Philip Meier

At Iodine Software we have been harnessing the power of AI for a long time to reduce the administrative toil and waste in our healthcare system. Take a few minutes to tune in and listen to this podcast from William Chan, our Founder and CEO to learn how AI can be leveraged responsibly to address key pain points in the mid revenue cycle #HealthcareAI #healthcareinnovation

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One of the most important concepts we introduced in our recent Healthcare AI roadmap is the notion of “Modality—Business Model—Market fit.” This concept describes how the delivery method and business model of AI in healthcare directly shape its potential to create value. There are many possible combinations of modalities and business models in healthcare. AI can be delivered as software, copilots, agents, services, diagnostics, or therapeutics, supported by business models ranging from usage-based and volumetric pricing to performance-based and shared savings arrangements. Nailing selection of modality and business model is essential for AI companies seeking large market opportunities and profitable operations. As we describe in an example with computer vision-based diabetic retinopathy screening, modality and business model selection can drive a difference in TAM by upwards of 25x, and has serious implications for overall margin structures. Read more here 👉 https://lnkd.in/eK7rjpJz

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Incredibly excited to share our updated Healthcare AI thesis. This is not a market map – in 4,000 words, we dive deeply into the research, products, business models, and market entry points that are reimagining healthcare with AI. More specifically, we cover: • Translating AI research into real-world products • Mapping modalities (software, copilots, agents, services, diagnostics, therapeutics) to problems in healthcare • Designing AI business models that unlock billion-dollar markets • Closing current gaps in healthcare AI infrastructure • Distinguishing AI platforms from point solutions Let us know what you think ⤵️

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🚀 Exciting New Development in Healthcare Tech: Structured Outputs in AI APIs 🚀 As we continue to build the future of consumer healthcare, tools like AI are reshaping how we deliver better, more personalized care. The recent announcement of Structured Outputs in the API—with models now adhering reliably to JSON schemas—marks a huge step forward in healthcare tech innovation. In consumer healthcare, where accuracy and data integrity are critical, structured outputs mean more reliable decision-making. Imagine a seamless experience where every touch point—whether it’s symptom tracking, medication reminders, or patient triage—is driven by precise, structured, and actionable insights. Here’s why this matters: ✅ Faster integration with healthcare systems like EHRs, reducing delays in patient care. ✅ Improved clinical accuracy, with AI-generated outputs adhering to standard formats. ✅ Data compliance and security, ensuring patient information is handled more responsibly. ✅ Real-time patient insights, fueling better care and outcomes in telemedicine and remote care services. As the CTO and founder of several healthcare tech startups and consumer healthcare brands in the UK, I’m constantly seeking the latest advancements that will push the boundaries of how we care for patients. Structured outputs from AI models promise to revolutionize everything from telehealth consultations to chronic disease management. #HealthcareTech #ConsumerHealth #AIinHealthcare #Innovation #DigitalHealth

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Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Clinician Assistant • Suki is partnering with Zoom to generate clinical notes as well as other integrated AI features.    • Net Health announced it would integrate AI ambient scribe Tali AI. • Aidoc is partnering with NVIDIA to create a guideline to accelerate AI adoption, the Blueprint for Resilient Integration and Deployment of Guided Excellence (BRIDGE). 📞 AI Contact Center • Authenticx released a new AI assistant that helps users uncover business insights from their data. • Infinitus Systems, Inc. closed a $51.5M Series C with participation from Memorial Hermann Health System and others.   🏥 AI Facility Management • Cognosos, Inc. acquired Cox Prosight. 🌡️ AI Remote Patient Monitoring and Triage • Biofourmis has merged with CopilotIQ. 📈 Data & Analytics • Avandra Imaging launched out of stealth; it plans to sell to biopharma, medical research, and AI companies. • Gradient Health acquired DataAppraisal. • HealthEx launched and announced $14M in Seed and Series A funding. 📋 Patient Administration • Notable released Flow Builder, which allows users to develop their own AI agents for operational workflows within EHRs and other systems. • MIla Health Inc released its flagship AI solution of the same name, and announced implementations at provider organizations including Aayu Clinic, MIMIT Health, and Best Practices Inpatient Care. 🔁 RCM • Oracle’s patient accounting solution has been integrated with FinThrive, which enables improved functionality, performance, and a unified login experience. • Blue Shield of California and Salesforce are partnering on a solution that promises to give patients prior auth responses within the course of their appointment. ⚙️ Clinical Operations • Dyania Health closed a $10M Series A, including participation from Cleveland Clinic Ventures. 📱 Patient Support & Communications • Psych Hub released a new solution designed to support patients seeking mental health support, directing them to both non-clinical and clinical resources, thereby assisting behavioral health providers to reach new patients. • Mayo Clinic has partnered with Lin Health to support patients after treatment for fibromyalgia.   🍎 VBC • WellSky acquired Bonafide Health, allowing it to provide a more integrated platform for providers to run their businesses. 🧠 Food For Thought • 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘁𝗼 𝗸𝗲𝗲𝗽 𝘂𝗽 𝘄𝗶𝘁𝗵 𝗮𝗹𝗹 𝘁𝗵𝗲 𝗔𝗜 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀? Andrew Hines, CTO at Canvas Medical, shared a breakdown (plus some hot takes) on all of the many entities currently vying for market share in the AI governance space https://lnkd.in/eMNpXw4t --- Want the latest healthcare tech news delivered to your inbox each week? Sign up here 📬 👉 https://lnkd.in/e2UJ3ckP

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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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🌟 7 Crucial Elements for Scaling Startups to Successful Exits 🌟 Reflecting on my recent experience at the OPEN Silicon Valley Forum in Santa Clara, some of us engaged in a deep discussion on what repeatedly drives startups from inception to successful exits. Our collective experience pointed to these seven essential elements: 1️⃣ Timing is Everything: Ensure the market and technology are ready for your solution. Being too early or too late can hinder success. 2️⃣ Align with Your "Why": Success requires internal motivation. Knowing your “why” keeps you resilient and purpose-driven. 3️⃣ Master the Problem, Not Just the Product: Deep understanding of the problem is far more critical than focusing solely on a product or idea. 4️⃣ Define Culture from the Start: The first five hires set the tone. Invest in "A" players who embody the culture you envision. 5️⃣ Obsess Over User Experience: A relentless focus on user experience can make or break your product’s adoption. 6️⃣ Build for Scale from Day One: Architecting for growth is not optional—it’s essential for long-term success. 7️⃣ Map Out Your Go-to-Market Strategy Early: Before building, have a clear thesis on how to generate revenue and achieve product-market fit. These principles are tried and true across multiple industries. If you’re launching a startup or scaling a business, how do these resonate with your experience? What would you add to the list? #startups #entrepreneurship #growth #scaling #productmarketfit #OPENsiliconvalley #innovation #founders Tashfeen Suleman Amer Haider Umair Khan Junaid Qurashi Wardah Inam

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🎬 Watch Anna in Action! Our latest demo video showcases how Anna orchestrates complex data to deliver actionable insights in real-time. 🚀 Don’t miss out—watch now! 👇 ▶️ https://lnkd.in/e_fFV8KB

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What are important benchmarks you use with LLMs with #AIinhealthcare? Can #LLM excel in medical encoding? Yes! But what is your measurement of success? That you found a billable code or is it the RIGHT billable code? Does it help you ALSO find the RIGHT non-clinical code because of Regulatory pressures like #USCORE and #USCDI? Or for Research, Adverse Events, Observational outcomes, etc...? Questions you should be asking yourself in measurements of success: - Did you benchmark manual review vs LLM review? Was the encoded encounter the same code that left the organization on the bill? Was there a human in the loop to confirm its accuracy? - Was the encoded encounter changed from point of care to #CDI? Did CDI recommend a change after LLM? - Is the LLM recommending other code sets for the encounter because encoding is only a portion of the clinician's need? Are they the right codes or purely a crosswalk? - Did the LLM provide you an international version of your encoding requirement or did they give you a US version (AKA training Hallucination)? Here at IMO Health, we leverage terminology services as an expert system to couple IMO Clinical AI to train LLMs. This greatly improves the accuracy and support of medical encoding and supporting the overall F-Score of what truly occurred during the encounter. If you’re curious about how technology can positively impact medical coding accuracy, this read is for you. https://bit.ly/3VBZFqn  #DataQuality #NLP #ClinicalAI #ClinicalTerminology

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Let's talk AI with payers and providers—some interesting numbers to consider from the latest research (Sept 2024) (KLAS Research & Bain) Here are four eye-opening takeaways: 1. AI Strategy Surge: • 15% of providers now have an AI strategy, tripling from 5% last year • 25% of payers are leading the pack with established AI strategies • Both sides are bullish on generative AI's potential 2. Tech Spending Spree: • 75% of surveyed orgs boosted IT investments • Top priorities: cybersecurity, clinical workflow optimization, and data platforms • 70% of orgs doubling down on security 3. Payer Priorities: • Focus on care coordination, utilization management, and claims processing • 65% grappling with legacy tech challenges • Trend towards streamlining tech stacks and favoring existing vendors 4. AI Adoption Hurdles: • Regulatory and legal concerns top the list • Cost and accuracy issues (like AI hallucinations) are giving pause • Growing need for robust governance and ethical frameworks The report here: https://lnkd.in/gxjKPUaW Is your organization leading the charge or playing catch-up? #HealthcareAI #TechInnovation #FutureOfHealthcare

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I'm seeing a lot of "hot takes" about the The New York Times article* discussing autonomous AI being better than hybrid (human+AI) in clinical settings, where the latter has been dogma for a long time (based on a neat paper published in JAMA**. There have been a few other similar studies published recently. The Google AMIE*** paper is my personal favourite!). *https://lnkd.in/e5pGzfyY **https://lnkd.in/ed5Je3p5 ***https://lnkd.in/eVrs3p7G All I'm going to say is... experimental paradigms are important. And if that's too subtle, then please refer to the image below (which I first saw in a phenomenal MS/Neurodegen lab I worked in during medical school... if you ever want to chat oxidative stress and eIF2α, shoot me a message). Okay, back to work. P.S. I'm not saying autonomous isn't > hybrid, but rather the studies to date don't *prove* anything. No need for the existential panic, just yet. We've still got the hard part left, i.e. actually generating some real world evidence based on an outcome that human beings care about...

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Healthcare tends to lag behind in tech adoption, but that might be its biggest strength as AI transforms our industry. Julie Yoo, General Partner at a16z, recently made a compelling point: Unlike other industries, healthcare isn’t weighed down by billions of dollars of investment in legacy software systems. This lack of "sunk-cost bias" means we have a unique opportunity to leapfrog directly into the benefits of AI without the burden of ripping out decade-old tools. Our industry is slow to adapt not because of a lack of awareness, but because the mindset is deeply rooted in established ways of working. The key to advancement lies in shifting this mindset. Luckily, this opportunity couldn’t come at a better time. Healthcare is facing a critical workforce shortage, with hundreds of thousands of doctors and nurses short of what’s needed to meet growing demand. AI can help fill this gap by scaling clinical expertise, making certain that the best practices are shared widely and that every patient receives the same high standard of care, regardless of location. However, none of this matters if we don’t build trust in the AI tools being developed. Healthcare already has strong regulatory frameworks, with the FDA approving hundreds of AI-driven clinical products. But trust is built slowly, and the human element must remain central to any transformation. At PROXDIS, we’re focused on how AI can drive positive change, without sacrificing the relationships and trust that are the core of patient care. Technology has incredible potential for improving care, but it can never replace the relationships and trust that are at the heart of patient care. As Julie pointed out, healthcare can use this opportunity to jump ahead—but only if we guarantee that this innovation is always centered around the real needs of providers and patients alike. Are we ready to adapt to AI at a pace that allows for meaningful change while keeping the human element front and center? Let me know your thoughts.

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Presenting at the Senate as part of the Coalition for Health AI (CHAI) "Day on the Hill" wasn't on my 2024 bingo card, but I'm feeling energized by the bipartisan discussions on healthcare AI. The numbers tell a compelling story: while nearly 2,000 healthcare AI startups have received funding over the last two decades, only 1 in 20 provider-facing companies reach late-stage maturity. Beyond natural market dynamics, two critical barriers are holding back AI's full potential in healthcare: unsustainable payment models and inadequate evaluation infrastructure – only 5% of research studies on LLMs in medicine utilize real patient care data, and studies advancing our understanding of performance beyond medical licensing exam benchmarks remain anemic at best. At CHAI, we're advocating for essential foundations: • Rigorous technical model performance evaluations • Comprehensive bias assessments across diverse patient populations • Thorough pre-deployment simulations and prospective studies • Robust ongoing local monitoring The path forward demands strong reporting infrastructure, advanced healthcare AI benchmarks, and meaningful public-private partnerships to drive real-world AI deployment in healthcare. Stay tuned for CHAI's upcoming initiatives in 2025 as we expand our engagements with the startup and investment ecosystem. https://lnkd.in/enw-GQcA

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As large language models (LLMs) gain traction in healthcare, the potential to streamline and automate processes—like converting unstructured clinical data into organized notes—seems incredibly appealing. However, the temptation to blindly rely on these models without fully understanding their limitations is risky. LLMs can produce outputs that appear highly credible yet may contain significant errors, known as "hallucinations," or omit critical information. In the clinical context, where accuracy is paramount, even small mistakes in AI-generated summaries could lead to severe consequences for patient care. In "Safety Principles for Medical Summarization Using Generative AI," Dr Dillon Obika, Christopher Kelly, Nicola Ding, Chris Farrance, and their team delve into these pressing safety concerns. Their work explores how LLMs like MedLM, designed for healthcare applications, can indeed transform workflows but must be used cautiously. Key risks include the generation of incorrect information, omission of essential details, and the introduction of biases, each of which could adversely impact patient safety if not carefully managed. The paper outlines a structured approach to managing these risks, emphasizing four key safety considerations: Intended Purpose: Clearly defining the purpose of an LLM in healthcare is essential for guiding safety assessments and usage protocols. For MedLM, the initial use case was restricted to creating draft summaries for clinician review, helping ensure outputs are vetted by healthcare professionals. Risk Identification: Specific risks, such as ways LLM outputs could harm patients, must be rigorously identified. This includes technical and ethical reviews, covering aspects like model transparency, interpretability, and bias mitigation. Risk Evaluation: Quantifying and assessing the likelihood and severity of potential risks can be challenging due to the varied settings in which these systems may be used. A multi-layered evaluation approach is recommended to reflect the real-world interplay between AI and healthcare professionals. Risk Mitigation and Monitoring: To address hazards like hallucinations, models should be paired with robust mitigations, including labeling and the use of model cards and guides to improve transparency. Continuous post-deployment monitoring is critical, as new risks may emerge over time. Guided by these principles and Google’s AI Principles, the paper advocates a cautious, phased approach to LLM deployment in healthcare. To me, this paper is interesting because it underscores the need for clear, proactive risk management as AI becomes more integrated into healthcare workflows. This is important because the tendency seems to be relying blindly on these tools in many domains. However, in healthcare, the consequences of doing so might be too costly. Paper: https://rdcu.be/dXF4o #AI #Healthcare #AIHealthcare #LLM

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To follow up on Dede Ainbinder recent post: If we use the current EHR data to build a AI clinical decision support tool it is unlikely to capture our clinical decision making process. EHR's are inherently designed to maximize billing and minimize risk of litigation. A clinical decision support tool based on this data will likely do the same. There is a large sunken cost fallacy here where we continue to hang on to garbage data. Just because the data gathering and storage process was capital and time intensive doesnt mean the data is valuable. To truly capture the clinical decision process we need to capture data which sits outside billing and litigation. Its hard to imagine a future where this data isnt used for billing and cannot be called upon during litigation. #EHR #healthdata #clinicaldecision

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What we learned at the AVIA Summit this week! We are at a tipping point where #technology has caught up with the needs that health systems are facing. 1. #AI is only as good as the workflows, data, and the problems you are trying to solve. Patient and staff experience MUST be at the forefront of any efforts to improve experience, not cause more friction. #humancentereddesign 2. Health Systems are only piloting this #technology and scaling is difficult because of the #complexity for each workflow. ( #clinicaldocumentation #clinicaldecisionsupport ). It's important to become autonomous #agile digital enterprises to drive the work forward. 3. AVIA is here to #accelerate your efforts from #aistrategy to #execution. We are experts with #digitaltransformation and what it takes to embed into workflows and navigating #changemanagement . It's time for #radicalreinvention! We are experts and passionate acceleration partners! Linda Finkel Eric Langshur Ted Meisel Patti Sweeney Melissa Masopust

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