Matteo Zanella

Finally - a production-ready agent framework that does not require babysitting (mostly). OpenFang presents itself as the first genuinely production-ready alternative to OpenClaw and its numerous competitors. While most agent frameworks remain heavy, demo-focused systems requiring constant human oversight, OpenFang takes a fundamentally different approach by treating agents as autonomous workers rather than chat companions. Built entirely in Rust, it ships as a single 32MB binary with 180ms cold start times, enabling multiple agents to run continuously on modest hardware without the bloat typical of Python-based alternatives like LangGraph or CrewAI. The standout feature is OpenFang's "Hands" concept - seven pre-built autonomous agents that bundle capabilities, system prompts, domain expertise, and guardrails into independent packages. These agents can activate on schedules, conduct research, generate leads, and manage multi-channel communications across 40 different platforms without requiring babysitting. The security architecture is surprisingly robust for a pre-1.0 project, featuring 16 discrete security layers including WASM sandboxing and Merkle hash-chain audit trails. Check out the article here: https://www.openfang.sh/ If you like this content, consider subscribing to my weekly newsletter where I share 3 key events in the data and AI space, 2 BigQuery tips, 1 thing that piqued my curiosity - completely FREE. Follow the link below to sign up.☟ www.beardeddata.com/signup #AIAgents #Rust #ProductionAI #Automation

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The Fragmentation Problem in Healthcare Program Integrity (some thoughts after attending the HPRI Chicago 2025) One thing that struck me at HPRI Chicago was how payment integrity and FWA teams often don't talk to each other. They're frequently reporting to different leaders, working on what are really sub-puzzles of one problem, but approaching them from completely different angles. Part of what keeps these teams apart is all the jargon we've built up around what should be straightforward work. Pre-payment, post-payment, claim edits, prior authorization, utilization management, data-mining - these terms have become more like barriers that facilitate the divide between teams and technologies and point solutions. I think in the end, the task is as simple as: find things that don't look right, and if they're not right, make them right. The level of expertise in this field ranges widely - from people who mostly repeat buzzwords to those who actually understand the underlying problems. It's always enlightening to find people I'd like to learn from and showing lots of their passion, but I think the average level isn't quite there. Correction. Let's say it is not that they are not good at what they do. They know their domain well enough, but it's hard for them to cross over into other areas. The real difficulty of having a comprehensive view of program integrity is that it requires a combination of medical expertise, policy expertise, analytics, informatics, and cybersecurity. It's often difficult to excel in just one vertical, but program integrity demands all of these at a high level. Sometimes this work is run by medical officers who understand data and policy, sometimes by data experts who grasp the other pieces. But finding that combination isn't easy. The industry's immaturity shows in the lack of senior leadership dedicated solely to program integrity at most payers. This absence of executive accountability reflects how the field still treats program integrity as separate functions rather than an integrated discipline requiring unified leadership. While CMS and other regulators are pushing for more integrated approaches to program integrity, the fragmented structure makes it harder for organizations to respond cohesively to regulatory expectations and reporting requirements. This makes me think there may need to be a leadership position called Chief Program Integrity Officer - someone who oversees the correctness and compliance of all transactions and records. It's a bit like an extended concept of Chief Privacy Officer, but broader. They'd need to understand the business of health insurance regarding policy and medical practices and data, and most importantly, they'd need to understand how it all should work together. What are your thoughts? Is it about time to have Chief Program Integrity Officer in payers? Image credit: Gemini

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Clinicians want to use AI but don’t have the bandwidth to evaluate all the different models. Meet Health Olymp, an AI tool purpose-built for clinicians. Here’s why this matters 👇 1. Health and life science experts gain instant access to frontier models for free. No procurement hoops or IT bottlenecks. Every clinician gets their own sandbox to explore. 2. Clinicians can compare models side by side and pick the one that works best for their use case. That transparency builds trust and sparks ideas for novel AI applications in care delivery, diagnostics, and research. 3. Most AI benchmarks are too generic or disconnected from clinical reality. Health Olymp brings frontline feedback into the loop, helping the industry identify models that perform reliably in real world clinical environments. Congrats to Ruslan Nazarenko and the Lumos team as they put AI model benchmarking directly into the hands of healthcare professionals. ⬇️ (link in comments)

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*The $30+ Billion Consulting Paradox That's Failing American Health* Healthcare payers are hemorrhaging billions on consulting firms that deliver the same recycled PowerPoints while our system burns through 20% of GDP with nothing to show for it. *The brutal math:* •⁠ ⁠US healthcare spending: $4.5 trillion annually (20% of GDP) •⁠ ⁠Major payers spend: $20-40B+ on consulting annually   •⁠ ⁠Health outcomes ranking: 46th globally, behind Cuba and Costa Rica •⁠ ⁠Life expectancy: Declining for 3 consecutive years pre-pandemic Yet every major payer gets sold the same "transformative solutions": → Digital transformation roadmaps (that look identical across competitors) → Member engagement strategies (copied from 2019 best practices) → Cost containment frameworks (that somehow never contain costs) → AI/ML implementations (that automate the wrong processes) *Here's what $30 billion in consulting actually bought us:* •⁠ ⁠More administrative complexity •⁠ ⁠Longer prior authorization delays   •⁠ ⁠Higher member friction •⁠ ⁠Zero improvement in clinical outcomes •⁠ ⁠The same broken fee-for-service incentives, just digitized Meanwhile, direct primary care practices operate at 80% lower costs with better patient satisfaction. Value-based care pilots show 30-40% cost reductions with improved outcomes. Community health centers deliver preventive care for pennies on the dollar. *The real transformation won't come from consultants.* It will come from payers who stop buying expensive reassurance and start funding actual innovation: •⁠ ⁠Investing in longitudinal care relationships •⁠ ⁠Eliminating administrative waste, not automating it •⁠ ⁠Rewarding prevention over procedures •⁠ ⁠Building partnerships with community providers who actually know their patients American healthcare doesn't need another consulting framework. It needs leaders and partners willing to abandon profitable complexity for effective simplicity. #Healthcare #HealthcareAI #ValueBasedCare #HealthTech #Leadership #AI #HealthcareData

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In 2020, Ron Li , Steve Asch, and Nigam Shah published a multidisciplinary framework for AI delivery science in healthcare (recently shared by Jan Beger). Five years later, I'm seeing a clear pattern: organizations that treat governance as a design principle, not a gate check, are consistently outpacing their peers. Why it works: embedding governance early reduces costly rework cycles that stall releases and drain budgets. While competitors navigate months-long review loops, teams that are compliant by design ship the next iteration. The playbook I keep seeing succeed: - Inventory all sensitive data via AI-driven discovery - Categorize models early under EU AI Act tiers, FDA SaMD rules, HIPAA - Embed privacy-preserving tech — redaction strategies, differential privacy, synthetic data, continuous monitoring, and remediation previews — wherever sharing is constrained The 2025 debate isn't whether to invest in AI governance. It's whether you'll use it to pull ahead of teams still bolting compliance on at the end. What governance tactic has saved your team the most time? I'd love to hear your thoughts and examples. Jan Beger, GE HealthCare, Ron Li , Steve Asch, and Nigam Shah

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Build for the 3 Waves of Innovation in Healthcare Healthcare is poised for a dramatic shift. It's not just incremental change; we're talking about a fundamental rewrite driven by three converging waves of innovation. Imagine the landscape of healthcare being completely reshaped, leaving no aspect untouched. This isn’t a gradual evolution; it's a rapid transformation. The 3 Waves of Innovation AI is accelerating everything from diagnostics to personalized treatment plans at an unprecedented pace. It's like having a supercharged research and development team at your fingertips. Simultaneously, Web3 is laying the groundwork for secure, decentralized data, effectively putting patient records back into the hands of the individual, much like how you built your businesses by empowering your teams. Finally, Biotech is pushing the boundaries of what's possible in human health, from gene editing to regenerative medicine. This convergence isn’t just adding up; it’s multiplying, creating a new paradigm for healthcare delivery. It's akin to identifying and capitalizing on a market disruption before anyone else. Healthcare access will be redefined, with remote diagnostics and personalized medicine reaching underserved populations. Professionals will evolve, with AI handling routine tasks, allowing them to focus on complex cases. Data-driven decisions will become the norm, with predictive analytics anticipating health crises before they occur. Picture a world where healthcare is proactive, not reactive. It's like moving from reactive problem-solving to proactive strategic planning, a skill you’ve honed over your career. A Builder’s Market We're moving beyond the Information Age, where data was king. Now, intelligence is the driving force. This shift will democratize knowledge and empower individuals and small teams to innovate, much like your own journey in building successful businesses. Millennials, with their unique perspectives, will lead this transformation, shaping healthcare for generations to come. This transition will be dynamic, and we need to be prepared. Elevate Health 2050 is designed to help navigate this change, providing the insights and connections needed to capitalize on these emerging trends. It's about staying ahead of the curve, something you understand well. How will these three waves of innovation impact your role in healthcare? What opportunities do you see emerging? Join the conversation and explore the possibilities of the Intelligence Age at the Elevate Health 2050 substack: https://bit.ly/4efm4BE Let's discuss and learn together. Join our community and discover how to leverage these innovations.

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In our latest newsletter, I go deep into why we believe the next three years of healthcare innovation will be defined by two specific trends: Voice Agents and Hybrid AI Care Models. Here is the quick breakdown: 🎤 Amplifier Health (Voice AI Diagnostics): Amit Mehta, Jeremy Barber, and Peh Keong are doing the impossible. They have built a Large Acoustic Model that can screen for multiple medical conditions from a phone call. By focusing on the physical properties of sound rather than just words, they are creating a screening and diagnostic tool that is accessible to anyone with a phone. 🧠 Meru Health (Hybrid AI-Enhanced Care): Kristian Ranta, Riku Lindholm, and Henri Koponen are solving the "efficacy crisis" in mental health. Their 12-week program treats the mind and body together by combining licensed clinicians with biofeedback technology. With millions of lives covered, they are proving that value-based care is the only way forward. The Bigger Picture: We believe the "Golden Age" of healthtech is moving toward a model where AI handled the routine data and diagnostics while humans focus on high-level clinical decisions. Whether it is a voice agent triaging a patient or an AI-enhanced clinician monitoring recovery, this hybrid approach is how we solve for both access and quality. Read the full breakdown of our investment thesis #HealthTech #VentureCapital #AI https://lnkd.in/dqDGBnJF

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Three incredible days at #JPM2026. Same conversation with 10+ health system execs: "We're building our AI strategy. Evaluating vendors. Need to pick the right partners." This sounds familiar. 2009-2012: The EHR decision. After the HITECH Act, health systems spent 18-24 months evaluating Epic vs. Cerner. Consultants. Committees. RFPs. Multi-year implementations. The logic made sense: EHRs were monolithic platforms with huge switching costs. You had to get it right. 2025: The AI decision. I'm watching health systems do the same thing. "AI steering committees." "Strategic AI partner" RFPs. Trying to pick winners. But there's a critical difference: EHRs were a single, massive platform decision. AI in healthcare is dozens of different problems that need dozens of different solutions. Here's what health systems are missing: They're looking for "the AI vendor" when they should be building "an AI portfolio." There's no single AI platform that solves:   → Documentation burden (ambient notes) → Radiology throughput (workflow optimization) → Clinical trials matching → Revenue cycle optimization (across specialties) → Clinical decision support These are different problems. Different technologies. Different vendors. Different timelines. Some need FDA clearance and 24-month validations. Some can pilot in 90 days. Some integrate with Epic. Some replace parts of Epic. You can't solve all of these with one "strategic AI partnership”....no matter that Anthropic is saying right now. <ducks> The AI landscape is evolving too fast for monogamous vendor relationships. 6 months ago: GPT-4 for clinical documentation. Today: Ambient listening, agentic workflows, multimodal imaging analysis. 6 months from now: Something else entirely. That's why you need exposure across the whole landscape—not a bet on one vendor. The mistake I see health systems making: Approach 1: "Strategic Partner" Strategy Pick one AI vendor → Enterprise deal → 3-year commitment → Hope they solve everything Result: You're locked in. Technology moves on. You're stuck in vendor negotiations while competitors move faster. Approach 2: "Portfolio Strategy" Deploy capital across multiple companies → Take equity → Co-develop solutions → Maintain optionality Result: You capture upside. You shape products. You're first to new capabilities. You have relationships across the ecosystem. After 20 years building innovation platforms at JPMorgan, Mousse Partners, Blueprint Health, Junto Health, Atlantic Health: In fast-moving markets, portfolio strategies beat single-vendor bets. Healthcare AI is moving fast. The question isn't "Which AI vendor should we pick?" The question is: "How do we build an AI portfolio that gives us the most optionality" If you're thinking about this, let's talk. 📩 douglas.m.hayes@gmail.com ☎️ 215-620-2032 #healthcareAI #digitalhealth #venturecapital #innovation

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