where IT flips a switch. Businesses need to identify areas where AI can make a real impact and strategically deploy AI there.”

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⬇ Great book by Marty Cagan TRANSFORMED. See below for our book review on the topic. Anyone have it on their reading list for May half term?

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Building & Engaging Expert Networks To Drive Growth ⭐ One type of initiative I’m often leading within my health tech and investment firm clients is buildout and management of networks of key opinion leaders, subject matter experts, and target customer groups that my clients can engage at regular intervals to drive solution development and commercialization. ⭐ For many leaders, it takes a bit of a mindset shift and time working with these networks to realize they are strategic resources that can be leveraged to achieve multiple organizational growth goals – not just to be used for one-off market insights or tactical commercial activities. ⭐ So many of us were originally trained to think of and engage with KOLs, experts, and the like as resources to be used sparingly (due to cost and degree of influence) and for a limited set of activities (in some cases, for very real regulatory reasons, but in many other instances, primarily to avoid them seeing the messiness within our organizations and solutions in development.) ⭐ I’ve developed a process to design, implement, and manage networks of KOLs, SMEs, and target customers that align to my clients' organizational growth goals, while also giving teams time and experience engaging with these networks in new and different ways, with my support. ⭐ If you give these individuals a more intentional seat at the table, where they can truly learn about your organization, team, and solutions, and you enable a two-way dialogue that facilitates ongoing learning and feedback, all parties will derive more value from these relationships. ⭐ If you and your team are thinking about how to better engage these stakeholders, reach out to set up time. Thanks! #growth #product #commercialization #insights #experts #KOLs #SMEs #marketresearch #venturecapital #privateequity #healthtech --- Hi, I’m Brittany Ryan, Founder & Principal at Sunrise Advisors. I serve as a fractional commercial executive to health tech and life sciences startups, investment firms, and venture-building teams. I help my clients define their solutions, engage their stakeholders, and go to market effectively. On weekends, I run around in the White Mountains of NH with my family.

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The UK's delicate industrial strategy balancing act: our service industry must embrace GenAI, but the risk is that we hand our future to US BigTech. My article in Bright Blue's new collection of essays on the impact of Generative AI, alongside such luminaries as Kenneth Cukier Rose Luckin Gerard Grech Tom Watson. "It is easy to be cynical about endless promises surrounding the transformative potential of AI. However, even if the current break-neck pace of technological development were to slow down, we are already seeing the first stirrings of a productivity revolution, especially in the services industry. This matters because the UK is the second-biggest exporter of services in the world, and, at present, we face a conundrum. If we do not embrace the AI opportunity, then we risk falling behind and losing our competitive edge. But, if we do fully embrace AI, we risk handing the keys to our economy over to the US tech giants on whose AI platforms our technological future depends." Let's hope that the next Government has the bandwidth and vision to focus on both aspects of this challenge. Do check out pages 38-39 (as well as all the other great articles obviously) for the Best Practice AI view on this. With thanks to Sarah Kuszynski and Ryan Shorthouse. https://lnkd.in/g8CJtAkJ

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🌟 Purpose-driven and Product-led Growth Strategy: Insights for (AI) Product Managers I found value and enjoyed listening to the podcast 🎙️ featuring Alex Hardiman, Chief Product Officer at The New York Times. Her discussion on purpose-driven and product-led growth strategies resonate deeply with me, and I have tried to capture my thoughts on how it applies to (AI) Product Managers across industries. 👥 Customer-Centricity: Regardless of industry, successful product management thrives on a deep understanding of customers. Alex's insights underscore the significance of knowing customer needs, market segments, and the business landscape—a cornerstone for shaping AI-driven products and services. 🎯 Ambition with Purpose: She emphasizes the pivotal role of ambition fueled by purpose; a principle applicable not only to traditional industries but also to tech companies. (AI) Product Managers play a crucial role in driving purposeful innovation in the tech sphere. 🔮 Visionary Strategy: Crafting a clear product vision is essential for aligning teams towards common goals. This is especially pertinent for (AI) Product Managers who leverage AI technologies to tackle real-world problems and foster business growth. ✨ Core Product Skills: Her insights highlight the importance of core product skills—creativity, expertise, and leadership. These traits are indispensable for (AI) Product Managers navigating the complexities of AI development and deployment. 🤝 Collaborative Innovation: Collaboration is key in both traditional and tech companies. At The New York Times, the collaboration between content and product experts mirrors the spirt within tech companies where (AI) Product Managers work closely with cross-functional teams to drive AI-driven innovation. 🛠️ Practical Wisdom: Alex's practical guidance for (AI) Product Managers emphasizes simplifying frameworks, aligning metrics with goals, and fostering inclusivity in AI product development practices. 💡 Catalyzing Transformation: (AI) Product Managers are agents of change, applying empathy, resilience, and clear communication to drive impactful transformation. Whether in traditional industries adopting AI or tech companies pioneering AI-driven innovation, their role is paramount. Let's harness the power of purpose-driven and product-led strategies to foster meaningful change and drive growth in AI-driven products and services across industries! If you're interested, you can listen to her podcast here: [https://lnkd.in/dMM_sE3v] #AI #ProductManagers #PurposeDrivenInnovation #ProductLedStrategy

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Brody Clemmer explains the "product class" he implemented at Koa Health to upskill teams into a product way of thinking. Full conversation here: https://lnkd.in/ejibKX2E #productthinking #productmanagement #productpodcast

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I‘m proud to join the panel talking about digital twins, the paradigmshift in health data management through cloud and the efforts we need to take to improve the health outcome. The White paper to the paradigmshift, including the building block description you can find here: https://lnkd.in/eXS3UAAT Better data, better insight, better outcome! #Cloud #AWS #digitaltwin #healthdata

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Strategic Prioritization in Product Management: An Introduction (3/3) In my previous posts, we covered the Kano and Value Complexity methods, as key approaches to strategic prioritization in product management. Today, I'll wrap up this series by discussing the Pairwise Ranking, which TBH, I find may show surprising results. 5. Pairwise Ranking When to Use: - When you need to prioritize just a few (lesser than 12) items. - Effective for teams looking to build consensus through comparative evaluation. Shortcomings: - Requires an intuitive understanding for each item. - Might require additional analysis to break ties and rank closely rated items. - If a good uni-modal histogram does not come out, you may need to try something else. Team Involvement: - Product Managers, Engineers, and key stakeholders. Customer Involvement: - Low to Moderate. Customers may provide input on the importance of items, but the team conducts the pairwise comparisons. Understanding and choosing the right prioritization method can make a significant difference in your product's success. Each method has its strengths and weaknesses, and the key is to select the one that aligns best with your team's needs and project goals. Later this week, I'll be talking about how much can be prioritized, what stages are best, and other thoughts that need to be added into the mix. What prioritization methods have you used in your projects? Share your experiences in the comments!

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Clinical adoption of digital health technologies is one of the greatest barrier for any healthtech innovator. 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐚 𝐫𝐞𝐚𝐥𝐢𝐭𝐲 𝐜𝐡𝐞𝐜𝐤. Selling to the NHS is hard. 😖 But even if you somehow manage to convince a health board to procure your clinical app. You still need to ensure: 1. That clinicians know about your app. 2. That there is an easy way for them to prescribe it. 3. That there is an easy way for patients to access it . Because if it takes a #clinician hours to figure out how to even get an app into the hands of #patients, the rate of adoption drastically decreases. 👉 And no adoption = no sales. Which is why I was so pleased to learn more about ORCHA's digital health formulary. Already operational in 70% of all #NHS sites, the digital health formulary allows clinicians to easily prescribe clinical apps/ #digitaltherapeutics to patients. 𝐅𝐨𝐫 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐨𝐫𝐬, 𝐭𝐡𝐢𝐬 𝐢𝐬 𝐟𝐚𝐧𝐭𝐚𝐬𝐭𝐢𝐜. Now, there is a way to rapidly scale within the NHS - without the need to set up properiatry distribution platforms to ensure your product gets into the right hands. Oh and did we mention that it is already integrated to Epic, #SystmOne and #CERNER? ________________________________ Incredibly honored to have worked with Liz Ashall-Payne and Tom Micklewright on this carousel. 🤗 👇 Please do check it out and let us know what you think! ♻ Do repost if you found this helpful! ❓ Have you heard about the Digital Health Formulary before? Dr Wiktor Szczudlinski | Arpit Srivastava | Gurnak Singh Dosanjh | Dr Faith Ndebele | Nina Sesto, PhD | Sam Milliken-Smith | Nara Moripen | Peter Hames

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My new report with FemHealth Insights on #DigitalHealth is out! A $2.5b sector within #WomensHealth that had a ton of data to dive into. Available for purchase on femhealthinsights.com.

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It was a pleasure to listen to Judd Antin, Dave Hora, and Christiana Lackner discuss “The Future of UX”  with Erin May and Carol Guest on User Interviews’ Awkward Silences podcast. One portion of the conversation focused on aligning user experience research with organizational aims and constraints. Such conversations appear to be occurring a lot in the UX field today, or at least they are amplified as of late. I find this surprising. When I transitioned into “industry” from academic industry I heard a lot about biases against academics due to a perceived lack of business acumen and understanding of organizational aims, constraints, and limitations. That biased perception never resonated with me. I have always felt that organizational and other structural considerations are part and parcel to research. In fact, due in great part to my direct experiences in the dumpster fire that is higher education today, I have an awareness of such things. Indeed, I would say that most academics are acutely cognizant of the implications of higher-level structures, as they were forced to survive and thrive amidst the stormy seas of higher education. Given this, I'm left somewhat puzzled and with an impression that many UX researchers lack, or at least underappreciate, awareness of at least some of the structural and organizational realities of capitalism that underlie most, if not all, of the work they do. If this diagnosis is correct, one suggestion is that researchers read Marx (and other social theorists, but maybe start with Marx). My first reading of Capital, Volume One, which I did with the accompaniment of wonderfully insightful lectures by David Harvey, was world-shifting. Marx’s work helped me to develop an initial understanding of the machinations of capital, which in turn led to greater understandings of the constraints, limitations, motives, and contradictions that drive regimes of capital accumulation. Beginning to understand and come to terms with the ultimate realities of those machinations–and the realities that those mechanics produce–is fundamental to the research that  I do today, no matter what the more proximal contexts are. Marx’s work also inspired me to adopt a dialectical approach to research and critical analysis. Dialectical thinking involves tacking back and forth between different scales and modes of analysis to obtain a more holistic view of structures, organizations, and processes, as well as their interrelationships. The approach allows me to recognize the interplay between various factors and develop a deeper understanding of the broader contexts in which my work is situated. By embracing such an approach, I can uncover more meaningful insights and produce research outcomes that have more nuance in terms of value propositions, strategy, and decision-making.

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Many companies are looking to product transformation as a growth lever. But product leadership is not one size fits all. My colleagues offer a useful approach to trade-offs.

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Estimating the Cost to Develop a Healthcare Chatbot Like Google's AMIE AMIE's exceptional performance has been rigorously evaluated through comprehensive testing and comprehensive comparison with primary care physicians. The results have unequivocally shown its excellent capabilities. In a detailed study, AMIE demonstrated superior diagnostic accuracy but outperformed 84% of participating physicians in assessing various patient scenarios. Its remarkable efficiency in quickly analyzing vast amounts of medical data and delivering accurate diagnoses in mere seconds sets it apart from its human counterparts. Factors Affecting AMIE-like Healthcare Chatbot Development Cost ·      Features and complexity ·      Integration with healthcare systems ·      Data acquisition and annotation ·      Technical stock ·      Regulatory compliance ·      Development team expertise Developing a healthcare chatbot like Google's AIME requires a significant investment in time, resources, and the support of an expert mobile app development company. Therefore, it requires thorough planning, strategic implementation and cost-saving strategies to manage the budget landscape. Read more!! https://lnkd.in/geTSPvAb

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From the article: "This latest progress in AI might not be revolutionary but it is fast-paced.. More than a year after GPT-4 spurred a frenzy of new investment in AI, it may be turning out to be more difficult to produce big new leaps in machine intelligence... It’s unclear how long the world must wait for that next big leap in AI ... In the meantime, we will need to figure out new ways to measure how useful the technology really is." https://lnkd.in/g7r7H_Nn

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There is a big debate right now about approaches to product development. On one side, product coaches and authors like Marty Cagan and Petra Wille highlight what sets the best apart from the rest. On the other side, some practitioners argue these views set unrealistic expectations for the "real world." Really easily it becomes a battle of extremes. However, I believe it boils down to whether you believe in working a certain way or not. If we abstract the content the criticized books emphasize customer-centricity, leveling up your game, and striving higher. Taking inspiration from companies that work that way. You can't achieve this overnight, and you must adapt to your situation. But if you believe in these principles, they can work. You can make them work. 👉🏻 Let’s take a metaphor I relate to: I love running marathons and admire Eliud Kipchoge. He shows that no human is limited, running a marathon in under 2 hours. While I might never achieve that, I can still draw inspiration and push my own limits. The same applies to companies in product development. Strive for customer-centricity and value creation, but adapt to your context: • If you're managing outputs, start by validating assumptions and getting closer to your customers. • If you struggle with prioritization, begin discussing the goals you're trying to achieve. • If you work in B2B, start by talking to customers at least once before developing. Take inspiration, strive for value creation, and adapt these principles to your situation and your area of influence. In the comments some examples on how I worked with this “transformations in practice”. What is your take on the debate? 💡

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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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