Christie Mealo

Ok, I am impressed with Chat GPT 4o I gave it the following prompt: "Write a one page essay describing why generative AI will have negative affects on society because it ill increase the power of big tech companies" And it returned a good essay, including this (in my opinion) impressive passage: "Additionally, the power imbalance created by generative AI could lead to a new form of digital colonialism, where a few tech companies control the digital infrastructure and services that underpin much of modern life. This control extends beyond national borders, allowing these corporations to exert influence over governments and societies worldwide. Developing countries, in particular, may find themselves dependent on the technologies and platforms provided by big tech, limiting their ability to develop indigenous technological solutions and weakening their digital sovereignty."

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Such an insightul discussion with Michael Pencina at NODE.Health digitalmedicineconference. Discussing how we need network of networks to lift all boats with AI and concept of Olympic symbol or Venn diagrams where overlapping areas are partnership area that serve as glue. Need to have national standards (such as through FDA, and Coalition for Health AI (CHAI) ), AI toolkit and infrastructure guidance (such as through TRAIN) and local execution and value creation (Valid ai w NODE.Health) ..

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The devil is in the details. Learn why record retrievals for HEDIS and Risk can be challenging and how Inovalon solutions help

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https://lnkd.in/e_KBZUx2 this can help providers leapfrog their capability maturity but clean data pipelines are a foundational prerequisite.

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

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17 reasons I loved #GTM24! 17. Sonal Mane could be a standalone reason to love this event! Her future of GTM AI and automation session with John Hessinger identified things GTMers should be experimenting with and how to incorporate GenAI into existing workflows. 16. Peter Ward’s discourse on formalizing market readiness and ways to align cross-functional groups. 15. Macy Berg Mody’s example of eliminating repetitive x-team readouts and making meetings more productive with shared tech informing multiple groups on customer feedback. 14. Holly Holbrook's best practices for internal comms like creating a “front door” for cross-team conversations and ideation. 13. Emi Wayner’s practical guide to GTM launch metrics was great, but it was even better to hear how she evolves metrics while retaining focus on key impact areas. 12. I loved Candice Sparks and Lexie Knauer’s dialogue on building for parity vs. differentiation. Don’t let your product get in the way of a good story! 11. The most POV-altering moment of the conference (for me) was the fireside chat on unlocking brand growth with Maya Shah-Ceccotti, Karen Sage, and Nidhi D.. It changed my thinking re: CLG. Build it into growth motions intentionally upfront! 10. “If you build it with them, they are already there.” - Kasey Stinson. 9. 43% of buyers desire a “seller-free” sales experience, according to Gartner (and Filip Szymanski in his session on PLG vs. SLG). I appreciated his thoughts on scaling growth loops, whether collaboration growth loops or user content growth loops. 8. More people would remotely lead successful GTM teams if they followed Paige Lapen’s “make work rock” framework or James B. Stanton’s weekly meeting cadence: Commit - “CFC” - Complete. 7. Internal messaging gets diluted with multiple communication methods, as Somesh Chablani pointed out. Don’t drop it into Slack and assume everyone knows. Overcommunicate with remote teams. 6. Daniel Kuperman’s competitive intelligence matrix and launch blueprint could have been an entire session, and I still would have wanted more! 5. Nonya Collier is the collaboration queen! Co-creation in a brain trust, codifying insights, amplifying the collective intel. 4. “Beyond the Launch” by Ed Cho may have convinced me that math can be fun and definitely convinced me of the necessity of focusing on pipeline maturation and not just pipeline creation. 3. Ariel Cai walked through how AI is redefining the go-to-market playbook. 2. I was excited to share my session on accelerating sustainable growth with ideal customer profiles. Plus, in a shocking turn of events, I got to chair the second day! 1. The number one reason to attend any event from The Alliance/GTM Alliance is to connect with the most incredible people! I can’t list everyone, but I have to mention Chase Payne, Cinthia Silva, Jan, Amy 🐆, Rohit Angle, Alisha Johnson, Catherine Knoll, Samantha, Sasha, and I finally got to meet the mastermind behind it all: Jack Debono!

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In today's fast-paced and competitive landscape, leveraging proprietary knowledge is essential for healthcare organizations aiming to stay ahead. The article "How to Train Generative AI Using Your Company’s Data" by Tom Davenport and Maryam Alavi highlights the potential of generative AI to transform knowledge management within organizations, including those in healthcare. I think this is a critical topic for us to consider… Generative AI offers a powerful means to harness and disseminate critical internal knowledge. For healthcare organizations, this means improving patient care, streamlining administrative processes, and enhancing decision-making. Fine-tuning or prompt-tuning large language models (LLMs) with specific domain knowledge can lead to significant advancements. For instance, Google’s Med-PaLM2 project, which fine-tuned a general LLM with medical knowledge, demonstrates how AI can enhance clinical knowledge, albeit with ongoing quality assurance and evaluation. However, the integration of generative AI in healthcare must prioritize data privacy and security. Given the sensitivity of patient data, healthcare organizations must ensure robust governance frameworks to prevent data breaches and maintain compliance with regulations such as HIPAA. Strategies like using private instances of LLMs, implementing stringent access controls, and employing advanced safety features are critical. Furthermore, the quality of AI outputs must be meticulously managed. Healthcare applications cannot afford inaccuracies, as errors could be life-threatening. Continuous evaluation, akin to Morgan Stanley’s "golden questions" method, ensures AI-generated responses remain accurate and reliable. In essence, the adoption of generative AI in healthcare organizations promises improved efficiency and innovation. Yet, it demands a careful balance between leveraging AI's capabilities and safeguarding patient privacy and data security. The future lies in transparent, accountable AI use, ensuring that technological advancements translate into tangible benefits for both healthcare providers and patients. #HealthcareInnovation #DataPrivacy #AIinHealthcare https://lnkd.in/e7ZTZE6v

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HEDIS aims to elevate measurement standards and enhance the quality of patient care. It is one of the most important tools in the payers' toolbox, so walking through the "what, how, and why" behind HEDIS is incredibly valuable. Read the blog for details: https://okt.to/FBkERU

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If you’re headed to HLTH here are the three topics that are top of mind for me right now. If they matter to you too, wave me down and let’s talk: 1. Despite all the AI, value based care, and innovation, life expectancy in America is declining—for everyone. 2. As the industry faces profitability pressures, doctors and nurses from the C-suite to the frontline are being marginalized even more. 3. We have an election in two weeks, one that will likely have a greater impact on health and healthcare than any one of us. For me, working in healthcare is an immense privilege—and responsibility. While we each have our organizations and careers to support, we also are accountable to the people and communities who entrust us with their lives.

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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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He fell in love with an AI chatbot; then built a $24M healthtech company from scratch. Now clinicians are champions of his product. (but he had 0 healthcare experience) Alex LeBrun, Nabla's CEO, shared a fascinating story with me: Years ago, he was captivated by an AI chatbot named Cybele. This "online dating" experience sparked his passion for AI. Here's how he did it with 0 prior experience and connections in healthcare: 1. Started his own clinic ↳ Invested heavily in creating a learning environment ↳ Operated in two countries to avoid cultural bias ↳ Observed real healthcare processes firsthand ↳ Gained credibility in the industry 2. Found the right partners ↳ Looked beyond conventional healthcare thinking ↳ Avoided those fixated on minor EMR improvements ↳ Built relationships with forward-thinking clinicians 3. Learned from your mistakes ↳ Acknowledged the high cost of building a clinic ↳ Used the experience to gather crucial initial data ↳ Leveraged it to build a strong clinician network 4. Focused on distribution, not just product ↳ Made the product accessible to individual doctors ↳ Let users become product champions 5. Made his product frictionless ↳ Created a simple Chrome extension ↳ Enabled instant trial without complex setup ↳ Designed for overwhelmed, time-poor doctors ↳ Prioritized ease of use over feature overload 6. Adapted the product to individual needs ↳ Recognized diverse documentation styles ↳ Allowed personalization down to user level 7. Built a community of champions ↳ Focused on delighting individual users ↳ Encouraged word-of-mouth promotion ↳ Leveraged doctors' influence in hospitals The key to disrupting healthcare as an outsider is to stay curious, adapt quickly, and solve real problems. PS: Are you an outsider with a bold idea for healthcare? Your fresh perspective might be exactly what the industry needs. Listen to the fascinating episode here: Apple Podcasts: https://hubs.la/Q02CS_300 Spotify Podcasts: https://hubs.la/Q02CTkn60 #CharmHealth #DigitalHealthDisruptors #HealthcareAI #ClinicalAI #AIInnovation #Nabla #AlexLebrun #HealthcareRevolution #Podcast #AIChampions #MedicalTechnology

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I’m more of a value + application person.  I’ve worked with amazing tech developers who keep my guardrails up, but developing new products for product-market fit is key. That said,  I want to show appreciation to NYU Langone Health's initiative to use AI for making doctors' notes more patient-friendly.  It is a testament to the potential of generative AI in enhancing healthcare communication. This approach not only promises to bridge the gap between patient understanding and professional medical knowledge but also showcases the practical value of AI in improving patient-provider interactions. By making health information more accessible, we're stepping into a future where healthcare is inclusive, empowering patients through education. This development represents a promising application of generative AI with significant product value. Learn more here.

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

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🔄 The Case for an Enterprise GenAI Platform: Promoting Reusability to Drive Innovation 🚀   Building on my recent post about the necessity of an Enterprise GenAI Platform (https://lnkd.in/eeQBtV9p), let's delve deeper into its key benefits, starting with reusability. In my view, reusability is a crucial factor that can dramatically accelerate innovation within your organization. Why? Just consider the resource and budget fragmentation occurring in your company as each LoB (line of business) attempts to create their own GenAI POC. The majority of these use cases employ a RAG (retrieval augmented generation) design pattern, essentially applying NLP (natural language processing) to their corpus of unstructured data. Although we’re using the same fundamental architecture, there are numerous distinct codebases now being developed & maintained by different teams, leading to significant redundancy in efforts in large enterprises!   Reusability isn't just a technology component—it's a catalyst for efficiency in the GenAI ecosystem and a game-changer for innovation. By harnessing pre-built design patterns and common components, organizations can: 1/ Accelerate Development: Drastically reduce time-to-market for new GenAI solutions, 2/ Ensure Consistency: Maintain uniform quality and standards across projects, and 3/ Optimize Resources & budget Dilution: Focus on novel challenges instead of reinventing the wheel.   A well-architected Enterprise GenAI Platform should encompass these pivotal features (though this isn't an exhaustive list) to amplify reusability: 1/ Modular Architecture: Enables easy integration and reuse of components across various applications. 2/ Component Registry: A centralized repository for discovering and utilizing reusable assets like pre-trained models, APIs, data pipelines, and even GenAI applications. 3/ Standardized APIs: Facilitates seamless connections between GenAI models and internal systems. 4/ Version Control: Ensures consistency and allows teams to track changes in reusable assets. 5/ Pre-built Templates: Accelerates development by providing templates and best practices for common use cases (think document summarization, formatting documents, summarizing transcripts to generate minutes of the meeting, etc.) By focusing on reusability, organizations can significantly accelerate the development of GenAI use cases. While the exact impact would vary by org size and complexity, reusability through an Enterprise GenAI Platform should lead to substantial efficiency gains.   💬 How are you currently managing resource allocation & fragmentation across different lines of business when developing GenAI solutions?   📢 Subscribe to my newsletter for more insights: https://lnkd.in/g3bdneR7   #aws #ai #EnterpriseAI #GenAI #Innovation #AIReusability #MLOps #AIPlatform #DigitalTransformation #AIStrategy #TechLeadership #FutureOfAI #GenerativeAI #cio #ciso #cto #cdo

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We are thrilled to announce that our upcoming TECH WEEK by a16z event is going to be bigger than ever before! We’ve partnered with amazing women-led brands like The Fourth Effect with Breen Sullivan and Katrin de Haën , zant, and 2FIT BRANDS to bring you an unforgettable experience. Furthermore, we're adding two sessions on advancements in #AI and women's health to foster insightful discussions and exchange ideas. Join us for an evening of networking and deep dives into #GenAI during #NYTechWeek, personalized women’s health empowerment, and investment opportunities. We're excited to offer you the opportunity to join two exclusive sessions led by Marina Vieva, an active participant in the Bay Area community and leader in women's health. These sessions are packed with insider information, innovative solutions, and engaging discussions with industry leaders. Don’t miss out—spaces are limited to ensure interactive conversations! Session 1: GenAI Trends, Silicon Valley Vision, Concerns, and Opportunities Get insider access to the latest GenAI trends, visions from Silicon Valley, and the concerns and opportunities shaping the future. Marina Vieva will share her unique insights, and you'll have the chance to engage in a lively discussion with industry players. Session 2: Women's Health Perspective on GenAI and Future Advancements Discover how GenAI can revolutionize women's health. Marina Vieva will present innovative solutions and a forward-looking vision for the future of women's health. Participate in stimulating discussions and connect with market leaders. Sign Up Now! https://lu.ma/6esiktxd Future of AI: New horizons in Digital Health. Discussion, brainstorming, networking. NY #TechWeek · Luma #GenerativeAI #LeadershipInAI #WomenInTech #AIInnovation #FutureOfAI #TechSummit #NYTechWeek #AILeadership #AIConference #AI2024 #WomenInLeadership #AIRevolution #AICommunity #InspireAI #TechTalks #MarinaVieva #FemiSpace #AIEvent #AIInsights #TheForthEffect #Zant #2FITBRANDS

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First post-PE move by Surescripts is to open up their Medication History for Populations product to payers. This product is not their medication history query that many EHR users are familiar with. Instead, customers enroll/subscribe their populations and receive updates whenever a new prescription is dispensed (or missed). It's also FHIR native (although it was STU3 when I last checked) whereas the medication history query is NCPDP. It's more a strategic/legal move rather than a pure product one, seemingly. I'm curious if they always had data right to share data from pharmacies (this product, at least historically, pulls from pharmacies and not PBMs) for non-Treatment use cases or if they had to make legal changes/negotiations behind the scenes to accomplish this. https://lnkd.in/g3PyG6qE

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In digital health, success isn’t just about closing deals to get revenue—it’s about staying hyper-laser focused on what truly matters. Too often, companies fall into the trap of chasing quick wins, sacrificing long-term vision for short-term gains. But in this industry, where patient care, outcomes, and trust are at the heart of everything we do, we can’t afford to lose sight of our mission. It takes real discipline to grind through the noise and stay focused on the metrics that matter: improving patient outcomes, driving engagement, and creating value that lasts. Those are the indicators of long-term success, not just revenue growth. The hard work is in resisting distractions and staying committed to solving real problems. The grind is constant, but the impact we can make—on healthcare systems, individuals, and society—is worth every bit of it. Let’s keep building with purpose. Longterm revenue growth will come. ❤️ #DigitalHealth #FocusOnWhatMatters #HealthcareInnovation #LongTermSuccess #hlth

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𝗪𝗵𝗮𝘁 𝗿𝗼𝗹𝗲 𝗱𝗼𝗲𝘀 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘆 𝗶𝗻 𝗵𝗲𝗹𝗽𝗶𝗻𝗴 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝘃𝗮𝗹𝘂𝗲-𝗯𝗮𝘀𝗲𝗱 𝗰𝗮𝗿𝗲? 🌐 As healthcare transitions from fee-for-service to value-based care, the 𝗶𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 of data becomes 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹. Platforms like Arcadia.io 𝗮𝗿𝗲 𝗽𝗹𝗮𝘆𝗶𝗻𝗴 𝗮 𝗽𝗶𝘃𝗼𝘁𝗮𝗹 𝗿𝗼𝗹𝗲 by leveraging 𝗙𝗛𝗜𝗥 (Fast Healthcare Interoperability Resources) to enable real-time, 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲-𝘄𝗶𝗱𝗲 𝗱𝗮𝘁𝗮 𝗳𝗹𝗼𝘄. This not only breaks down silos but provides actionable insights for predictive analytics and clinical decision support. 🔍 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀), integrated into healthcare data systems, are further transforming personalized care by offering tailored suggestions to clinicians and identifying high-risk patients long before conditions manifest. AI-powered clinical decision support can streamline workflows, all while reducing costs. ➡ Is your organization already harnessing the potential of FHIR and AI to elevate patient outcomes? If not, how do you foresee these technologies evolving your healthcare delivery? Learn more about 𝗔𝗶-powered solutions driving value-based care: [Arcadia.io](https://www.arcadia.io/) #HealthcareIT #Interoperability #ArtificalIntelligence #ValueBasedCare #AI #FHIR #PredictiveAnalytics

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