Adam Berger

This California Management Review article explores the ROI of AI ethics and governance investments through two lenses: loss aversion and value generation. While many companies focus on minimizing risks like legal penalties and reputational harm, the article argues that a value generation approach—using ethics to build trust, foster innovation, and create competitive advantages—offers greater long-term benefits. By integrating ethics into strategic planning, organizations can both mitigate risks and enhance overall growth.

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Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

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In case you missed it.... We published a great H:F guest episode with Joel Portice, CEO of egnite - delving into the transformative role of data and AI in revolutionizing cardiovascular care. Joel shares his journey of leading five successful healthcare data companies, including the genesis and rapid growth of egnite, which was spun out from Edwards Life Sciences. egnite's flagship platform, Cardio Care, uses advanced analytics, machine learning, and AI to identify and manage cardiovascular patients, saving lives daily. This episode is a strong addition to our Heart Health Series on Health:Further. Please check it out: https://bit.ly/4fKmzCH

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Though benchmarks for medical AI are still imperfect, there's no question that AI will continue to march towards diagnostic excellence. These trends will place greater emphasis on physician-scientists – those who advance medical knowledge via closed loop clinic/lab interfaces, which can then feed back into large-scale AI systems that serve entire populations. This was all part of the OG vision for the learning health system. source: https://lnkd.in/eh2RM72s

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Unlocking Healthcare Insights with AI: 5 Steps to the Right Approach for LLMs Over my 17+ years in IT, working with many organizations I've witnessed how transformative AI can be useful in healthcare. Large Language Models (LLMs) are now revolutionizing patient care by providing deeper insights into population health, predicting disease trends, and personalizing treatment plans. 🏥 However, implementing LLMs effectively isn't just about adopting new technology—it's about finding the right and cost-effective approach. Here are some steps I've found valuable: 1️⃣ Leverage Existing Infrastructure: Utilize your current systems and integrate LLMs without a complete overhaul, saving costs and resources. 2️⃣ Ensure Compliance from the Start: Align with HIPAA and HITECH regulations early to avoid costly adjustments later. 3️⃣ Embrace Interoperability Standards: Implement HL7 and FHIR protocols for seamless data exchange between systems, reducing integration expenses. 4️⃣ Start Small and Scale Responsibly: Begin with pilot projects to demonstrate value before investing heavily, minimizing financial risk. 5️⃣ Collaborate with Experienced Partners: Work with experts who understand both the technical and regulatory landscapes to navigate complexities efficiently. At Arihant Technologies, we combine deep industry experience with practical solutions to help healthcare organizations adopt AI in a way that's both effective and budget-friendly. #ArihantHealthTech #ArihantTechnologies #healthcare #ai #llms #hipaa #hitech #interoperability #costeffective #experiencematters #healthtech #populationhealth #patientcare #dataanalytics #healthcareinnovation #fhir #hl7 #compliance #digitalhealth #medicalai #affordableai

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Some of the questions I've been thinking about lately and discussing with researchers/founders: LLM OS - If GPU-as-a-service is a race to the bottom, how do AI inference clouds differentiate beyond fine-tuning-as-a-service? - Firework has opted to specialise in optimising small models for specific use cases using quantisation and better function-calling models, delivering a better mix of cost, latency and accuracy Model Architectures - Deepseek's success with Mixture of Experts, as well as Snowflake's hybrid Dense-MoE, suggested MoE is a durable architecture to scale performance. Llama on the other hand eschewed MoE for a straightforward dense architecture, famously overtraining well beyond Chinchilla Optimal - How will transformer-specific infra (e.g. AI ASICs like Etched) defend its lead relative to alternatives like State Space Models? And more: https://lnkd.in/ehD4nvQ4

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Best practices in AI governance. Panel discussion by Keerthika Melissa Subramanian and Matt DeVoe. l Thank you to Keerthika Melissa Subramanian, partner at.Winston & Strawn LLP, and Matt DeVoe, Chief Information security officer at Zoetis, for sharing their insights and learnings with the students at the #CDAIO program at Executive Education at Carnegie Mellon University’s Heinz College. In a wide ranging discussion, these leaders shared their perspectives on - The need for AI governance - How to integrate AI governance into a broader digital and data risk management - Emerging AI regulations - Achieving the balance between unlocking value from innovation while minimizing risk - The ways to improve the legal.and compliance fluency of AI and Data leaders https://lnkd.in/eUWq83EY

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𝐓𝐡𝐞 𝐍𝐞𝐱𝐭 𝐁𝐢𝐥𝐥𝐢𝐨𝐧-𝐃𝐨𝐥𝐥𝐚𝐫 𝐀𝐈 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐖𝐢𝐥𝐥 𝐁𝐞 𝐢𝐧 𝐀𝐩𝐩𝐬 🚀 Like cloud and mobile revolutions gave rise to giants like Meta and Uber, AI's next $1B+ companies will emerge at the application layer. With foundational players like OpenAI and AWS stabilising, the focus shifts to apps with advanced agentic capabilities—reasoning, problem-solving, and delivering outcomes. 💡 What industries stand to benefit most from agentic AI apps? 💡 Are we ready for the cognitive leap in applications? 💡 What role will trust and ethics play in their adoption? #AI #Innovation #AgenticApps #FutureOfTech

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Exclusive: Health-care AI startup Suki on Wednesday announced it's building patient summary and Q&A features with Google Cloud as it looks to push beyond clinical documentation. Suki’s flagship product, called Suki Assistant, allows doctors to record their visits with patients and automatically turn them into clinical notes, helping physicians avoid the headache of manually writing out all of that information. The new features with Google Cloud will allow Suki to provide clinicians with more assistive tech as they care for patients, the startup said. It is the next frontier for the seven-year-old company. “We were never really building a clinical documentation tool only, it was supposed to be an assistant,” Punit Singh Soni, the founder and CEO of Suki, told CNBC. “An assistant can help you with documentation, but it can also start doing other things.” More details here!

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The final ten minutes of Scott Galloway and Ed Olsen's latest (and highly addictive) Prof G podcast (https://lnkd.in/d6eKdJFt) explores the explosion of live-streaming—how it evolved from a niche gaming activity into a mainstream phenomenon. What I love is the analogy that streaming is like watching a football match surrounded by fans, while recorded content is like watching a game in an empty stadium. It's the live feedback, shared experience, and connection to the talent that makes streaming so incredibly engaging. For years, gamers have created the perfect storm of content, community, and chaos. They pioneered the art of keeping audiences glued—not just through gameplay, but through personality, banter, and live reactions. As Scott put it, "It's like going direct-to-consumer, cutting out the middleman, and letting fans drive the show." Gamers were ahead of the curve long before anyone else caught on. In 2024 1.4 billion hours of streaming was watched in the UK alone. If you're interested in what's happening next, do as Dr G orders: pay attention to gaming. Nabil Moutran Dan Moore Joe Abboud

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I came across an interesting event that might pique your interest in the world of Decentralized AI. Check it out: https://lu.ma/j0uxdk4o. Let me know if you’re keen to join! #DecentralizeAI ScaleUp Labs

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Artificial intelligence (AI) holds immense promise for transforming numerous fields, from healthcare and finance to education and transportation. The potential for AI to contribute to the medical field is particularly compelling. Given its ability to process vast amounts of data and identify complex patterns, AI could play a significant role in accelerating the discovery of treatments for diseases that have previously defied conventional medical approaches, such as cancer and AIDS. By leveraging AI's capabilities, researchers may be able to uncover potential solutions that could lead to groundbreaking advancements in the treatment of these challenging and persistent health issues.ArtificialIntelligence #MedicalResearch #HealthcareInnovation #DataProcessing #DiseaseTreatment #ResearchAdvancements

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We're trying a new thing: Company spotlights to help people better understand what various health IT vendors actually do 🔦 We’ll be cutting through the marketing speak to give short, to-the-point descriptions of a company’s product and/or service + some context on how they see their differentiators. Tag any companies in the comments you'd like to see us cover (and follow Elion if you want to stay in the loop!)

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Generative AI is spreading like wildfire. 💡 Nearly 40% of US adults have embraced it in just months - outpacing the adoption of PCs and the internet. This tech revolution is reshaping how we work and live. Are you ready? #GenerativeAI #TechAdoption #FutureOfWork #AIInnovation #DigitalTransformation What's your experience with generative AI? Share your thoughts below!

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Your AI investment could be underperforming by 63% due to one commonly overlooked factor - proper LLM training. #1 What's the big deal? Goldman Sachs projects generative AI could drive $7 trillion in economic value over the next decade, but many organizations are discovering their initial LLM implementations aren't delivering expected results. According to McKinsey, 65% of organizations now use generative AI, yet 63% report significant accuracy and quality issues with their models. The root cause? Many companies try to minimize costs by skimping on proper LLM training and evaluation, leading to poor performance and downstream business impacts. #2 Why this is important? For business leaders investing in AI, this highlights a critical truth: LLM training isn't a place to cut corners if you want sustainable ROI. Proper model training, evaluation, and refinement are essential for transforming an underperforming AI system into a true competitive advantage. This requires a systematic approach involving third-party evaluation, strategic data sourcing, continuous performance monitoring, and domain-specific expertise. Organizations that treat LLM training as a strategic investment rather than a cost center are better positioned to capture the technology's full business value and avoid costly implementation failures that can erode stakeholder confidence. Source: "Maximizing Your LLM ROI: The True Costs of Poor Strategy, Evaluation and Training", Turing Consulting, https://lnkd.in/eMdcKGk8 #AI #GenerativeAI #BusinessStrategy #DigitalTransformation #Leadership

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New study suggests that gpt-o1 achieves "superhuman performance" on clinical benchmarks that include differential diagnosis, diagnostic reasoning, and crafting care management plans: * On Management Decisions, o1 outperformed both GPT-4 and clinicians by ~40% in generating accurate and actionable management plans. * On differential diagnosis, o1 included the correct diagnosis in 78.3% of cases, and its first suggestion was correct in 52% of cases. * On 70 diagnostic cases, o1 produced a response with an exact or very close correct response in 88.6% of cases. This was done with relatively simple zero-shot prompts. During the early 20th century, x-ray machines gradually went from "technical novelty" to "it's unethical to diagnose without them" for a range of medical conditions. It would be no surprise if the industry went through a similar transition to require use of medical generative AI over the next 2-3 decades. https://lnkd.in/gd3RhFbr #ai #generativeai #healthcare #healthcareai #healthai #datascience

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HeAR is now available to researchers to help accelerate development of custom bioacoustic models with less data, setup and computation. 🚀 Our goal is to enable further research into models for specific conditions and populations, even if data is sparse or if cost or compute barriers exist. #HeAR #accousticdata #bioaccousticmodels

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