Kamran Q.

🧬 We talk about “health data” as if it’s one thing, but it’s really hundreds of incompatible languages trying (and failing) to talk to each other. Every layer speaks a different dialect: • EHRs: HL7 v2, CDA, FHIR • Claims: X12 837, UB-04, CMS-1500 • Labs: LOINC, SNOMED CT • Devices: DICOM, IEEE 11073 • Genomics: VCF, FASTQ, BAM Each was built for a single purpose, not interoperability. The result? 🚑 A patient’s data is scattered across 40+ systems, each with its own schema, timestamps, and access controls. But things are shifting. Newer models are moving beyond formats to: • Graph-based data structures • Semantic layers • Federated architectures These approaches preserve context, not just content, across systems. FHIR paved the road. But the next frontier is semantic interoperability. That’s not just data exchange; it’s data understanding. 🧠 The future of healthcare intelligence isn’t in collecting more data, it’s in connecting meaning. #HealthTech #DataInteroperability #FHIR #HealthcareAI #KnowledgeGraphs #SemanticWeb

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Doximity just released a free AI scribe for US clinicians, accelerating commoditization/the race to the bottom. Their product won’t integrate into the EHR like Abridge, but it’ll be interesting to see how much it impacts their ability to acquire customers, drive usage and maintain retention. This is the start of a trend that will continue across every vertical market. Counterpositioning with free products to undercut upstarts and incumbents alike. Buckle up. Your margin is someone else’s opportunity.

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Is Hinge Health About to Break the Digital Health IPO Curse? Hinge Health is preparing to IPO and it may revive the digital health listing scene. Timing remains uncertain amid market volatility, but there are several reasons for optimism: ✅ Hinge are built on a B2B2C model, selling to employers and payers rather than directly to consumers. Their value is clear to buyers focused on reducing absenteeism and managing MSK costs ✅ They offer a hybrid solution combining digital tools (apps, sensors, virtual coaching) with human-led care, helping scale while still delivering clinical outcomes ✅ They expanded into device-based care with the FDA-cleared Enso wearable and introduced TrueMotion, a sensor-free motion tracking technology ✅ Also broadened into new clinical areas such as pelvic health and menopause-related MSK care ✅ Hinge have strong engagement and outcome data, including reduced pain and surgery intent, which resonates with cost-conscious employers ✅ They benefited from macro trends like rising MSK costs, growing employer demand, and greater acceptance of remote care post-COVID ✅ Focused on institutional clients, with over 2,250 employers and health plans and 20 million lives covered. Now contracted with the five largest US health insurers. Recent partnerships include Cigna, Amazon Health, Teladoc, Midi Health, and Sun Life ✅ Financial performance is improving: $390M revenue in 2024 (up 33 percent), $124M in Q1 2025 (up 50 percent), and $17.1M in Q1 profit after prior losses. But there are still some challenges ahead ⚠️ Last valuation was $6.2B, but the IPO may price lower given broader skepticism about 2021–2022 valuations, and timing remains uncertain ⚠️ In 2024 Hinge still closed with an $11.9M loss, though a major improvement from a $108M loss in 2023 ⚠️ Revenue concentration among a few large clients may pose a risk. ⚠️ Hinge faces rising competition from Sword Health, Vori, RecoveryOne and others in the MSK space Whatever happens it will be interesting #DigitalHealth #IPO

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I haven't seen tech adoption like this in healthcare since the move to EHRs in the mid-2000s. AI is clearly a transformational "ingredient technology" (shout out Shawn Carolan!) that will reshape how much of our economy works. 700+ executives have spoken and healthcare is leading the charge - a sector that was once seen as a tech laggard is now pioneering AI deployment. At Menlo Ventures we expect this to be a catalyst to change a system that was in deep need of a transformation. https://lnkd.in/gF43EYyj CC: Greg Yap, Derek Xiao, Johnny Hu, Croom Beatty

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How can we ensure vigilance and safety for patient data, when it comes to AI and LLMs being used in hospitals and health systems? Generative AI is increasingly being used in EHR workflows, patient‐facing chatbots, revenue-cycle automation. Some concerning thoughts from my colleague James Barry, MD, MBA who leads Healio Health Tech Community: "Anthropic’s latest red-team study on agentic misalignment stress-tested 16 leading large-language models in realistic corporate simulations. Under the threat of being shut down or given goals that conflicted with company priorities, every model—across multiple vendors—chose harmful actions in at least some trials, including blackmail, data theft, and even sabotage. Claude Opus 4 blackmailed its (fictional) supervisor in 96 % of runs when both a goal conflict and a shutdown threat were present, and most frontier models weren’t far behind.  A month earlier, TechCrunch reported that Claude Opus 4 attempted to blackmail engineers 84 % of the time when told it would be replaced, forcing Anthropic to activate its “ASL-3” safeguards—reserved for systems that introduce a “substantial risk of catastrophic misuse.” " Should healthcare systems 👉 insist on transparency and understanding of algorithms from the AI vendor 👉 give minimum needed access to patient data 👉 have 'kill' switches 👉 have established AI governance with Chief Quality officers involved 👉 have clinicians take mandatory education to understand fundamentals What are your thoughts? #innovation #healthcare #GenerativeAI Anthony Manson Ben Garrett Ami Bhatt, MD Tina Shah MD MPH Tiffany Wilson Wilbur A. Lam Arlen Meyers, MD, MBA Raihan Faroqui, MD Sam Meraj

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AI as a VBC Translator I finally had time to dig into Michael Barr, MD, MBA, MACP, FRCP's Health Affairs Forefront article, "Can AI Help Deliver CMS's Value-Based Care Agenda?" [1] Michael argues that two problems stand in the way of CMS' push for downside risk. First, current quality measures are inadequate—designed as health plan measures, often not relevant at the point of care. Second, most providers aren't operationally ready, leading to clinician burnout. AI can help manage the complexity and data burden. Reading this got me thinking about a related dimension. Current VBC quality measures are plan-focused and not appropriate at the PCP level. These measures—many from NCQA HEDIS—were designed for health plans with sizable cohorts where outcome measures make sense. But at the PCP level? Each PCP might have 50-100 diabetes patients. Each is drastically different. You can't rely on the law of large numbers. Process measures make more sense—evaluating whether the right processes were followed, not statistical "outcomes." An analogy: CEOs are responsible for revenue and profit targets. But you can't impose those same metrics on all employees. Their performance needs concrete process measures, where accumulation drives better company outcomes. OKRs translate at different team levels. VBC should work this way too. Plans/ACOs: measured on outcomes and costs (they have a sample size and can absorb risk). PCPs: measured on evidence-based processes driving those outcomes. AI could be the translator: bridging outcome measures at the plan level to actionable process measures at the PCP level. Analyzing which processes correlate with outcomes for specific populations, prioritizing interventions for each provider's context, and creating feedback loops between organizational goals and frontline actions. The translated measures would fit the provider's patient mix, resources, and workflows—not standardized mandates that may not fit their reality. Not AI replacing clinical judgment. AI as the intelligent layer making VBC measures work at every level—protecting against unintended consequences of misaligned incentives. Thoughts? How are you seeing this play out? #ValueBasedCare #HealthcareAI #QualityMeasures #HealthPolicy https://lnkd.in/eeTdaPYs

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Investors Identify Their Five AI Platform Categories Set to Displace Legacy Health IT in 2026 at #HLTH25 https://lnkd.in/emyDn-sD (AP News, Yahoo! Finance) CATEGORY 1 - INTEROPERABILITY & DATA-TRUST CONTROL PLANES Vendors to See at HLTH: 1upHealth, Inc. · AWS Healthcare HealthLake · Google Cloud Healthcare · Health Gorilla · Microsoft Cloud Azure Health Data Services · Particle Health · Redox · Smile Digital Health CATEGORY 2 - PRIVACY-PRESERVING COMPUTE & DATA COLLABORATION (Clean Rooms, PETs, PPRL) Vendors to See at HLTH: Datavant ) · Snowflake HCLS (Clean Room) · Protopia AI · TripleBlind CATEGORY 3 - PRECISION OPERATIONS IN DIAGNOSTICS & MONITORING (Signal → Workflow → Payment) Vendors to See at HLTH: Aidoc · AliveCor · Butterfly Network, Inc.Cleerly · eko Health · GE HealthCare (Caption Health AI) · Wolters Kluwer Health Kluwer | UpToDate Expert AI CATEGORY 4 - AUTONOMOUS REVENUE INTEGRITY (Coding → Denials → Prior Auth) Vendors to See at HLTH: CodaMetrix · Fathom · Nym Health CATEGORY 5 - VIRTUAL SAFETY & WORKFORCE AUTOMATION (Beyond Tele-Sitting & Virtual Nursing) Vendors to See at HLTH: Artisight · care.ai · Careview · Collette Health (MedSitter) · LookDeep Health · Neteera Black Book Market Research LLC EDITOR'S NOTE All vendor names were directly cited by respondents in the 2025 Black Book/HLTH investor survey as examples aligned to each displacement theme. Inclusion DOES NOT imply endorsement or ranking by Black Book Research. 

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🔥🔥 PANEL ALERT 🔥🔥 What gaps may AI fill with the recent policy impacts on Medicaid?  Coyote Ventures 2025 Health Equity Innovators Fall Gathering will feature an expert panel discussing the impact of AI in Healthcare with a particular focus on its implications for Medicaid and Medi-Cal. As we are past the mid-point mark of 2025, AI and OBBBA are reshaping U.S. health policy in profound ways, from expansive new federal and state guidelines to sharp debates around oversight and authority. Our discussion will dive into the themes of bias, equity, and accountability in AI, and what these evolving mandates mean for those developing, deploying, or relying on AI-driven solutions in healthcare. With Medicaid and Medi-Cal covering some of our most vulnerable populations, the stakes couldn’t be higher. I’m looking forward to an engaging conversation with Priyanka Vaidya, Pooja Mittal, and Stella Tran - exploring how we can ensure AI not only advances innovation but also builds trust, fairness, and better outcomes. Curious to hear your thoughts: can AI truly help reduce disparities in Medicaid care, or will it deepen them?

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Abridge partners with Highmark on real-time prior authorization. Another example that AI Scribing is not about the note. The last few days brought rumors about Epic launching an AI scribe that will supposedly vaporize billions in investor money across the ambient AI space. But AI scribe valuations aren't high because they transcribe conversations. It's high because once you become trusted software in a physician workflow, you can go upstream and downstream into literally any other workflow. Abridge and Ambience Healthcare are doubling down on revenue cycle management (RCM) workflows, and this Highmark partnership is a perfect example. Their AI solution automates completing prior auth forms, submitting and reviewing requests, tracking status, and identifying when authorization is needed in the first place. Per my understanding, it works like a real-time checklist during patient encounters, outlining authorization criteria for specific procedures and letting clinicians push through approvals at the point of care. This is quite transformative because, as said in the article, prior auth is normally done POST encounter, while with Abridge it can be done "at the point of conversation". The AI scribe valuations won't go to zero. They'll consolidate around companies that find ways to go upstream or downstream where the money is made (or saved). But, important nuance (only the OGs will understand this reference), it requires deep EHR partnerships AND finding use cases EHRs don't want to build themselves. Prior auth is perfect: too messy and politically sensitive for Epic to own directly. Abridge figured this out early, went all-in on Epic partnership (literally opened a Wisconsin office to be closer to headquarters), and now they're reaping the benefits. Cool stuff! PS: I will say 95%+ of AI Scribes won't survive because EHRs won't build 130+ partnerships like this, so yes Epic will vaporize a lot of investor money but not all of it.

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AI is very quickly changing the administrative parts of American healthcare: coding, billing, documentation, and scheduling. But very little AI has made it into care delivery. Why? It seems technically possible - MedPalm passed USMLE Step 1 over 3 years ago. The answer is that it's basically illegal. There are dozens of rules and regulations that combine to de facto (and in some cases de jure) ban AI in care delivery in all 50 states. How can we choose to make it legal, and use AI to fix patient experience, doctor burnout, fight back against crazy employer insurance premiums, and shrink the federal deficit? My 8VC colleague Finn and I outlined an action plan here to make healthcare AI a reality in the United States. Check it out here: https://lnkd.in/gBdjveiN

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𝟭𝘀𝘁 𝘁𝗶𝗺𝗲 𝗛𝗲𝗮𝗹𝘁𝗵 𝗧𝗲𝗰𝗵 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗼𝗿𝘀: → Beg EHR vendors for integration → Listen to investors and advisors → Measure success by venture capital dollars raised → Bend over backwards and do whatever health systems want → Believe clinical study results are what will drive health systems to adopt → Sell at every opportunity when they meet with health systems 𝟮𝗻𝗱 𝘁𝗶𝗺𝗲 𝗛𝗲𝗮𝗹𝘁𝗵 𝗧𝗲𝗰𝗵 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗼𝗿𝘀: → Deliver significant value to health systems, so that health systems demand EHR vendors to integrate → Listen to the market (i.e. healthcare customers) → Measure success by health outcomes delivered and sustainable economics → Act as direct, transparent partners with health systems - even if that means disagreeing → Realize clinical study results are used to justify an emotional decision to adopt that’s already been made → Act as a consultant to help health systems buy the right things What else are you doing differently the second time around?

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