The Race: Between January and April 2026, six major tech companies shipped consumer health AI products: ChatGPT Health (OpenAI), Claude for Healthcare (Anthropic), Amazon Health AI, Microsoft Copilot Health, Perplexity Health, and Google Health AI (Gemini). Nothing like this has happened before.

The Pattern: All six aggregate personal health data (EHRs, wearables, labs) and turn it into explanations, summaries, and visit prep. All six say “not for diagnosis.” All six are trying to become the front door of healthcare.

The Verdict: When graded through the Vowels of Clinical Design (AEIOU), the picture is clear: strong Adoption wedges, early Interoperability connectors, and almost zero Evidence loops, Ownership structures, or Unit Economics at the point of care. These are dashboards. They are not yet circuits.

The Gap: As one physician on X put it: “A dashboard that flags your rising Lp(a) without connecting you to a physician who will actually act on it is not healthcare. It is a sophisticated anxiety generator.” The missing vowel across all six is the human loop.

1. Ninety Days That Redrew the Map

In January, I published an analysis of ChatGPT Health vs. Claude for Healthcare through the lens of Clinical Design.12 At the time, the framing was two companies and one open question: who will succeed?

Well, that framing lasted about eight weeks.

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Between January and April 2026, four more products landed. Amazon expanded Health AI to all users via Prime and the Amazon app.3 Microsoft launched Copilot Health, aggregating records from 50,000+ hospitals and 50+ wearable platforms into a single encrypted vault.4 Perplexity shipped Perplexity Health on top of its agentic Computer platform, with connectors to 1.7 million providers.5 And Google kept building Gemini Med and its health stack, though without the singular consumer launch event the others staged.6

The result: a new category consolidated in real time. The industry is now calling these “AI front doors” to health, and the question is no longer whether LLMs belong in healthcare. The question is whether any of them can get past the lobby.

2. The Six Players: What They Shipped

Before grading, a brief map of what each product actually does. The differences matter more than the similarities.

ChatGPT Health (OpenAI)

A dedicated Health space inside ChatGPT. Connects to medical records and wellness apps (Apple Health, nutrition trackers) via live connectors. The user drives: upload labs, ask questions, iterate. 230 million health queries per week were already flowing through ChatGPT before the launch.7 The product formalized an existing behavior. It didn’t create a new one.

Claude for Healthcare (Anthropic)

Enterprise-first. HIPAA-ready posture, connectors into CMS coverage rules, ICD-10, NPI registry, PubMed, and explicit FHIR development. Targets prior authorization, claims support, care coordination, clinical documentation. The ambition is the operational backbone, not the consumer layer.

Amazon Health AI

The most action-oriented of the six. Explains lab results, renews prescriptions, books appointments with One Medical providers, and gives eligible Prime members up to 5 free direct-message care visits. Built on Amazon Bedrock with a declared multi-agent architecture that includes escalation to licensed clinicians.8 Distribution moat: 200 million Prime users.

Microsoft Copilot Health

The aggregation play. Records from 50,000+ hospitals, 50+ wearable integrations (Apple Health, Oura, Fitbit), and lab results, all funneled into an encrypted “health vault.” On the clinical side, Dragon Copilot handles ambient documentation and note generation.9 Internal research (MAI-DxO) reportedly outperformed physicians on NEJM case studies.10

Perplexity Health

Launched March 19, 2026. The freshest entrant. Sits on top of Perplexity Computer (their agentic platform) and connects Apple Health, wearables (Fitbit, Ultrahuman, Withings; Oura coming), EHR data from 1.7 million providers via b.well and Terra, and lab results. The differentiator: answers grounded in cited medical literature, plus agentic tools that build personalized nutrition plans, training protocols, and visit summaries.11 Pro and Max subscribers in the US only, for now.

Google Health AI (Gemini)

No single consumer launch event. Instead, a continuous stack: Gemini Med (strong medical benchmarks), Personal Health LLM (wearable/sensor integration), and Google Cloud partnerships with CVS Health and Highmark. Gemini 2.5 Pro scored top-2 in recent complex medical question studies. The perception gap: best benchmarks, lowest consumer buzz.

3. The AEIOU Verdict: Six Products, One Framework

In Article 2, I graded ChatGPT Health and Claude for Healthcare using the Vowels of Clinical Design. Now let’s run all six through the same framework. The pattern that emerges is striking: strong A, partial I, and near-empty E, O, and U across the board.

A — Adoption

Winners: ChatGPT Health and Amazon Health AI.

ChatGPT Health wins because it formalized something people already did. The product didn’t change behavior; it gave existing behavior a home. That’s the lowest-friction adoption wedge possible.

Amazon wins differently: through action. Every other product on this list is fundamentally a conversation. Amazon is the only one that books appointments, renews prescriptions, and connects you to a licensed clinician inside the same flow. Action beats explanation.

Microsoft and Perplexity sit in the middle. Copilot Health’s aggregation is impressive, but its adoption depends on whether users will actively manage a “health vault” (most won’t, unless prompted). Perplexity’s agentic layer is powerful for self-trackers and biohackers, but that’s a niche, not a population.

Claude is the weakest on consumer adoption by design: it’s built for enterprise workflows (prior auth, claims, coding), where the adopter is an institution, not an individual. That’s a deliberate trade-off, not a flaw.

Google has the reach (Search, Android, Fitbit, Pixel) but hasn’t consolidated it into a single consumer moment. The pieces are there. The circuit isn’t.

E — Evidence

No clear winner. This is the weakest vowel across all six.

None of these products have published real-world evidence on clinical outcomes. None have pragmatic trial data. None have continuous monitoring of drift, safety signals, or diagnostic accuracy in the wild.

ChatGPT Health generates personal evidence loops (test a hypothesis on your own data, iterate, discard). That’s useful. But one physician tester reported the model flagging a correlation as “significant” on n=3, then losing it when pressed to go deeper.12 Sample-size blindness in a health product is a design flaw, not a feature gap.

Claude’s operational targets (prior auth time, denial rates, documentation throughput) come with built-in metrics. That’s structurally better for evidence generation than consumer wellness. But the evidence doesn’t exist yet.

Microsoft’s MAI-DxO benchmark is eye-catching (85.5% vs 20% on NEJM cases). A benchmark on curated cases remains a benchmark, not a real-world evidence loop. Google’s Med-Gemini scores are similarly impressive and similarly lab-bound.

The bottom line: none of these products can yet answer the question Clinical Design demands: “Can we prove clinical value at run-rate?”

I — Interoperability

Leaders: Amazon, Microsoft, and Claude (each in different directions).

Amazon connects to clinical action (appointments, prescriptions, escalation to One Medical). Microsoft connects to clinical data (50,000+ hospitals, FHIR, wearable streams). Claude connects to clinical codes (CMS, ICD-10, NPI, PubMed, FHIR development). Three different interoperability bets. All partial.

The shared constraint: none of them have solved what Josh Mandel called the “clinical note text” problem.13 Structured FHIR data without narrative context is not what clinicians experience as “the chart.” Connecting to records is not the same as connecting to the clinical moment.

Perplexity’s connector layer (b.well, Terra, 1.7M providers) is broad but untested at scale. ChatGPT Health connects to the user’s personal data well; it connects to the care delivery system less well. Google’s interoperability lives in Cloud partnerships, not in the consumer product.

O — Ownership

Only Amazon and Claude have legible ownership structures. For different reasons.

Amazon is the only product with a direct human-in-the-loop: One Medical clinicians who can see, validate, and act on AI-surfaced insights. The ownership chain is clear. The AI suggests. The clinician decides. The patient receives care. That’s a circuit.

Claude’s enterprise posture (BAAs, access controls, governance, auditability) creates institutional ownership. When a hospital deploys Claude for prior auth, someone owns that workflow: revenue cycle, clinical ops, utilization management. There’s a pager holder.

The other four are consumer products where ownership is diffuse by default. The user owns the habit. Nobody owns the outcome. And that’s the structural problem a physician on X described with surgical precision: “Every AI company is racing to build the best health dashboard... But a dashboard that flags your rising Lp(a) without connecting you to a physician who will actually act on it is not healthcare. It is a sophisticated anxiety generator.”14

Perplexity’s advisory board (Eric Topol, Devin Mann, Wendy Chung) is a credibility signal, not an ownership structure. Advisory boards don’t hold the pager at 3am.

U — Unit Economics

Amazon has the clearest path. Everyone else is subsidizing.

Amazon monetizes through Prime membership, One Medical visits, and pharmacy integration. The unit economics are visible: reduce friction for the patient, generate visits and prescriptions, capture value through existing commerce infrastructure. It’s the only model where the health AI product has an obvious revenue loop that doesn’t depend on enterprise licensing alone.

Claude sells to institutions (enterprise contracts, BAAs). The ROI case is strong (reduced admin labor, fewer denials, faster throughput), but requires selling into budget owners who may not capture all the value.

Microsoft, OpenAI, and Perplexity are all subscription plays (Pro/Max/365) where health is a retention feature, not a revenue center. That’s fine for now. It’s not a unit economics model for healthcare.

Google’s model is B2B (Cloud partnerships), which has enterprise-grade economics but no consumer-facing health monetization yet.

4. The X Verdict: What the Field Is Actually Saying

The conversation on X in March/April 2026 is the most mature I’ve seen around health AI. The hype-to-substance ratio has improved. Three threads capture the mood.

Thread 1: “All doing basically the exact same thing.” A viral post on March 19 asked the question directly: “GPT Health, Claude Health, Copilot Health, Amazon Health, and now Perplexity Health... all doing basically the exact same thing. Who will win and why?” The fact that this question is being asked publicly, without irony, tells you the category has commoditized at the surface layer. The differentiation lives underneath.

Thread 2: The anxiety generator.” The @agingroy thread became the most cited critique of the entire wave. The argument: connecting records and flagging lab anomalies without closing the loop to a physician who will act is worse than useless. It generates anxiety without resolution. The thread resonated because it describes an experience most early testers recognized.

Thread 3: “Overconfident and wrong.” @dampedspring tested Claude, Gemini, and ChatGPT with real abdominal pain. All three were roughly equivalent, all overconfident, and all missed the diagnosis until the ER confirmed kidney stones.15 The word used was “totally useless.” That’s harsh but instructive: in the consumer frame, these models present with clinical confidence they haven’t earned.

The privacy drumbeat is constant. Every launch gets the same reply pattern: “We take your privacy seriously... same sentence every platform uses right before the breach.”16 The references to 23andMe, MyFitnessPal, and Cerebral are not abstract. They’re recent.

And the startup ecosystem is watching: “Perplexity Health just sent an entire sector of YC back to the drawing board... your startup’s moat was just AI for lab results... you’re now a feature.” 17

Overall sentiment: approximately 70% optimistic realism, 30% cautionary. The optimism is about capability. The caution is about the gap between capability and clinical accountability. That gap is precisely where Clinical Design operates.

5. The Pattern: Dashboards vs. Circuits

If you step back from the feature lists and marketing copy, a single pattern emerges from all six products.

They are all dashboards. Brilliant, data-rich, increasingly well-connected dashboards. But dashboards don’t save lives. Circuits do.

I used this metaphor in Article 3, comparing PRAIM and TRICORDER.

ChatGPT Health is a dashboard for personal wellness. Amazon Health AI is the closest to a circuit (it closes the loop with action and clinicians). Claude for Healthcare is building circuit components for the enterprise backbone. Microsoft is building the data substrate. Perplexity is building an agentic dashboard with cited sources. Google is building the research engine.

None of them are complete yet.

A complete circuit in health AI would look like this: data flows in with semantic meaning (I). The interface exists inside the clinical or personal health moment without friction (A). Continuous evidence validates performance in the real world (E). Someone holds the pager and is accountable for the output (O). And the economics sustain the system at run-rate (U).

That’s the AEIOU framework, fully pronounced. And no product on the market today pronounces all five vowels.

6. What to Watch: The Next 6 Months

The race is on. Here’s what will separate the winners from the features:

Who ships real-world evidence first? The first product to publish pragmatic outcome data (not benchmarks, not case studies, but population-level RWE) will own the Evidence.

Who closes the physician loop? Amazon is closest with One Medical. But the real question is whether any product can embed clinical accountability without owning a provider network.

Who survives the privacy reckoning? A single major breach in one of these products will reshape the entire category. The HIPAA-adjacent posture most of them maintain is necessary but not sufficient.

Who moves beyond the US? All six are US-first (or US-only). The European health system, with its fragmented payers, strict GDPR requirements, and public procurement cycles, is the real stress test. The product that figures out EU scaling first wins a massive structural advantage.

Who makes the startup ecosystem obsolete? If Big Tech health AI products commoditize the “explain my labs” and “prep for my visit” layers, the next generation of health startups will need to build deeper: into specific disease pathways, into operational workflows, into the spaces between these dashboards and the clinical moment.

7. The Call to Action

In January, two companies launched health AI products. By March, six had shipped. The category that didn’t exist in December is now table stakes.

But table stakes are not the same as value delivered.

When I look at these six products through the framework of Clinical Design, I see the same thesis confirmed that this newsletter has argued since Article 0: we have the technology. The algorithms work. The connectors are being built. What’s missing is the discipline of designing systems that close the loop between intelligence and care.

The models are good enough. The data pipes are being laid. The question is no longer “can AI understand health?” The question is: who will build the circuit?

Why I’m doing this: I believe the next 10 years won’t be defined by who discovers the next molecule, but by who figures out how to deliver it.

Whatever your role (clinician, founder, investor, or policy maker) we are all architects of this new system.

Let’s build.

— Marcos

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Note & disclaimers

Context: The Clinical Decade (and this article) explore the theoretical foundations of Clinical Design, a teaching framework created by Marcos Gallego Llorente. It has been developed through independent research and academic activities, and is shared here as a personal contribution to the field.

Independence: Views and materials published in The Clinical Decade are personal/independent and do not represent any employer, client, or institution.

License: Licensed under Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0), unless otherwise stated.

• The Trial: TRICORDER is the first pragmatic, cluster-randomised implementation trial of a clinical AI technology at national scale—205 UK NHS primary care practices, 1.5 million patients, published in The Lancet(Feb 2026).

• The Surprising Result: When clinicians used the AI stethoscope, detection rates jumped dramatically—heart failure ×2.3, atrial fibrillation ×3.5, valvular heart disease ×1.9. But the headline result was neutral. Because 40% of practices stopped using it.

• The Reason: The algorithm didn’t fail, implementation did. No EHR integration, extra steps in 10-minute consultations, alert fatigue…. This is, yet again, the classic Last Mile collapse.

• The Explanation: TRICORDER is the most powerful real-world validation of Clinical Design theory to date. Every single vowel (Adoption, Evidence, Interoperability, Ownership, Unit Economics) explains a piece of the failure, and a piece of the path forward.

• The Learning: We are officially in the Implementation Era. The question is no longer “does AI work?” It’s “can we design systems that let it?”

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1. A Landmark Trial Hiding in Plain Sight

Let me be direct: TRICORDER may be the most important trial published in healthcare AI so far this year. And not because it succeeded, but rather, because it failed in exactly the right way.

The study (Kelshiker, Bachtiger, Petri et al., Lancet 2026)1 deployed an AI-enabled stethoscope (Eko Health) across 205 NHS primary care practices in North West London. The device records a 15-second single-lead ECG and phonocardiogram during routine cardiac auscultation, then runs three cloud-based AI algorithms for heart failure (reduced LVEF ≤40%), atrial fibrillation, and valvular heart disease.

The design was pragmatic by intention: No cherry-picked sites, no artificial incentives, no dedicated research staff running the workflow… There were real GP practices, real Tuesday-morning consultations, and real NHS infrastructure.

The intention-to-treat result: no significant difference in heart failure detection between intervention and control groups (IRR 0.94, 95% CI 0.87–1.00). No difference for AF or VHD either.

Headline: “AI stethoscope fails to improve detection.”

But that headline is wrong. And understanding why it’s wrong is the entire point.

2. The Per-Protocol Signal: The Algorithm Works

When clinicians actually used the AI stethoscope (per-protocol analysis with propensity score matching) the results were unambiguous:

• Heart failure: IRR 2.33 (95% CI 1.28–4.26)

• Atrial fibrillation: IRR 3.45 (95% CI 2.24–5.32)

• Valvular heart disease: IRR 1.92 (95% CI 1.09–3.40)

Time-to-diagnosis was shorter: Detection was genuinely increased. It was not an artifact of coding noise or surveillance bias.

As Bo Wang put it on X: “The algorithm works. No question. […] The humans were the bottleneck.” 2

This is not a negative trial. This is a positive algorithm trapped inside a negative system.

Eric Topol called it “an important lesson for AI med trials”3: the tool improved diagnoses when used, but wasn’t used enough by the doctors. That’s the real finding.

3. The Anatomy of Collapse: Why 40% Walked Away

Across the 96 intervention practices, the AI stethoscope was used 12,725 times in 12 months. That sounds like a lot. It isn’t. It means roughly 2 uses per day per practice—in sites seeing dozens of cardiac-relevant patients daily.

By month 12, the usage distribution was brutal:

• High users (≥31/month): 6% of practices

• Medium users (10–30/month): 15%

• Low users (1–9/month): 40%

• Non-users (abandoned): 40%

The top 5 practices contributed 34% of all recordings. One outlier practice alone contributed 19%.

When surveyed, clinicians identified the barriers with razor-sharp precision:

1) No EHR integration. The AI stethoscope was not embedded in the EHRs. This means that results required manual entry. In a 10-minute NHS consultation, that’s a dealbreaker.

61% of respondents ranked EHR workflow integration as the most influential change to improve use, even ahead of financial incentives (52%).

2) Extra workflow steps. Turn on device. Connect Bluetooth. Open app. Place stethoscope. Record 15 seconds. Wait for result. Manually log finding. Each step is a friction tax on an already-overloaded clinician.

3) Alert fatigue and false positives. The positive predictive value for heart failure was 0.30 (70% false positives). For VHD: 0.10 (90% false positives). In low-prevalence primary care, even high specificity generates noise; and so, clinicians learn to ignore the signal.

Therefore, this is not a story about a failing technology, it’s about a lack of clinical design.

4. TRICORDER Through the Vowels of Clinical Design

If you’ve been following this newsletter, the failure pattern should feel familiar. Let’s run TRICORDER through the vowels of Clinical Design: The AEIOU framework.

A — Adoption

The AI stethoscope failed the sacred rule: if it doesn’t exist inside the clinical moment, it doesn’t exist.

The device lived outside the EHR. Outside the natural rhythm of the consultation. Clinicians had to leave their workflow to use it. And in primary care, not being in the workflow means not existing.

Compare this to PRAIM (the Vara breast screening study I discussed in Article 0)45: there, the AI was integrated as the viewer itself (triage and safety net built into the radiologist’s native reading environment). Adoption was structural and organic, not optional.

TRICORDER asked GPs to add a tool. PRAIM gave radiologists a better version of the tool they already had.

The learning: friction beats accuracy. Again.

E — Evidence

Here’s where TRICORDER actually shines—paradoxically.

This is the first pragmatic, cluster-randomised implementation trial of a clinical AI technology in a national primary care system. The study didn’t just measure algorithm performance. It measured system performance. It generated real-world evidence on adoption curves, workflow barriers, usage decay, and the gap between per-protocol efficacy and intention-to-treat effectiveness.

That’s exactly the kind of evidence loop Clinical Design demands. Not “does the model work in the lab?” but “does the system work on a Tuesday?”

The answer (honestly reported) was: the model works, the system doesn’t. Yet.

That honesty is more valuable than a hundred press releases about AUC scores.

I — Interoperability

This is where the disconnect was most obvious.

The AI stethoscope was not integrated with electronic health records, and results were stored on the manufacturer’s cloud platform. Clinical users were encouraged (not required) to label recordings with patient NHS identifiers. Only 49% of recordings (6,224 of 12,725) were actually linked to patient identifiers.

In Clinical Design terms: the data didn’t flow to the right place, at the right time, with the right quality.

Contrast this with EAGLE (the closest US comparator trial)6 where the AI result was embedded in the digital ECG report and linked to an automated EHR alert recommending echocardiography. There, EAGLE achieved near-complete uptake. TRICORDER didn’t.

The lesson writes itself: the possibility to connect is not enough. A full embedding must be the standard.

O — Ownership

TRICORDER had institutional backing: NHS executive clinical leadership, a regional guideline for use, blanket data governance approvals for 209 practices. That’s more ownership infrastructure than most AI deployments ever get.

But ownership at the institutional level didn’t translate to ownership at the practice level. Use was discretionary, no champion was required per practice, there was no accountability for non-use, and no feedback loop between the system and the clinician.

No operational accountability means that a mandate is not a mandate, is a memo.

U — Unit Economics

No discretionary research payments or financial incentives were provided to practices. The trial deliberately avoided artificial incentive structures to preserve real-world relevance. That’s methodologically admirable, but it’s also why the economics collapsed.

In NHS primary care, GPs are operating under crushing workload pressure. Adding an uncompensated and complex workflow step with no reimbursement pathway, no time allocation, no promise of time savings, and no visible return on effort is asking clinicians to subsidize innovation with their own exhaustion.

Previous studies suggest a £2,500 saving per heart failure diagnosis made in primary care rather than via emergency hospitalisation. This means that the case for unit economics does exist. It just wasn’t surfaced, aligned, or activated at the point of care.

5. The X Verdict: Optimistic Realism

The reaction on X has been remarkably nuanced—a sign the field is maturing.

The dominant voices clustered around three positions:

“The AI didn’t fail: the system did.” This was the most common reaction. One viral thread called it the implementation paradox. The technology works; the organizational barriers (time, integration, incentives) killed it.

“This is actually good news for AI.” Bo Wang’s high-engagement post captured this angle: when used, detection rates jumped dramatically. The algorithm is validated. The problem is deployable, solvable, and well-characterized. However, that’s an optimistic take. It will take effort and ecosystem engineering to solve the disconnect. And that’s where Clinical Design enters the conversation.

“This is the wake-up call for the Implementation Era.” Multiple commentatorsincluding Topol, framed TRICORDER as the definitive proof that we’ve moved beyond the era of model building. The question now is system design. As one commentator put it: “An algorithm with 95% accuracy that sits in a drawer is worse than one with 80% accuracy that’s actually deployed.”

The sentiment snapshot: approximately 70% optimistic realism, 25% cautionary, 5% neutral shares. No significant backlash. No viral misinformation. The conversation is evidence-based and focused on deployment, exactly where it should be, and where Clinical Design shines.

6. TRICORDER vs. PRAIM: Two Trials, One Lesson

These two studies are the bookends of Clinical Design theory.

PRAIM (Nature Medicine, 2025)7: AI integrated as the viewer. Triage + safety net built into the radiologist’s native workflow. Result: +17.6% increase in cancer detection across 463,094 women. Workload reduction potential of 56.7%.

TRICORDER (Lancet, 2026)8: AI as a separate device. Discretionary use. No EHR integration. Result: algorithm works (per-protocol ×2.3 heart failure detection), but system-level impact neutral because adoption collapsed.

Same thesis. Different outcomes. The variable wasn’t the algorithm. The variable was the design.

PRAIM designed the circuit. But TRICORDER limited itself to deploying the component. And in healthcare, components don’t save lives: Integrated circuits do.

7. What TRICORDER Tells Us About the Next Decade

TRICORDER is a blueprint that tells us exactly what the next generation of AI implementation trials needs:

First: EHR-native integration is not a “nice to have.” It’s the minimum viable product. If your AI output doesn’t appear where the clinician already works, it will not succeed. Congratulations: you’ll have done a nice science project, but nothing else.

Second: Pragmatic trials of AI must measure system performance, not just algorithm performance. Kudos to TRICORDER on this one.

Third: Incentive alignment must be designed, not assumed. If using the tool costs the clinician time with no visible return, usage will decay. The unit economics must be extremely, extremely obvious to all.

Fourth: Ownership must be operational at the user level, not only institutional. A regional guideline is necessary but not sufficient. Someone in each practice needs to own the workflow, drive implementation, oversee the feedback loop, and congratulate the team on the results.

The authors themselves acknowledge this: the next phase will prioritize “seamless integration into clinician workflows (e.g., through EHR linkage) whilst considering selective population targeting and financial incentivisation.”

That sentence defines Clinical Design in everything but name.

8. Call to Action

We have entered the Implementation Era. TRICORDER and PRAIM together prove what this newsletter has argued since day one: discovery scaled. Clinical delivery didn’t… Yet.

The AI is done. The algorithms work. The performance is sufficient (barring the issue of false positives). The regulatory frameworks exist.

What’s missing is the discipline of designing systems that let innovation reach the patient.

That discipline is Clinical Design. And TRICORDER is its most powerful case study to date.

The AI is not failing, but the system is. Now let’s fix the system.

Why I’m doing this: I believe the next 10 years won’t be defined by who discovers the next molecule, but by who figures out how to deliver it.

Whatever your role (clinician, founder, investor, or policy maker) we are all architects of this new system.

Let’s build.

— Marcos

Subscribe now

Note & disclaimers:

• Context: The Clinical Decade (and this article) explore the theoretical foundations of Clinical Design, a teaching framework created by Marcos Gallego. It has been developed through independent research and academic activities, and is shared here as a personal contribution to the field.

• Independence: Views and materials published in The Clinical Decade are personal/independent and do not represent any employer, client, or institution.

• License: Licensed under Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0), unless otherwise stated.

January 2026 broke that pattern.

OpenAI shipped a dual strategy in the same week: ChatGPT Health for consumers, plus OpenAI for Healthcare for hospitals.1 Days later, Anthropic answered with Claude for Healthcare and an expanded Claude for Life Sciences stack. 2

Now the system has to react. Clinicians, payers, patients, and builders are no longer debating “should LLMs be in healthcare?” They’re dealing with an early version of what it feels like when they are.3

This is the moment Clinical Design starts to matter.

Because models don’t scale in healthcare. Systems do.

So I’m going to grade these launches using the Vowels framework: AEIOU.

The Vowels of Clinical Design (AEIOU)

A — Adoption: does it show up naturally inside the human moment?
E — Evidence: can it prove value continuously, in messy reality?
I — Interoperability: does data flow with meaning into decision points?
O — Ownership: who holds the pager, the risk, the rollout?
U — Unit Economics: who pays, who benefits, what keeps it alive?

Read my previous article here where I introduce the academic framework of Clinical Design, which will serve as lens throughout this roadmap.

Abstract (TL, DR)

• ChatGPT Health is an Adoption wedge. It rides an existing behavior and makes it coherent. Its main constraint is interoperability with the clinical system: it connects well to you, less well to care delivery.

• Claude for Healthcare is a systems wedge. It targets the administrative and clinical backbone, with a compliance posture and integration ambition that matches enterprise reality. Its main constraint is friction: if the connector layer feels brittle, it won’t survive frontline usage.

• Both are early. The next 3–6 months won’t be decided by model quality. They’ll be decided by who ships the missing vowels.

0) Mapping the strengths of both solutions reveals two very different strategies

I mapped both launches onto a typical patient journey—from lifestyle and prevention, through first symptoms, workup, diagnosis, treatment decisions, operational logistics, follow-up, and long-term recovery. The infographic isn’t trying to predict winners. It’s showing where each product naturally “lives” today, where it’s merely supportive, and where it’s missing the bridge into the clinical moment. The teal intensity is deliberate: darker boxes mark the steps where the product is structurally advantaged; lighter boxes mark where it’s present but not yet workflow-native. Read it as a delivery map—because in healthcare, the model is rarely the constraint. The journey is.

1) ChatGPT Health — AEIOU

We have now analyzed ChatGPT Health with the Vowels of Clinical Design framework developed by Marcos Gallego.

OpenAI’s consumer bet is straightforward: a dedicated Health space in ChatGPT, grounded in personal context via connectors (medical records and wellness apps).4

A — Adoption

This product wins because it formalizes something people already do.

Power users had already been managing health through ChatGPT: uploading labs, histories, preferences, then iterating over time. The launch experience feels like that pattern, with live connectors and a clearer health-native home.5 Chat GPT already fields a massive scale of health usage at consumer level.6

Adoption risk shows up in two places:

1) Seam friction.
Early user reports describe small but real setup quirks (files, web/mobile inconsistencies). In consumer health, the smallest seam becomes the exit ramp. People don’t troubleshoot their wellbeing.

2) Proactivity.
Right now the product shines when the user behaves like a project manager. Example: you ask, “Do my steps correlate with sleep?” then iterate: “What about resting heart rate before bed?” then discard weak hypotheses in seconds.7 That’s powerful for a self-tracker. It’s not how most people behave. If the system can’t surface “what changed” on its own, it will skew toward power users and plateau.

E — Evidence

ChatGPT Health’s best feature is the evidence loop: fast hypothesis testing on your own data.

It’s also where reality bites.

One physician tester reported the model calling a cycle correlation “significant” on n=3, then losing the correlation when pressed to go deeper.8 That’s not a scandal. It’s a design requirement: sample-size awareness, data-quality checks, uncertainty signaling, and a bias toward “insufficient data” when the inputs are thin.

Healthcare evidence doesn’t tolerate confident guesses. Especially when the user doesn’t know they’re guessing too.

I — Interoperability

This is where precision matters: interoperability with what.

ChatGPT Health is strong on user interoperability: it connects to consumer sources (Apple Health, nutrition apps, wellness platforms) and gives the user a unified conversational layer.

Its weakness is corporate interoperability: the care delivery world. EHR-native workflows. Clinical note text. Provenance. The difference between “I have my labs” and “my care team can act on this.” 9

OpenAI has a separate enterprise answer—OpenAI for Healthcare—so the real question is the bridge: how (and whether) insights can move from the consumer Health space into clinical workflows safely, with consent, audit trails, and accountability. That coexistence is implied, but not yet fully legible in product form.

A clean way to say it: the product currently connects the user to their data better than it connects the user to the system that treats them.

O — Ownership

Consumer ownership is diffuse by default. The user owns the habit. Nobody owns the outcome.

Enterprise ownership exists on paper: governance, compliance, roles, clinical leadership.
The missing piece again is the handoff. Once patient-side intelligence starts influencing clinician behavior, someone needs to own how that intelligence is generated, presented, and trusted.

U — Unit Economics

ChatGPT Health threatens “health information discovery” as a category. Reuters noted that huge volumes of health questions already flow through ChatGPT weekly.

But monetization depends on what it becomes:

• a sticky wellness layer that drives retention and paid plans, or

• an on-ramp into clinical journeys (referrals, follow-ups, documentation, longitudinal care)

Right now it reads closer to the first. The intention to move into care delivery is visible in the enterprise suite, but the consumer product still looks like it could remain in lifestyle territory.

Net: ChatGPT Health is the Adoption play, with a clinical interoperability gap that will define whether it becomes infrastructure.

2) Claude for Healthcare — AEIOU

We have now analyzed ChatGPT Health with the Vowels of Clinical Design framework developed by Marcos Gallego.

Anthropic’s bet is enterprise-first: HIPAA-ready posture, connectors into the backbone (CMS coverage rules, ICD-10, NPI registry, PubMed), and explicit FHIR development skills.

A — Adoption

The target is right: clinicians and operators want less administrative debt.

Prior auth, claims support, care coordination, coding. These are adoption-positive domains because they’re painful, measurable, and budgeted.

The risk is friction.

A sophisticated clinician reported issues connecting health records through Anthropic’s connector flow—spinners, missing data, unclear manifests, and the model failing to query supposedly ingested records. This is the kind of failure mode that kills usage even when leadership is excited.

Frontline adoption is not won in procurement. It’s won in reliability.

E — Evidence

Claude’s advantage: many of its core use cases have built-in metrics.

Time-to-authorization. Denial rates. Documentation time. Claims throughput. Message triage latency.

Evidence can be continuous because operations already produce the numbers. That’s a strong setup for an evidence loop.

I — Interoperability

This is where Claude is most aligned with Clinical Design.

Connecting to coverage databases, coding systems, registries, and literature isn’t glamour. It’s how products become part of the machine.

The trap is usability: “connected” isn’t enough. Josh Mandel’s note about missing clinical note text is a perfect example of partial reality. Structured FHIR without narrative notes is not what clinicians experience as “the chart.” 10

O — Ownership

Enterprise posture creates ownership: BAAs, access controls, governance, auditability.
Operational workflows also come with owners—revenue cycle, utilization management, clinical ops. That’s why enterprise-first products have a structural advantage in ownership.

U — Unit Economics

Claude has legible ROI because it targets expensive friction:

• admin labor

• denials and delayed revenue

• burnout and turnover

The only landmine is misaligned incentives between payer and provider. Claude will have to sell into the budget owner who captures the value, or prove value is shared.

Net: Claude for Healthcare is the systems play. It will live or die on frictionless embedding and “usable interoperability.”

3) The Comparison: Two Wedges, Two Constraints

OpenAI is trying to turn the patient into the interface, then connect that interface to hospitals.
Anthropic is trying to make the hospital the platform, then extend outward to patients.

So the constraints differ:

• ChatGPT Health fights data reality: fragmented records, inconsistent labs, uneven access to EHR-grade context.

• Claude for Healthcare fights workflow reality: if ingestion and connectors are brittle, frontline usage collapses.

You can see the mirror:

• ChatGPT Health: Adoption ahead of corporate interoperability

• Claude for Healthcare: interoperability ambition ahead of frictionless adoption

The Big question: 3–6 Months from Now

Neither of these is designed for the tool graveyard. Not yet.

But in a few months, the shape will be obvious.

• Does ChatGPT Health become an on-ramp into care, or stay a self-contained lifestyle layer?

• Does Claude turn enterprise readiness into daily reliability, or remain impressive but fragile at the edges?

The healthcare AI revolution won’t be decided by who has the smartest model.

It will be decided by who can ship delivery as a system. By who can ship the vowels.

Why I’m doing this: I believe the next 10 years won’t be defined by who discovers the next molecule, but by who figures out how to deliver it.

Whatever your role (clinician, founder, investor, or policy maker) we are all architects of this new system.

Let’s build.

— Marcos

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Note & disclaimers:

• Context: The Clinical Decade (and this article) explore the theoretical foundations of Clinical Design, a teaching framework created by Marcos Gallego. It has been developed through independent research and academic activities, and is shared here as a personal contribution to the field.

• Independence: Views and materials published in The Clinical Decade are personal/independent and do not represent any employer, client, or institution.

• License: Licensed under Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0), unless otherwise stated.

The result is Pilot Purgatory—a graveyard of brilliant algorithms and therapies that never reached scale.

Today, we stop diagnosing. Today, we formalize the solution.

It’s time to introduce a new discipline: Clinical Design; and its central framework, the Vowels of Clinical Design.

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Abstract (TL;DR)

• The Definition: Clinical Design is the simultaneous engineering of healthcare innovations and the care delivery environment. It is the discipline that turns biomedical discovery into scalable reality.

• The Framework: To succeed, you must solve for the Vowels of Clinical Design (AEIOU): Adoption, Evidence, Interoperability, Ownership, and Unit Economics.

• The Laws: Friction beats accuracy. The clinical moment is sacred. Incentives are part of the product. Evidence is a loop.

• The Reality: If you miss any one vowel, you don’t have a product. You have a science project.

1. The Canonical Definition

Let’s clear the air immediately.

When we say Clinical Design, we are not talking about aesthetics. We are not talking about a prettier app or a sleeker logo. And crucially, we are not talking about clinical trial design.

The definition: Clinical Design is the simultaneous engineering of healthcare innovations and the care delivery environment.

It is the engineering of the socio-technical system that ensures an innovation is actually used, maintained, and creates value in the real world, by aligning workflow, incentives, evidence, interoperability, and accountability.

It operates on a premise the industry still resists:

Implementation is not a logistics problem. It is a dual engineering challenge.

Just as we engineer molecules for safety and digital tools for stickiness, we must engineer environments for adoption.

2. The Outcomes: What Clinical Design Optimizes For

Clinical Design is a discipline with five measurable outcomes. If these don’t move, you didn’t design delivery: you shipped a science project.

To move this from philosophy to engineering, we need a clear framework: call it the Vowels of Clinical Design.

A — ADOPTION (The Human Layer)

• Focus: Frictionless workflow integration.

• The Reality: If your innovation requires a separate login, a second screen, or 15 clicks, it won’t be used. Not because clinicians are irrational—but because innovation usually dies on a Tuesday at 15:00 AM by adding cognitive load to an exhausted clinician.

• The Goal: Move from “Click Fatigue” to “Invisible Tech.” The best clinical design is the one the user barely notices. The solution is felt in time saved and errors avoided, not in attention demanded

• Key Question: Does this exist naturally inside the clinical moment?

• Stakeholders: Clinicians, nurses, operators

E — EVIDENCE (The Validation Layer)

• Focus: Continuous Real-World Evidence (RWE).

• The Reality: Regulatory clearance (FDA/CE) is permission to market, not proof of value. Static studies do not predict dynamic performance.

• The Goal: Shift from Evidence as an “Event” to Evidence as a “Loop.” Continuous monitoring of safety, drift, and outcomes.

• Key Question: Can we prove clinical value at run-rate, not just accuracy in the lab?

• Stakeholders: Payers, Regulators, Medical Directors

I — INTEROPERABILITY (The System Layer)

• Focus: Data Liquidity and Semantic Context.

• The Reality: Healthcare data is “dirty ore.” Most innovations fail because they create a “Fragmentation Tax”—adding yet another isolated silo to the stack.

• The Goal: Moving beyond “having an API” to Semantic Reality. Data must flow with meaning to the right place at the right time.

• Key Question: Does the data flow without friction, or are we building another walled garden?

• Stakeholders: CIOs, IT, Hospital Data Managers

O — OWNERSHIP (The Governance Layer)

• Focus: Accountability, Leadership, and Safety.

• The Reality: The “Orphan Pilot” problem. Projects fail because responsibility is diffused. Who drives this project forward? Who will get their hands dirty implementing the work? Who owns the risk if the AI hallucinates? Who drives the co-development?

• The Goal: Clear Human-in-the-Loop governance. Ownership is not just about who gets the credit; it’s about who drives, who pushes, and who holds the pager when things break. It turns “users” into “leaders.”

• Key Question: Who is the captain of this ship?

• Stakeholders: Solution owners, pilot owners.

U — UNIT ECONOMICS (The Financial Layer)

• Focus: Sustainable ROI and Incentives.

• The Reality: Misaligned incentives are the silent killer. If Pathology pays for a tool that saves money for Oncology, the tool will die.

• The Goal: A clear path from Pilot Funding to Run-Rate Sustainability: budget owner clarity, procurement reality, and measurable return per stakeholder.

• Key Question: Who pays, who benefits, and do the incentives align to keep this alive at scale?

• Stakeholders: CFOs, Hospital Leaders, Investors

3. The Physics of Clinical Design (The Inviolable Laws)

Every discipline has laws. Ignore them at your peril.

Law 1 — Friction beats accuracy

A 99% accurate algorithm that adds 10 clicks will fail.
A 90% accurate one that saves 10 clicks will scale.

In care delivery, time is the scarcest resource—not intelligence.

Law 2 — The clinical moment is sacred

If your tool doesn’t exist inside the decision window—inside the viewer/EHR/bedside moment—it doesn’t exist.

You can’t ask clinicians to leave reality to use your product.
Your product must go to them.

Law 3 — Incentives are part of the product

You cannot design the tool without designing the payment logic.

If your tool reduces readmissions (saving the payer) but reduces hospital revenue (hurting the provider), you built a product your customer has an incentive to kill.

Law 4 — Evidence is a loop, not an event

The old model: Study → Approval → Launch
The Clinical Design model: Launch → Design → Refine both → Prove

Software is a living organism. Evidence generation must be alive too.

4) The Blueprint (What changes from here)

Discovery gives us bricks: molecules, models, devices. Clinical Design is the blueprint.

It ensures the building is:

• desired (Adoption),

• habitable (Evidence),

• connected to the grid (Interoperability),

• taken care of (Ownership).

• and economically viable (Unit Economics).

We spent the last decade obsessed with the bricks. It’s time to obsess over the blueprint.

Welcome to the discipline of Clinical Design.

Next week

I’ll publish the Clinical Design Stack (how to pronounce each vowel) and the Clinical Design Loop (how to build the house): a repeatable method you can use to evaluate any innovation in minutes and see exactly why it will scale (or die in pilot purgatory).

— Marcos

Note & disclaimers:

• Context: The Clinical Decade (and this article) explore the theoretical foundations of Clinical Design, a teaching framework created by Marcos Gallego. It has been developed through independent research and academic activities, and is shared here as a personal contribution to the field.

• Independence: Views and materials published in The Clinical Decade are personal/independent and do not represent any employer, client, or institution.

• License: Licensed under Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0), unless otherwise stated.

Executive Summary (TL;DR):

• The Paradox: Clinical technology has failed to increase productivity. For every hour with a patient, doctors now spend nearly two hours feeding a screen.

• The Trap: We optimized for “Approval” and “Big Problems” on paper—and ignored the “Last Mile” reality of care delivery.

• The Shift: 2015–2025 was the Era of Blind, Linear Development. 2026–2036 will be the Era of Clinical Design.

• The Proof: PRAIM (Nature Medicine, 2025) shows that **workflow design—not just the algorithm—**drove a +17.6%increase in cancer detection.

• The Promise: We cracked the code of biology. Now we must crack the code of implementation.

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In medicine, cutting-edge technology has often decreased productivity instead of increasing it.

The metrics are brutal. Studies (like the Sinsky et al. report) show that for every hour a doctor spends with a patient, they spend nearly two hours interacting with the EHR and other desk work.1

Meanwhile, the world is drowning in data. In 2025, we generated roughly 175 zettabytes globally—about 175 trillion gigabytes.2 But the bottleneck isn’t volume. It’s interoperability, meaning, and governance. Healthcare is a rounding error in the datasphere… until you try to move a single imaging study across institutions.

And in this industry, we keep pretending data is “liquid currency.”

It isn’t.

It’s dirty ore: hard to find, hard to share, full of legal and regulatory hazards, and often unusable.

The result is a tragic paradox: we can cure diseases that were fatal twenty years ago—yet in many cases, a patient’s location and access to top-tier care can rival the impact of their genetic code on outcomes.

We have built the science of the future on the infrastructure of the past.

Discovery scaled. Clinical delivery didn’t. Yet.

Welcome to The Clinical Decade.

2. The Diagnosis: The Incentive Trap

Why does a sector that attracts the world’s brightest minds feel so broken at the point of care?

The answer is uncomfortable: we spent two decades optimizing for Funding and Approval, not for Reality.

We assumed that if the science was sound, the system would absorb it.

We were wrong.

Today we’re stuck in a trap defined by three disconnects:

Regulation ≠ Adoption

“Clearance tells you it’s safe; it doesn’t tell you it fits Tuesday at 11am in the clinic.”

We built an ecosystem designed to jump through regulatory hoops (FDA, CE, GDPR). But clearance is permission to market—not a guarantee of utility. We confused safe to use with easy to use.

Procurement & IT Constraints

“If it’s not in the viewer/EHR where the decision happens, it’s not real.”

We gave clinicians AI tools—and trapped them inside closed systems or endless “pilot purgatories.” Innovation dies at the hands of legacy IT lock-ins and procurement cycles that reward lowest cost over interoperability.

The Payment Disconnect

“Budgets are siloed; benefits are systemic.”

We built million-dollar gene therapies and tried to shoehorn them into reimbursement models designed for aspirin. Value accrues to the patient and society; costs land on a department line item. The incentives don’t meet.

We have mastered the science of discovery.

We are failing the science of delivery.

3. The Shift: From Discovery to Design

History will likely remember 2015–2025 as the end of the Era of Blind, Linear Development—the golden age of the “What.”

Biologics. mRNA. CRISPR. Multimodal diagnostics. Biomarker-driven therapies.

The biological hardware was built.

But 2026–2036 will be different.

This will be the Era of Clinical Design.

And let’s be precise about what “design” means here.

Clinical Design is not about a prettier UX or a sleek app.

It’s the discipline of designing the socio-technical system: the interplay between clinicians, workflows, incentives, evidence, data, and accountability.

Real innovation fails in the Last Mile because we treat implementation as an afterthought—as a logistics problem—when it’s actually an engineering problem.

To move from aspiration to reality, we need a new architecture.

A framework that bridges state-of-the-art science with world-class outcomes.

I call this Clinical Design.

Not the product. The ecosystem around the product. The missing discipline that turns biomedical innovation into adoption, evidence, and ROI.

(Next week, Part II: I’ll publish the 5-layer Clinical Design framework—and how to apply it.)

4. The Proof is in the Design: The Vara Case Study

We don’t need to imagine this future. We’re already seeing glimpses of it.

Consider PRAIM (Nature Medicine, 2025)3: a massive real-world implementation of AI in breast cancer screening across 12 centers in Germany.

They didn’t drop a high-AUC model into a hospital and hope for magic.

They designed the workflow.

The AI (Vara) was integrated as a viewer with two explicit design functions:

• Normal Triage: flagging “very normal” studies to reduce workload.

• Safety Net: alerting only after a radiologist dismissed a case the AI found suspicious.

The result: across 463,094 women, the AI-workflow group saw a +17.6% increase in cancer detection (6.7 vs 5.7 per 1,000) without increasing recall. The safety net triggered 3,959 times; radiologists accepted the second opinion 1,077times—leading to 204 cancers that would likely have been missed.

Post-hoc analysis suggested that if “very normal” cases were autonomously filtered, reading workload could drop by about 56.7%.

That’s the point.

The productivity didn’t come from deep learning alone.

It came from circuit design: triage + safety nets + integration into the real decision pathway. And when the viewer didn’t fit consensus workflow (e.g., missing synced zoom), adoption dropped.

All of this is Clinical Design in action.

Friction beats accuracy.
The real bottleneck is integration, not inference.

5. The Roadmap: What to expect from The Clinical Decade

This newsletter is my attempt to map this transition. I’m not here to hype press releases. I’m here to dissect the machinery of healthcare.

As a geneticist turned strategist, I look for signal in the noise. In The Clinical Decade, we will explore:

• The Unit Economics of Innovation: how do we make precision medicine solvent for public systems?

• Delivery is a Science: adoption as a discipline—change management, implementation science, operating models.

• Infrastructure Debt: legacy EHRs and data silos as the technical debt of the clinic.

• Adoption Metrics: Time-to-Value, Clicks Saved, Pajama Time, % Eligible Patients Reached.

• Europe vs. USA: why startups fail differently—payer fragmentation vs public procurement and multi-country scaling.

6. The Call to Action

The era of linear innovation in healthcare is over.

The era of system design has begun.

If you are a builder, a translator, or simply someone who believes the future of health should be evenly distributed—not just evenly discovered—you’re in the right place.

We have the science. Now let’s build the system.

Next Monday: I’ll unveil the Clinical Design Framework—the five pillars required to cross the Last Mile of innovation.

Don’t miss it.

Welcome to THE CLINICAL DECADE

Why I’m doing this: I believe the next 10 years won’t be defined by who discovers the next molecule, but by who figures out how to deliver it.

Whatever your role (clinician, founder, investor, or policy maker) we are all architects of this new system.

Let’s build.

— Marcos

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