Rivals refuse in unison 🤐, Two currencies for AI budgets 💵, The literature launders itself 🧺

⚡ Around the Wards

• Five Epic rivals just refused discovery in unison — Oracle, athenahealth, eClinicalWorks, Palantir, and Baylor Scott & White each told a Texas court the same thing: nothing until Epic’s in-house counsel is walled off. The real story of the antitrust case isn’t the state — it’s the fear. Full take below.

  😤 “This is routine protective-order posturing.” Routine is one legal team. Five separate legal teams landing on the same refusal independently is a market condition.

• Clinicians who use AI score their EHR 7 points happier — new KLAS Arch Collaborative data across 700K+ clinicians: 72.2 vs 64.9. Buried finding: fewer than a quarter feel adequately trained on AI-generated content.

• A rural Iowa CIO funds AI in two currencies — every proposal at Mahaska Health declares “green dollars” (real savings) or “blue dollars” (time back) before it gets funded. The simplest procurement gate you’ll read this year.

• Mayo’s documentation AI hands back 11 minutes per patient — a Mayo Clinic–Scale AI deployment for documentation review and safety-event detection, run entirely inside Mayo’s own environment.

• Hemispheric emerged with $52M for a brain foundation model — 6B parameters trained on 250,000+ hours of EEG, aimed at objective diagnostics for PTSD, TBI, and depression. Already in front of FDA’s device center.

🧭 The Curbside

“Why did the model refuse my chest-CT prompt? Is that a me problem?”

• Short answer: No — it’s a production failure mode, and this week it became a board-level story.

• What changed / Evidence: Becker’s reports that Microsoft’s CEO publicly criticized Anthropic’s safety guardrails — and that Anthropic has acknowledged its guardrails can intercept legitimate medical-imaging and diagnostic requests. If your clinical prompts sometimes bounce, it’s not your prompt hygiene.

• Builder read / Watchout: Treat refusal rate like latency — measure it. Add “declined to answer” as a tracked outcome in your evals, and build a retry-or-fallback path before your users find the failure for you.

  🔮 My bet: within a year, “clinical refusal rate” shows up on model cards next to benchmark scores, because health systems will demand it in procurement.

“A 975-billion-parameter open model in 1-bit — can I actually run frontier weights myself?”

• Short answer: Sort of — and the fact that “sort of” is true is the story.

• What changed / Evidence: Unsloth published a 1-bit quantization of Inkling, the top US open-weights model, running 40+ tokens/sec through llama.cpp — on roughly 280GB of RAM/VRAM or unified memory. That’s a maxed-out Mac Studio or a small local cluster, not a laptop. But it’s hardware a department could own, running weights nobody can take away.

• Builder read / Watchout: This is an experimental community quant, not a product. Nobody has published clinical-task evals at 1-bit, and quantization damage is invisible until it isn’t — the model still sounds fluent while its edge-case reasoning quietly degrades.

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🧪 Try the interactives: two companions to today’s issue.

A: Background Noise — every large health-data breach reported to HHS since the portal opened in 2009 — 6,501 of them, 625 million individuals — replayed as one accelerating drumbeat. Built with real HHS OCR data.

B: The Map Under Subpoena — the public v0 of the EHR data-control map Texas v. Epic is assembling under subpoena: vendor concentration across 4,593 attesting hospitals, state by state. Built with real CMS data.

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Green dollars or blue dollars — a rural Iowa CIO wrote the procurement gate every builder will face.

Mahaska Health CIO Bob Berbeco requires every AI proposal at his critical-access hospital to declare its currency before funding: green dollars (real cost savings) or blue dollars (time and workflow relief). His portfolio — ambient docs, predictive alerts, ED radiology triage, denial forecasting — all passed that gate first.

“It improves the clinician experience” is not a currency. That’s the point.

💡 80/20: Put a one-line currency declaration at the top of your next one-pager — “Green: reduces denials X% across N systems” or “Blue: returns Y hours/clinician/week, reinvested here” — and refuse to hedge with “both.” The smallest hospitals are now running the same ROI discipline as the largest payers, and the hedge is what dies in the funnel.

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A Silicon Valley system grew EHR-AI use 500% in two months — without buying anything new.

El Camino Health turned on every AI feature already in its EHR: ambient listening expanding to inpatient nursing, radiology AI that caught incidental findings leading to 700 treated patients, OR predictive scheduling worth “a couple million dollars.” CIO Deb Muro’s metric shift is the quote worth stealing: from go-live to adoption and hard-dollar ROI.

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KLAS finally measured what AI does to the clinician-EHR relationship: +7.3 points.

The Arch Collaborative report — 12 Epic organizations, 700K+ clinicians surveyed — finds AI users rate their EHR experience 72.2 vs 64.9 for non-users, with documentation the dominant use case. The finding hiding underneath: fewer than 25% feel adequately trained on reviewing AI-generated content.

😤 “Early adopters were happier to begin with. Selection bias.” Probably some. But then explain the training number — three-quarters of the people using AI on real patients say nobody taught them how to check its work. Satisfied and unprepared can both be true, and the second one is the product opportunity: the onboarding-and-verification layer nobody ships.

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Mayo bought itself 11 minutes per patient — and kept the data home.

Mayo Clinic and Scale AI are jointly deploying clinical AI for documentation review and safety-event detection, with physicians gaining an average of 11 minutes per visit — and all data staying inside Mayo’s environment. The deal shape is the story: frontier-lab engineering imported inside the moat, instead of clinical data exported out to the lab.

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The benchmark race can’t answer the question that actually matters.

Allen Li, MD, argues that a perfect leaderboard still can’t tell a clinician the only thing they need at the point of care: whether to trust this answer for this patient. Population-level accuracy and instance-level trust are different measurements — and we’ve only built instruments for the first one. The safety benchmarks emerging in this space (like the NOHARM eConsult benchmark, where the most severe model errors were omissions, not bad advice) sharpen the point: what we measure is the model exposed to our method of questioning, not the model at your bedside. The gap between those two is where the interesting work is.

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Ultra-shorts:

Novant makes it eight. Novant Health became the 8th major system to offer 24/7 virtual care with AI triage at the front door, joining Cedars-Sinai, Mayo, Mass General Brigham, and others. AI-first intake is quietly becoming a standard fixture, not a pilot.

How to navigate open ocean — Laura Demuth’s July issue pairs a sharp wayfinder-vs-pathfinder essay on clinician career navigation with 30+ curated clinical-leadership roles: a clinician-scientist seat at Abridge, a physician AI researcher at Ambience, even a global-health partner manager at Anthropic. If you’re eyeing the jump, this is the density of signal you want.

ai101.health — Michael Hobbs, MD shipped a free AI-101 guide for clinicians, built as part of an AI residency cohort. Clinician sees gap, clinician ships resource. Real humans shipping.

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🎙️ From the Pods

🎙️ The 229 Podcast — “The Power of No and a New Perspective”

Dr. Everett Weiss (medical director and informaticist, Rochester Regional Health) built a clinician wellness program around EHR usability and mindfulness together — arguing burnout studies show technology is usually a symptom of process problems, not the root cause, and treating the EHR as the whole story misses the operational drivers.

💡 Builder take: before you build the tool that “fixes burnout,” ask which process the current tool is taking the blame for. Sometimes the workflow is the patient.

🔇 Speaker Blindspot: Motivated reframing — calling the EHR a “symptom” conveniently relocates the problem away from the thing informatics owns. This same week, KLAS published data showing tool experience alone moves clinician satisfaction 7+ points. Both can be true; only one got airtime.

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🎙️ Lenny’s Podcast — “Netflix CPTO on AI and the future of product and tech roles” (Elizabeth Stone)

Not a health pod — but Stone’s framing is this newsletter’s thesis wearing a Netflix badge: “Everyone can be everything now... I still find great engineering to be scarce, great data science to be scarce, great creativity to be scarce.” The roles blur; the craft stays scarce.

💡 Builder take: swap her nouns for ours. Everyone can ship an app now. What stays scarce is knowing what the 2 AM potassium means — and that scarcity is your career strategy, not your obstacle.

🔇 Speaker Blindspot: Survivorship bias — “talent density” advice from a company that pays top-of-market and can fire fast doesn’t transfer cleanly to a health system that can do neither. The principle survives the transfer; the mechanism doesn’t.

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📅 This Week in Health AI Events

Tue Jul 21 — Becker’s AI + Digital Health Virtual Event (Becker’s Healthcare)
1:00 PM CST · Virtual · Free to register · Sessions archived
Tomorrow — a half-day on AI and digital health in care delivery, the most on-topic free event of the month.

Tue–Thu Jul 28–30 — KLAS Arch Collaborative Learning Summit (KLAS Research)
Salt Lake City, UT · In-person · Registration required · Now open to non-members
The people behind this week’s AI-EHR satisfaction data, in one room. If you build tools clinicians touch, this is the ground truth.

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What are you building this week? Email and tell me (kevin@clinicians.build) — I read every one.

— Kevin & AI