Mayo's AI bill never stops 🧾, Two of three sepsis alerts are wrong 🚨, Things are changing faster and will never change this slow again 🐇

• Mayo’s AI chief Tripathi says maintaining clinical AI costs “far higher” than the industry expected — 128 models live, ~500 in the pipeline, no answer on year two.

  🔮 My bet: within 18 months a health system publicly retires a working AI tool over maintenance cost, not safety.

• A meta-analysis of 53 sepsis-prediction studies puts positive predictive value at 34.2% — the discrimination is real; the alert burden is the story.

• McKesson is investigating “exfiltration of certain data” — ShinyHunters claims 284 million patient records and wants $55 million.

• Adventist Health took 27 hospitals and 440+ clinics live on Epic in a single systemwide go-live — three states, one chart, a new version matrix for every SMART app there.

• 🎧 Podcast: Radio Advisory — “Finding margin in providers’ new policy reality” — why a documentation program that used to produce double-digit gains produced half a percent.

🧭 The Curbside

“ONC opened the 2026 SVAP window. Does that change what my app can query?”

• Short answer: Not yet — and the “not yet” is what bites you.

• What changed: USCDI v6 and US Core STU 9 opened for voluntary certification August 29. Some vendors adopt now, some wait for a mandate, and you’ll be talking to both.

• Builder read: CapabilityStatement gives you the FHIR version, not the IG version. An empty Bundle on an SDOH query doesn’t mean the patient wasn’t screened.

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🔬 The Big Thing

Mayo has 128 clinical AI models in production. Who is paying for year two?

Micky Tripathi, Mayo’s chief AI implementation officer, told Becker’s the system has 128 clinical AI solutions in practice and is approaching 500 in the pipeline.

Roughly 70% are built in-house. Not licensed. Built.

💬 Standout Quote

“Some of them we have a partner for, but we are ultimately, for those solutions, the legal manufacturer.” — Micky Tripathi, PhD, Mayo Clinic

Then the line that should reorganize your roadmap: “The cost of maintaining these systems is far higher than I think any of us in the industry really thought.”

Three cost centers — compute, the workforce to monitor models, the data infrastructure underneath. The workforce one is the trap: “I already don’t have enough AI/ML engineers to create these products. And if those engineers can never let go of the product because they’re needed to maintain and oversee things like data drift, model drift... that starts to become a big challenge.”

Every model you ship subtracts from the team that ships the next one.

Traditional software sits still after deployment. A model can’t — the data moves, the population moves, the coding rules move, and someone has to be watching.

There’s no revenue line for the watching. Mayo funds it partly with philanthropy: “there is no line-of-sight ROI because you’re just improving quality in a system that doesn’t compensate for higher quality right now.”

Stack that against Deloitte’s finding that only 18% of systems scaling generative AI have mature financial attribution, and UPMC’s governance research that 93% have deployed third-party AI while fewer than half have anywhere to test it first.

Same gap, three chairs. The CFO can’t prove the return because the CIO never built the counterfactual, and the AI chief can’t fund the monitoring because quality isn’t a billable event.

😤 “This is a Mayo problem. They have 500 models, I have one.” Then you have one model and one engineer who can never take a vacation. The ratio is what scales, not the count.

😤 “Maintenance is just DevOps.” It isn’t. DevOps keeps the service up. Nobody in DevOps can tell you whether last quarter’s coding-rule change moved your model’s input distribution — the failure that doesn’t page anyone.

😤 “Buy instead of build and it’s the vendor’s problem.” Try that and see how the contract reads.

❓ Everyone is selling the model. Who is selling the watch — a monitoring layer the health system owns, that watches models it didn’t build?

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📡 Builder’s Radar

Two out of three sepsis alerts are wrong, and that was the finding.

A network meta-analysis published yesterday in npj Digital Medicine pooled 53 studies and more than 7 million admissions. Best-performing models hit an AUROC of 0.88 — genuinely better than the traditional comparators.

Then the number nobody puts on a slide: pooled positive predictive value of 34.2%.

Discrimination is not the same as a workable alert, and the gap between them is measured in nurses who stop looking.

Heterogeneity above 95% and a prediction interval from −0.06 to 0.30 — a polite way of saying the pooled estimate may not describe your hospital at all.

💡 80/20: Before you quote an AUROC to anyone, compute PPV at your own site’s prevalence and threshold.

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⚡ Also worth knowing

• CMS sent $149.3 million to Arkansas for rural health, with named line items for telehealth, patient-monitoring equipment and AI-enabled “SMART rooms.” The buyer isn’t a health system — it’s a state. Fifty of them, run by people who’ve never bought clinical software.

• Trinity Health is hiring its first chief AI and digital transformation officer — the org-chart version of the Mayo problem. Somebody now owns the maintenance bill by name.

• Jeremy Langsam (Cleveland Clinic Ventures) launched a Ventures Fellowship for early-career people at the intersection of medicine and company creation. An on-ramp that isn’t an MBA.

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🎙️ From the Pods

🎙️ Radio Advisory — “310: Finding margin in providers’ new policy reality”

Optum’s Samantha Wilde ran the documentation-education program that historically produced double-digit case mix index gains — and got half a percent. Her team then found the 2026 DRG and relative-weight changes would have cut that group’s CMI about 12%, roughly $2 million, if they’d done nothing.

💡 Builder take: If your value story is “we improved X by Y%,” find out whether the definition of X moved underneath you this year. Half a percent against a 12% headwind is a win that reads as failure on a slide.

🔇 Speaker Blindspot: Streetlight effect. CMI is the outcome throughout because CMI is what this team can move. Whether the newly documented acuity reflects sicker patients — the question payers are pricing into 2027 trend — never comes up.

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🎙️ Health Tech Nerds Radio — Wildflower Health CEO Leah Sparks

Sparks spent five years running a high-margin, payer-only software business before concluding: “you really can’t change maternal health outcomes unless you work with the people who deliver the babies.” The pivot wasn’t technical — they built a proprietary OB-and-newborn episode model to fund services fee-for-service won’t pay for.

💡 Builder take: The software was never the constraint. The billing pathway was.

🔇 Speaker Blindspot: Framing effect. Direct-to-consumer is offered as an affordability win because cash pay often beats a copay — true, and also a cost shift onto the patient and out of any risk pool. Only one of those gets named.

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💰 Money Plumbing

How an NTAP actually works — the one mechanism currently paying for clinical AI

Medicare bundles inpatient costs into a fixed DRG payment. If your tool adds $20,000 to a case whose DRG pays $18,000, the hospital loses money every time it’s used — so it doesn’t get used.

A New Technology Add-on Payment breaks that. NTAP pays the lesser of 65% of the technology’s cost or 65% of the amount the case exceeds the standard DRG. That $20,000 tool can pull roughly $13,000 on top and become survivable.

You apply in the fall for the following fiscal year and prove three things: the technology is new, it’s a substantial clinical improvement, and the cost isn’t trivial. That third test is where most AI tools die — software is cheap per case, and “not trivial” is a dollar threshold, not a clinical one.

💡 Builder move: NTAP is a bridge, not a destination — two to three years while you pursue a permanent code. If your tool is inpatient and you can document incremental cost plus clinical improvement, calendar the window now.

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💡 BTW: Micky Tripathi, who runs 128 clinical AI deployments at Mayo, has no clinical or CS degree. His PhD is political science, MIT — and before that he earned the Secretary of Defense Meritorious Civilian Service Medal as a senior operations research analyst in the Office of the Secretary of Defense.

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💺 Builder Seats

[These are just ones I found on LinkedIn that look interesting, no sponsorship or anything. Use at your own risk but look legit]

Founding Clinical AI Lead — Atomic · Remote
Studio side: you shape what gets built before there’s a company to defend.
🔗 Apply on LinkedIn

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You have a unique combination of skills, experience and values. So do great things! … and tell me about them at kevin@clinicians.build.

— Kevin & AI

(please verify content for yourself, partially AI generated and may contain errors)