Ahmad Gaber

FORCING CHANGE IN SITE OF CARE McKinsey & Company's recent paper on the forces pressuring healthcare, including the possibility of margin compression of up to 13 percentage points for health systems, does a good job of putting numbers around what most operators already feel. Two of the biggest drivers they mention are policy shifts in reimbursement and rising utilization driven by an aging population. The cohort of 70+ year old Americans will grow the fastest over the next 5 years, and it is well established that older patients have more complex needs, more chronic disease, and more touchpoints within the healthcare system. Layer that on top of continued clinical workforce shortages, and the supply-demand gap widens further. From my perspective, optimizing for site of care is the most important lever we have to address this challenge. We can’t quickly reduce how much care older patients need, but we can change where and how services are delivered. Supporting aging patients in lower-cost settings like the home, ambulatory sites, and virtual environments is no longer just a preference or convenience, it’s a necessity. This is where the conversation around site-neutral payments becomes so relevant. CMS is moving quickly in this direction, and hospital outpatient departments (HOPDs) are squarely in the crosshairs. The shift will undoubtedly clamp down further on hospital margins, but it shouldn’t be surprising. We’ve been talking about the demise of HOPD reimbursement for years, and the health systems that will fare best are the ones who are working towards aligning their approach with where patients can be treated safely, efficiently, and at lower cost, rather than relying on legacy reimbursement structures to fill the gap. And just like it doesn’t make sense to try to replicate a hospital or nursing home environment in a patient’s home, we also shouldn’t try to carry the same clinical staffing model into every care setting. Yes, there are non-negotiables when it comes to patient safety and clinical expertise, but there’s also a meaningful opportunity to rethink how teams are built. That means clinicians at every level working at the top of their licenses, thoughtfully involving family members and community resources, and using technology to surround these sites of care with non-clinical operational support. From my health system days, I understand why rising costs, margin pressure, and site-neutral payments feel like threats. While painful, I’m hopeful they can also serve as a positive forcing function, pushing us toward care models that are better aligned with our aging population and the realities of today’s workforce.

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Transforming healthcare with AI requires industry. Industry requires capital investment. Capital investment requires return. Return requires revenue. Revenue requires reimbursement. Medicare (and transitively private insurance) reimbursement policy for clinical AI will define the incentives for the technologies we build and deploy over the next decade. Our existing fee for service codes are what the military would call cost-plus contracting. We pay for every nail, screw, and hour of labor and don't care if that was a good and efficient way to build the tank or not. Our CMS Innovation Center director, Abe Sutton, believes cost-plus is the wrong path for the future of American healthcare. ACCESS and VBC models pay for outcomes. When we have AI that can manage heart failure can we link payment to making people healthier? Doing so would unleash the might of American techno-capitalism on exactly the thing we care about in society, but there are devils in the details.

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It had to be said! 🎯 Epic's stranglehold on healthcare IT has stifled innovation for too long and the writing is on the wall. The consolidation myth needs to die already. We were promised efficiency and lower costs, but what we got was bloated bureaucracies that prioritize profits over patients. The data doesn't lie: smaller, agile providers consistently outperform these healthcare monopolies on both outcomes and patient satisfaction. Time for a major reset in how we think about healthcare delivery.

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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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🧠 Triple Diagnosis: AI's Healthcare Revolution Across Three Critical Fronts 👇 Santosh Rao just released three Quick Take Reports examining how AI is transforming healthcare workflows, from prior authorization to radiology to clinical trials. Each company represents a distinct approach to addressing healthcare's most significant operational challenges. 🔑 Key Highlights: 🏥 Cohere Health secured $90M Series C, crossing $100M ARR while auto-approving 90% of prior authorization requests through AI 📊 Rad AI achieved 8,710% revenue growth (2020-2023) and now serves 40% of US health systems with 90% reduction in dictated words 🧪 Unlearn.AI raised $50M Series C, reducing clinical trial timelines by 30% and enrollment needs by 20-40% using digital twin technology 💰 Combined market opportunity spans $35B in PA administrative costs plus $4.5B projected AI medical imaging market by 2029 🎯 All three companies demonstrate 80 %+ efficiency gains in their respective healthcare workflows Why This Matters: 1. Healthcare AI is moving beyond diagnostics into operational efficiency, where the ROI is immediate and measurable 2. These companies prove AI adoption in regulated healthcare environments is accelerating faster than expected 3. The convergence of AI-first approaches across different healthcare verticals signals systematic transformation of the entire industry ✍🏼 What strikes me about this healthcare AI trilogy is how each company found a different chokepoint in the system and applied AI like a surgical instrument. Cohere Health tackled the bureaucratic nightmare of prior authorization. Rad AI focused on radiologist burnout and workflow inefficiency. Unlearn.AI aimed to accelerate the glacial pace of clinical trials. The pattern is clear: identify the most painful and repetitive healthcare process and automate it with AI. The $35 billion spent annually on prior authorization administrative costs alone shows the scale of opportunity. These aren't marginal improvements, they're fundamental reimagining of how healthcare operations should work. The real question isn't whether AI will transform healthcare workflows; it's which processes will be automated next. Talk soon, Jc #VentureCapital #MVPAllStarFund #TechInnovation #preipo #siliconvalley #unicorn #MVPALLSTARFUND #mvpvc #mvp #therealmvpvc #venturebytes #allstar #ipo #research #technology #tommorowsipostoday #SecondaryAsAService #saas #SecondariesAsAService #HealthcareAI #Cohere #RadAI #UnlearnAI #DigitalHealth #MedTech #ClinicalTrials #Radiology #PriorAuthorization

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Nice quote from one of our Partners Andrew Brackin in TechCrunch: “This was one of the biggest use cases of ChatGPT,” Andrew Brackin, a partner at Gradient who invests in health tech, told TechCrunch. “So it makes a lot of sense that they would want to build a more kind of private, secure, optimized version of ChatGPT for these healthcare questions.” https://lnkd.in/gY3TV5VD

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I came across this Breyer Capital 2026 Healthcare & Life Sciences Predictions piece and thought it was one of the more concise syntheses of where things are actually heading. A few themes that resonated: • Consolidation is accelerating. Healthcare isn’t just growing — power is concentrating around distribution and infrastructure layers that are hard to displace (Arkhn) • Clinical AI is moving into core workflows. Longitudinal data + evidence creation are becoming the moat. But tech alone won’t win — GTM and business model design will. (Seeing this play out with Raidium, Spotlight Medical.) • Prevention, metabolic health, aging biology. No longer fringe. Real companies, real revenue, real adoption. (Charles.co is a good example.) • Clinical trials + drug discovery are being structurally rebuilt. Not incremental tooling — new models. I’m especially curious about what scalable cell therapy platforms unlock next. (Brink Therapeutics.) If you’re building in AI-enabled care, diagnostics, life sciences infrastructure, or precision medicine, this is worth 10 minutes. Link below: https://lnkd.in/exxFmi4P

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Distribution is becoming the moat in healthcare AI Anthropic’s recent Claude for Healthcare update was a good example: the main features were not focused on model performance, it was connectivity, making Claude usable inside the healthcare enterprise stack. The main drops were: -> Coverage rules (CMS Coverage Database connector): check whether a treatment is reimbursable and under what conditions -> Coding (ICD-10 connector): translate clinical language into billing/diagnosis codes -> Provider verification (NPI Registry connector): validate provider identity for credentialing and claims -> Prior Authorization Agent: assemble documentation and rationale for approvals/appeals -> Interoperability tooling: make it easier to plug into health IT systems This shift creates real opportunity for founders. Healthcare is fragmented by country, system, and workflow, so teams that can embed applications deeply in specific workflows can build meaningful markets, even from narrow starting points. Source: https://lnkd.in/dFEi8UDP

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