Jaydeo Kinikar

Key trends and takeaways from HLTH 2024: 1. AI's Evolving Role: AI dominated discussions, with executives acknowledging its potential while also recognizing challenges in accuracy and clinician trust. There's a shift towards focusing on the practical applications of AI, with a call for partnerships to ensure reliable testing and implementation. 2. Infrastructure Focus: Beyond the glitzy AI tools, there's a growing emphasis on the "picks and shovels" needed for healthcare data accessibility, including infrastructure, compliance, and interoperability. Executives believe that improving data flow will facilitate the effective use of AI. 3. Middlemen Under Scrutiny: Pharmacy benefits managers (PBMs) faced criticism for their role in rising drug costs, with discussions highlighting misaligned incentives and the need for transparency in healthcare pricing. 4. Women's Health Spotlight: Women's health initiatives gained attention, particularly following government funding announcements and personal stories shared by advocates. However, investment in women's health remains minimal compared to overall healthcare funding. 5. Interest in Psychedelic Medicine: The potential of psychedelics for treating mental health conditions garnered significant interest, with discussions about funding and research moving forward, especially within the Veterans Affairs system. Executive Takeaways: Leaders from various sectors emphasized the importance of consumer-focused strategies, collaboration, and the need for responsible AI usage. There was a consensus on the critical role of addressing healthcare accessibility, improving patient experiences, and fostering innovations that genuinely enhance care delivery.

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Healthcare technology is in trouble. After the relative boom time we experienced through during the pandemic, the fervor to innovate in health systems and payers seems to have dried up. While organizations continue to post stellar earnings, they also continue to lay off experienced health technology people en masse. Why? I think it's time for a bit of honest reflection. 70% of all enterprise healthcare technology projects fail. 17% of of all large scale IT projects go so badly that they threaten the very existence of the company itself. Bridging Business & IT, LLC For projects with a total price tag of over $1MM, that failure rate jumps to over 90%. And the ever prescient Clayton Christensen believed that the failure rate for all new product introductions is 95%. This is not new information. What is new is the most recent E&Y Pulse Survey that has health system CIOs believing that 7 in 10 of their healthcare IT projects have had no discernible #ROI. None. And this is for the projects that didn't fail. https://lnkd.in/gRgVt_Xs This is an existential crisis that health technology must face honestly and soberly. Lack of planning and poor communication are the core culprits. Clunky and outdated Waterfall project methodologies can be blamed for many of these failures. And frankly Agile disciples could use a bit more planning before they start iterating so they know what they're building. There are, however, some problem actors and business models that need to be called out. The historically biggest culprits, in my humble opinion, are large, institutional, enterprise software vendors and their server-based architectures. For decades they feasted on seven figure licensing deals, sometimes humble-bragging to potential implementation partners about 5:1 or 7:1 implementation cost to license cost ratios. Companies just went along with it, because no one gets fired for buying IBM, right? But the failures in this segment are legendary. And many have indeed been a key cause in the parent company failure. As for the ROI, there was a bright spot in the E&Y Pulse Survey. Despite not having established a demonstrable ROI, 96% of them still wanted to move forward with the projects, believed the investment was worth it. So the issue isn't necessarily that the projects aren't generating value, the issue is that the organizations (and their vendors) didn't do a good job up front validating agreed upon ROI metrics and measures. They don’t fully understand the value it is (or isn't) creating. This is our collective job to be better at this. So what can we do to reduce the failure rate and get health systems and payers back into investing in #innovation? #ROI #startups #valueengineering #goldenpath #LCNC #opensource

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After a brief writing hiatus… Part III of “Who Pays for Healthcare AI” is live. The third installment defines five performance-based pricing models for healthcare AI and explores: • Four types of Return on Investment (ROI) in healthcare and life sciences • The importance of shielding at-risk pricing models from operational externalities • How better pricing model design can mitigate negative working capital risk under performance-based models • Taming the beast: attribution Check out Part III below, and subscribe to receive the final post of the series! https://lnkd.in/dPZSHMiy

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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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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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Do you ever wonder why health technology and therapy consumers make the decisions they do? I'm eager to hear how Roundpeg Consulting, LLC has leveraged MRI-Simmons' consumer insights to provide value to organizations and leaders across various sectors of healthcare. #Healthcare #B2C #Leaders #Consumer #HealthTechnology

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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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A great read about the shift in Epic's strategy, moving from a protectionist model to a true platform. Epic's new approach of building close partnerships a select, highly curated group of tech companies coupled with the enablement of consumer apps is the winning strategy moving forward. This is a 180 degree shift from the platform strategy that Salesforce used to dominate the CRM market - being the platform that every other company must build on. The "Epic model", made possible by AI, instead allows others to build and then feed into the Epic UI. Epic is the solution that the user knows and becomes indispensable in a world where it could have easily been relegated to a database in the background. It further derisks Epic's need to build its own AI capabilities and might even lead the company to a M&A strategy in the future. https://lnkd.in/gtSXNuru

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With $8.2B invested into digital health so far, 2024 is shaping up to be a $10-12B annualized investment year. This would make funding comparable to 2023. I suspect with continued venture investment, boards will require exits. One thing I’m particularly interested in is terms of recent acquisitions and growth equity rounds. What type of downside protection (e.g., structure) is not disclosed to media that protects the investor? Will funds who have specifically raised capital to buy stakes in VC-backed companies at significant discounts? And what revenue/EBITDA multiples are looking like for M&A in 2024/2025? Every founder has to spin their story in a positive light when speaking to media. I know this because PR agencies reach out to me about their clients (p.s. I don’t consider myself a member of the media). The private health tech market feels like it could significantly rip or drop in the next 12-24 months. Therefore, my general advice to early-stage founders is to assume you will not raise capital beyond your seed round. If the investor market heats up in 2025, you will have the option to raise a series A at that time if you’ve built a strong business. If you’ve already raised series c or beyond, then one of your goals will be to time the exit (M&A or IPO) appropriately so that you garner an attractive multiple. There are few private health tech startups that have founder board control, can reinvest positive cash flows, and stay private forever. So by necessity of the structure, exits are desired. - - - - Image Credit: Rock Health

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Many healthcare problems do NOT need a Tech solution... but it’s hard for startups to understand this because they can lack clinical context. Let me explain👇 On the outside looking in, healthcare has so many problems (yes, we hear you). And given how technology has transformed so many other industries… it seems intuitive to technologists that we should be able to extrapolate the “software is eating the world” approach to healthcare quite easily. On the other hand, what’s also intuitive to many Silicon Valley types is the idea of thinking from “first principles”. First principles thinking is a way of solving problems by breaking problems down into the most basic facts and core truths and then trying to solve the problem from scratch - instead of just going along with the assumptions the rest of the world makes. The challenge here is that there is so much nuance and complexity in healthcare that it’s very hard to understand all of the basic facts unless you’ve been immersed in the messy healthcare system yourself. This is especially true for very difficult clinical problems, problems related to patient care - where unless you’ve actually been part of the care team and lived in the messiness… it just becomes way harder. For example: unless you’ve worked in a busy clinic, and see how physicians and nurses are running around all day, often never getting a second to sit down and catch their breath… you don’t realize how adding a few extra clicks from your beautiful software might not actually be possible (as crazy as that sounds). In which case you have to be solving a problem so important, so meaningful that it’s worth their time… even for just a few extra clicks. Now take that one example and multiply it by dozens of other day-to-day aspects of a clinical workflow that you don’t totally understand. And then multiply that by the complexity of clinical situations for patient care that people spend sometimes a decade in school and training for depending on the specialty. So what technologists need to realize is that unless you are able to totally immerse yourself in the healthcare setting for a reasonably long period of time and develop that acumen for the complex clinical setting… you really do need clinicians on your team who already have that clinical context. Otherwise you will be unable to build up an understanding of a problem from first principles within a reasonable time frame. And without that clinical context, it’s really hard to understand when and why Tech is the right solution for the problem… as opposed to Tech being a hammer and treating every healthcare problem like a nail.

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Exciting to see CodaMetrix named as one of Boston's top startups to watch in 2024! The administrative burden on physicians has grown tremendously, leaving less time for providers to dedicate to patient care. CodaMetrix helps solve that problem with their AI-powered solutions, allowing the providers to focus on their patients and giving the coders technology that helps them focus their expertise on complicated cases. #medicalcoding #AI #healthcare #transforminghealthcare

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What is one of the country's leading safety net health systems focused on right now? Gabriel M. Cohen MD, CMIO for Population Health at NYC Health + Hospitals shared his perspective with us in this week's 𝘌𝘹𝘦𝘤𝘶𝘵𝘪𝘷𝘦 𝘐𝘯𝘴𝘪𝘨𝘩𝘵𝘴: 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝗲𝘀 𝗳𝗼𝗿 𝟮𝟬𝟮𝟰? 1️⃣ Patient engagement – leveraging omnichannel communication to efficiently close care gaps 2️⃣ Interoperability – putting usable data in front of our clinicians at the point of care to support care gap closure 3️⃣ Care coordination – building the tech infrastructure for our social workers, community health workers, and care managers 4️⃣ Care infrastructure for asylum-seeking and incarcerated patient populations – multiple initiatives, with a particular focus on providing housing for homeless populations 𝗪𝗵𝗮𝘁’𝘀 𝗮 𝗿𝗲𝗰𝗲𝗻𝘁 𝘃𝗲𝗻𝗱𝗼𝗿 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝘀𝘁𝗼𝗿𝘆? Our partnership with Findhelp. Addressing health-related social needs is a critical priority for NYC H+H Our organization already has a high penetration of social needs screening, so our focus today is connecting patients with the right resources to address the identified disparities. Findhelp helps us with this by: • Ingesting the health-related social needs screening data • Identifying those patients who have a need • Locating the best local resource(s) for that patient • Connecting the patient to the resource(s) • Facilitating end-to-end communication, including informing the physician and care team whether the patient accessed the resource(s) Key to this initiative's success has been its tight integration with our current workflows in Epic, to the point that it should largely feel seamless for our clinicians. 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗼𝗻 𝘄𝗶𝘁𝗵 𝗿𝗲𝗴𝗮𝗿𝗱 𝘁𝗼 𝗔𝗜? As a safety net health system, NYC H+H’s priority is to ensure that the AI tools and models we employ can sufficiently manage potential bias. We’re currently prioritizing governance-related workstreams (e.g. governance councils, frameworks, and processes) that will drive a safety net health system playbook for appropriately evaluating and managing AI. The specific AI use cases we're currently prioritizing include: • Chart summarization • Inbasket management Other future areas of exploration: billing automation and radiology automation — For more insight from leading health IT execs, as well as Elion's latest research and can’t miss industry news, subscribe to our newsletter 📬 👉 https://bit.ly/49z6AUW

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One key reason why healthcare technologies like AI, telemedicine, and software haven't structurally lowered costs or broadened access for the entire system after decades of experimentation (excluding pure financial engineering of payer contracts) is the limited reimagining of care delivery itself, with asynchronous telemedicine as a notable exception. In the same way that digitizing physical paper failed to exploit the capabilities of a new computing medium for word processing, hosting physician-patient conversations over video does not transform the actual delivery of care. In telemedicine, many of the inputs (e.g., clinician time) and therefore cost structures of in-person care persist, which is why we often see fee-for-service telemedicine companies struggle to achieve economies of scale (i.e., growing revenues non-linearly while growing costs linearly). Until we deconstruct the components of a medical appointment – from intake and triage, to chart review, history gathering, physical examination, diagnosis, treatment, and follow-up – we will continue to be underwhelmed by the disconnect between the transformative potential of these technologies and real-world patient/system impact. I’m optimistic that the emergence of reliable and performant medical AI agents will urge us to perform this deconstruction and redesign the fundamental building blocks of a “medical appointment.”

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Interesting take from TechCrunch on AI within healthcare. I think given the pace of acceleration in the space that many of these qualms won't be relevant in a fairly short amount of time. But, interesting to note what some people are thinking about when it comes to AI in healthcare.

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Hi, friends! What’s new? I’ve been following the advancements in healthcare closely and I’m thrilled to share how AI is revolutionizing the industry in 2024. The integration of AI in healthcare is not just a trend; it’s a transformative shift that’s improving patient outcomes, streamlining operations, and driving innovation. Key Areas Where AI is Making a Difference: Early Diagnosis and Predictive Analytics: AI algorithms are now capable of analyzing medical images and patient data with incredible accuracy. This allows for early detection of diseases like cancer and heart conditions, significantly improving prognosis and treatment outcomes. 🩺 Personalized Treatment Plans: AI is enabling the creation of personalized treatment plans tailored to an individual’s genetic makeup, lifestyle, and other factors. This personalized approach enhances the effectiveness of treatments and reduces adverse effects. 💊 Administrative Efficiency: AI-powered tools are automating administrative tasks such as patient scheduling, billing, and documentation. This reduces the workload on healthcare professionals, allowing them to focus more on patient care. 📋 Drug Discovery and Development: AI is accelerating the drug discovery process by analyzing vast amounts of data to identify potential drug candidates. This speeds up the development of new medications and brings innovative treatments to market faster. 💡 Recommendations: Stay Informed: Keep up with the latest AI advancements in healthcare by following industry news and participating in webinars and conferences. Invest in Training: Ensure your team is trained in AI technologies and understands how to leverage them effectively. Continuous education is key to staying ahead. Collaborate with AI Experts: Partner with AI technology providers and experts to implement AI solutions tailored to your organization’s needs. Focus on Data Security: With the increase in AI usage, ensure robust data security measures are in place to protect patient information. How has AI impacted your healthcare practices? 🚀 #HealthcareInnovation #AIinHealthcare #HealthTech #MedicalAdvancements #PatientCare

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David Blumenthal at the World Medical Innovation Forum shared some great insights about how generative AI will need to be regulated in order to prove helpful — “The knowledge we need to manage this technology is insufficient. What are the standards to measure this against? Need a foundational science to rate the quality of generative AI models. This is a government issue, it will require funding, and consensus development. The performance metrics and standards will need to be transparent. We will also need a platform for privacy performance. This is a novel form of intelligence with applications to healthcare. — Can you regulate intelligence? — Can we train it, apprentice it, can we test it, can we certify it, can we monitor it?” Trustworthy Technology and Innovation in Healthcare Book Series Consortium (TTIC) Sherri Douville Ed Gaudet Charles Podesta Shaun E. Garcia Joshua Tamayo-Sarver, MD, PhD, FACEP, FAMIA Thanks to Mary Aggarwal, Vinay Aggarwal, MD, MPH, and Bank of America Private Bank for the learning opportunity!

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A Timely Boost to Accelerate Universal Health Coverage Goals! We are thrilled to announce that Qure.ai's recent $65 million Series D funding marks a pivotal moment in our journey to revolutionize global healthcare. This boost in resources will empower us to expand our global footprint, enhance our product portfolio, and deliver more affordable, high-quality healthcare to underserved communities. Aligned with the Universal Health Coverage (UHC) goals, Qure.ai remains committed to decentralizing care, ensuring it’s accessible and close to the communities that need it most. Our unique blend of AI innovation, strategic business acumen, and deep clinical expertise positions us to tackle some of the most pressing global health challenges. We extend our heartfelt thanks to our investors, partners, and our exceptional team for their unwavering support and belief in our mission. Together, we will continue to impact lives globally, ensuring that cutting-edge, AI-driven healthcare solutions reach those who need them most. PATH Digital Square at PATH FIND FHI 360 Management Sciences for Health KNCV TB Plus Stop TB Partnership Private Sector Constituency to Stop TB Partnership TBPPM Learning Network TBPPM Learning Network USAID World Health Organization Ministry of Health and Family Welfare, Government of India MeitY Startup Hub Startup India URC Elizabeth Glaser Pediatric AIDS Foundation Ministry of Electronics and Information Technology The Rockefeller Foundation Bill & Melinda Gates Foundation The Global Fund Friends of the Global Fight Against AIDS, Tuberculosis and Malaria India Health Fund JSI Clinton Health Access Initiative, Inc. Jhpiego

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After attending the The MedTech Conference, here are some key takeaways and questions to bring to HLTH Inc. in the coming days: - MedTech investors are prioritizing #execution efficiency, focusing on both resource management and cash optimization, while demanding clear demonstrations of value. - #Discipline and #focus are essential: every dollar invested must create #value, and that value must be proven. - #Sustainability is becoming a top priority, not just for companies but for the entire healthcare ecosystem. We need to focus on #affordability, #implementation, and the demonstration of #benefits, ideally combining both cost-based and value-based approaches. Software medical devices are exploring new market access pathways in both Europe and the U.S., but healthcare systems are not ready yet. This raises important questions: How will this evolve? How can we define adapted and relevant routes for products that bring value to patients, healthcare professionals and systems alike?! These are key issues we’ll tackle at HLTH Inc. starting Sunday. While we may not have all the answers, I’m excited to brainstorm, explore, and hopefully collaborate with some brilliant minds (#whitematter 👌 ) to generate value and revenues within global healthcare systems.

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The new Enhancing Oncology Model (EOM) is a significant upgrade on the Oncology Care Model (OCM) in terms of both opportunity and complexity. The primary complaint I heard from participants during the original OCM was the "randomness" of payments. Many participants were not able to build additional revenues into their forecasts and budgets because the payments were intermittent and no one was able to explain what they had done to generate the successful payment. The EOM is designed to address this by adding additional adjustments to the payment methodology. This is a critical factor given the two-sided risk embedded in the EOM. It was one thing to get random bonus payments during the original OCM, it will be entirely different to get seemingly random bills from CMS during EOM. The new payment methodology is going to generate a less random result than the original due to the significant clinical and regional adjustments built into the algorithm. Participants will need to understand the complexity of the payment mechanism in order to anticipate how they will perform under this new model. As with BPCI-A analytics will play a key role in forecasting performance in the EOM. Key factors to analyze include: *Episode Attribution - Are you capturing all of your own episodes? Did you suffer from leakage during the baseline? *Baseline Expenditure - CMS will provide your baseline, but you will need to understand the components. How much of your baseline was impacted by other CMS programs (e.g. PC+, BPCI-A, et.c)? What is the impact of the trend adjustment? *Baseline Price - How does the Price Prediction model work? How is this affecting your baseline over time? What is the impact of the Experience Adjuster and what are the key components? What is the impact of the Clinical Adjusters? How are you poised to capture these in your clinical data? All of these factors (and others) are true enhancements to the program and will result in a more stable and predictable revenue stream for participants. To take advantage of these improvements to the payment methodology participants must work with their analytic teams to understand each component and start designing key reporting assets to track these as the program moves forward.

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