Bad data at AI speed 🚱, Tempus swallows Personalis 🧬, Dialysis AI
⚡ Around the Wards
Bad data at AI speed is still bad data — the week’s two live Epic Agent Factory deployments and a quality leader’s warning land on the same point: governance and data quality, not budget, are the wall (the Big Thing).
😤 Haters: “Sounds like an excuse to slow-walk AI.” No — it’s the difference between an agent that drafts a discharge summary and one that confidently drafts the wrong one.
Advocate Health and ECU Health went live on Epic’s no-code Agent Factory — inpatient pharmacy/infusion prep and patient-transfer/discharge summarization, respectively — and both flagged a data-governance and AI-expertise shortage as the real gate.
Tempus is acquiring Personalis for ~$1.5–1.7B, folding blood-based molecular-residual-disease testing straight into its AI precision-oncology platform. The diagnostics layer keeps consolidating around whoever owns the data model.
DaVita is embedding AI summaries and risk models into the dialysis workflow — 70% of its patients carry 3+ chronic conditions — betting on decisions-in-workflow over data accumulation.
The AMA’s new AI billing codes are drawing organized pushback from nursing — the code-creation machine hit political friction (full math in Money Plumbing below).
🧭 The Curbside
“Which FHIR/interop rules do I actually have to track right now?”
Short answer: More than one — the OMB Unified Agenda just loaded roughly five interoperability rules onto a six-month runway, all pointing at the January 1, 2027 CMS-0057-F electronic prior-auth deadline.
What changed / Evidence: HTI-5 final (certification-criteria and info-blocking revisions), an HTI-6 proposed rule (a new API-and-info-blocking rulemaking), a HIPAA access-timeline shortening toward 15 days, and a CMS X12 transaction-standard update are all queued across ASTP/ONC, OCR, and CMS. Announced/proposed ≠ final — most of these are on the agenda, not on the books.
Builder read / Watchout: Build for the stack, not the single rule. Each one changes a different layer — what certified EHRs must support, how APIs get certified, the access-response clock, the transaction format. And the conformance tests that enforce Jan 1, 2027 (the Inferno kits) are still in draft, so treat each version bump as a dependency change in your pipeline.
🔮 My bet: the regulatory cycle becomes a release cycle. By mid-2027 the teams that treated “which Inferno version are we conformant to?” as a standing CI question ship on time; the ones who read the rule once in December do not.
“Another 2.4-trillion-parameter frontier model dropped — do I care as a clinical builder?”
Short answer: Mostly no. Alibaba’s Qwen3.8-Max preview is a headline number, not your bottleneck.
What changed / Evidence: It’s a preview (positioned as #2 behind Fable 5, unverified independently) and expected to go open-weight “soon” — not open-weight yet, not GA. Frontier scale keeps climbing on a track you mostly rent, not build.
Builder read / Watchout: The clinician-builder’s constraint was never “is the biggest model smart enough.” It’s “can I run something good enough on data I’m not allowed to send anywhere.“ The number worth watching isn’t 2.4T parameters — it’s how small a model can get and still hold up on-device for PHI. That’s the ballgame; a bigger cloud model doesn’t move it.
🔬 The Big Thing
Everyone learned to build the agent. Almost no one can vouch for the data it’s standing on.
Two health systems put Epic’s no-code Agent Factory into live production this week — Advocate Health on inpatient pharmacy and infusion prep, ECU Health on patient transfers and discharge summarization.
Both were asked what the real barrier was. Neither said money, and neither said the model.
They said data governance, compliance, and a shortage of people who actually understand how to deploy this safely.
The same week, St. Luke’s (Boise) data-governance director Troy Heninger put the sharper version on the record: “bad data at AI speed is still bad data.” Ungoverned data doesn’t get better when an agent touches it — it gets amplified, faster, with a confident tone.
This is the bottleneck moving in real time. When the platform is no-code, the scarce input stops being “can you build the agent” and becomes “is the data underneath it good enough to trust an autonomous action to.”
And that second question is not an IT question. It’s a clinician question. Which fields are reliably populated and which are theater. Whether the problem list is real or a graveyard of resolved diagnoses. Whether an allergy is an allergy or a note someone made in 2019. The person who knows that is the one who’s been burned by it at 2 AM — not the vendor, and not the model.
😤 “Data governance is just the new way to stall the project.” Sometimes, sure. But watch what the people who actually shipped said — Advocate and ECU are live, and they still named governance as the wall. When the builders who cleared the bar tell you where the bar is, that’s not a stall. That’s a map.
😤 “Isn’t this just the ‘garbage in, garbage out’ cliché with a 2026 coat of paint?” It’s the cliché with a new failure mode. GIGO used to mean a bad report a human would eyeball and toss. Now the garbage gets an autonomous action attached to it and executes before anyone reads it. The cliché didn’t change; the blast radius did.
😤 “Fine, but data cleanup isn’t a builder job — it’s grunt work.” It’s the most leveraged builder job on the board right now. The tool that surfaces which of your data a given agent can and can’t safely rely on — a data-readiness map scoped to a specific workflow — is worth more than the agent it protects.
❓ What does a “data-readiness score for this specific agent” actually look like as a product? Not a generic data-quality dashboard — something that answers “can the discharge-summary agent trust the med list on this unit today?” I think there’s a real tool hiding in that question, and I can’t quite draw its edges yet.
📡 Builder’s Radar
Tempus reaches for the recurrence layer.
Tempus signed a definitive agreement to acquire Personalis at roughly $16.25/share (~$1.5–1.7B enterprise value), pulling blood-based molecular-residual-disease testing fully inside its AI precision-oncology stack.
MRD is the “is the cancer coming back” signal — the monitoring layer that turns a one-time diagnostic relationship into a longitudinal data stream.
The play isn’t the test. It’s owning the data across the whole arc — diagnosis, treatment selection, recurrence — so the AI has something to learn on that no one else holds.
🔮 My bet: the next 12 months of oncology-AI M&A is a land grab for longitudinal data, not point tests. Whoever strings together the timeline wins; whoever sells a single snapshot gets acquired.
DaVita bets on decisions-in-workflow, not data hoarding.
DaVita’s CIO described embedding AI-generated summaries and predictive risk models — including one for home-dialysis discontinuation — directly into the point of care, across a population where 70% of patients carry 3+ chronic conditions.
The framing worth stealing: they explicitly chose putting insight in the workflow over accumulating more data to look at later.
💡 80/20: “Connected data” only matters at the moment of a decision. If your tool makes a clinician navigate away to see the insight, it’s a dashboard, not a decision-support tool — and dashboards lose to the path of least resistance every time.
The migration agent is the boring clinical superpower nobody’s naming.
Anthropic published how it runs large-scale code migrations with Claude Code, built on one principle: fix the process that produces the code, not just the code. (Adjacent-possible: general dev tooling, clinical implication underneath.)
🎙️ Health Tech Nerds Radio — “The Grand Roundup: Payer Q2 Earnings”
The buried builder signal in the payer earnings: United called out the No Surprises Act as ~1% of overall medical-spend trend — a huge number — while Elevance, the plan most publicly fighting NSA, didn’t mention it. The out-of-network dispute pipeline is quietly a major cost center.
💡 Builder take: The IDR (independent dispute resolution) workflow is a document-heavy, deadline-driven mess — exactly the shape of problem where a domain-aware tool beats a generic one. If you know how a dispute actually gets adjudicated, that’s a build surface hiding inside an earnings call.
🔇 Speaker Blindspot: Narrative anchoring — the hosts spent most of the segment reverse-engineering Elevance’s Medicaid-exit messaging strategy (is it negotiating theater?) and comparatively little on the members in the markets being exited. The story got told from the earnings-call podium, not the exam room.
💡 BTW: Eric Lefkofsky, whose Tempus is buying Personalis this week, started the company in 2015 after his wife Liz’s breast-cancer diagnosis left him “perplexed at how little data had permeated her care.” His better-known prior venture: he co-founded Groupon. Forbes.
What are you building this week? Email and tell me (kevin@clinicians.build) — I read every one.
— Kevin