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Systems of Observation Imaging, labs, diagnostics — answer one question, then go quiet.

— the missing pillar —

The Clinical
Context Layer

System of Record The EHR — preserves what was decided, not why.

Healthcare has two pillars for holding information. Neither one holds what connects them — the evolving picture of a patient that changes with every new signal.

The problem

Two mature pillars. Nothing in between.

Every clinical fact a patient generates lands in one of two places — and neither one was built to hold the relationships between facts, only the facts themselves.

Systems of Observation

Imaging, labs, diagnostics, monitoring.

Designed to let a specialist answer one specific clinical question — then the data goes quiet.

System of Record

The EHR: diagnoses, medications, procedures, orders.

Excellent at preserving what was decided. Silent on the reasoning that produced it.

What gets lost

Relationships, trajectories, uncertainty, sub-clinical observations, and their changing significance over time.

Patient and caregiver input, continuous-device signals — speculative findings with nowhere to go.

The missing third layer

A living model of what everything known means for this patient, right now.

Queryable, durable, and pattern-rich — it lasts the life of the patient, not the length of one question.

Healthcare preserves observations and conclusions. Context preserves the evolving meaning that connects them.
The alternative

Messy — but a structured mess

Not a system of record. A system of information: it holds the entire settled record, every subclinical signal, and the connections between them.

The entire settled record

Not just the focus area of present concern — the whole history, carried forward.

Every subclinical signal

Patient and caregiver input, remote device data, anomalies never formally reviewed, speculative notes with nowhere else to go.

The connections between them

Semantically rich links between observations — as important as the observations themselves.

Its job is not to produce a definitive finding. It is there to generate patterns that are discoverable — and once discovered, those patterns can be raised to clinical validity. Uncertainty, ambiguity, and nuance are central to a system of information, not defects to be resolved away.
Portrait of Keith Deutsch

Keith Deutsch

Co-Founder — in his own words

Consider the settled record for a cardiac patient — in this case, my co-founder Keith's own story, in his own words.

A couple of years ago, I had a pacemaker implanted to address an AV block. Naturally, one of my first questions was why the block had occurred in the first place. The answer, as is sometimes the case in medicine, was essentially that no clear cause could be identified (idiopathic causation, a wonderful phrase).

A few months later, I underwent a cardiac MRI. In my mind, this represented an extraordinarily detailed inspection of my heart. If there was a clue to the underlying cause of the condition, surely this was the kind of diagnostic procedure that might reveal it. The amount of information being collected seemed immense, and I found myself assuming that somewhere within that data there might be insights that had not previously been available.

When the results came back, however, the discussion focused entirely on a much narrower question. The MRI confirmed that the pacemaker implantation had healed properly and that everything appeared to be functioning as expected. From a clinical perspective, this was entirely appropriate. The imaging had answered the question that had motivated the study. What struck me afterward was not that the analysis had been inadequate. It was that the richness of the underlying data seemed wildly disproportionate to the narrowness of the question being asked of it.

Here was an extraordinarily detailed representation of a human heart. Significant expertise had been involved in acquiring it. Significant cost had been incurred. Yet once the immediate clinical question had been answered, the images themselves effectively disappeared from the ongoing clinical narrative. They were archived, preserved, and available if someone chose to revisit them, but for practical purposes they had become inert.

The more I thought about it, the more I realized that this is not unusual at all. In fact, it is deeply embedded in the way modern healthcare information systems operate.

A CT scan, an MRI, a laboratory panel, or a diagnostic procedure is typically performed in response to a specific question. The resulting data is analyzed, interpreted, and summarized. What ultimately becomes part of the patient’s longitudinal record is not the data itself, but the conclusion. The healthcare system is remarkably good at preserving answers.

What it is far less equipped to do is preserve the broader space of questions that might later be asked as new information emerges. That distinction may not have mattered very much when medicine was primarily episodic. Increasingly, however, I am beginning to wonder whether it matters a great deal.

Why now

The old cycle no longer holds

A specific clinical question, followed by a bounded episode of care, was the assumption underneath both existing pillars. Two things broke that assumption at the same time.

Diagram: the old episodic cycle of visit, admission and follow-up with unobserved gaps between them, compared with today's continuous signal from wearables, implantables, home monitoring, and patient and caregiver reports.

Remote monitoring, wearables, and implantables have made observation continuous rather than episodic. Patient and caregiver reporting now fills the gaps between encounters — arriving constantly, unvetted by a specialist.

There isn't enough room in hospitals for every patient who needs monitoring. Care is moving into the home — which means what's being preserved is no longer a conclusion. It's a working model, updated continuously.

Patient state is becoming continuously observable faster than it is becoming coherently interpretable.
The technical thesis

AI needs context. Context needs AI.

The relationship runs in both directions — and most platforms only build half of it.

AI makes a living Context Layer possible

Semantic integration across records, devices, patients, and caregivers — continuous synthesis of relationships, trajectories, and changing significance.

But AI without context is constrained

EHR-centered AI sees the settled record, not the full evolving patient model. Useful for automation and summarization — not deep clinical reasoning.

Clinical reasoning needs a different substrate

Reasoning over uncertainty, relationships, and continuously changing state — without context, increasingly autonomous clinical AI risks acting on an incomplete model.

AI can automate the record. Clinical reasoning requires context.
How it works

What happens when something changes

Every new fact — a lab result, a medication change, a symptom report — moves through the same five stages before anything is shown to a clinician.

  1. 01

    An event arrives

    A lab result, a medication change, a symptom check-in, a treatment falling due — anything that could change the picture.

  2. 02

    Dispatch

    The layer chooses which reasoning agent should look at it, and records why.

  3. 03

    The agent reads

    The patient's full context, the Concerns a clinician has asked it to watch for, and the Constraints on what must not be done — written by clinicians in plain clinical language, not coded.

  4. 04

    It reasons

    One planning step over everything it read — not a chain of if-then rules.

  5. 05

    It produces a plan

    Register, propose, or withhold with a reason. Most of the time, silence — updating the picture and telling no one — is the right answer.

Two rules the system cannot break: it never asserts a clinical fact about a patient on its own — a clinician proposes and confirms. And it proposes; a clinician disposes. No clinical action is automated.
What it actually holds

One patient, fully connected

Every observation, clinician note, and device signal for a single patient — held as one queryable model, not a set of disconnected reports.

Clinical Context Layer

Holds a patient's whole clinical picture, so a signal in one part of their care can shape a decision in another.

SpecifiedPrototype nextNot in clinical use

What breaks today

Every signal judged alone, against a threshold.

  • Weight readingunder thresholdnothing
  • Lab resultover thresholdan alert
  • Symptom reportno rule for itdiscarded
Alerts clinicians ignoreConnections nobody makes

Where it sits

  • Health record
  • Lab feeds
  • Monitoring devices
  • Patient check-ins
Clinical Context Layer

Holds the picture. Reasons over it. Decides what a clinician needs to see.

The clinician

Makes every clinical decision. Nothing reaches a patient without them.

Beside the health record and the device platforms. Replaces neither.

What happens when something changes

1An event arrives
  • Lab result
  • Medication change
  • Symptom check-in
  • Treatment due

Anything that could change the picture.

2Dispatch

Picks the agent. Records why.

3The agent reads
  • Patient context the living picture
  • Concerns what to watch for
  • Constraints what must not be done
  • Policy whether it may run
  • Effect contract what it may produce

Authored by clinicians in plain clinical language.

4It reasons

One planning step over everything it read.

5It produces a plan
RegisterUpdate the picture. Tell no one.
ProposeA suggested action to a named clinician, with evidence.
Withhold, with a reasonSay nothing, and record why.
Every run records what it read, what shaped the plan, and what it produced. The trace a clinician or a regulator would be shown.

Two rules the system cannot break

It never asserts a clinical fact on its ownRegister, suggest, withhold. Never decide.
It proposes; a clinician disposesNo clinical action is automated. Their decision re-enters as a new event.

Conditions are authored, not coded. Adding a condition does not mean writing software.

The complete picture

The architecture, in one page

What breaks today, where the layer sits, what happens on every change, and the two rules it cannot break — the reference our own team works from.

From our internal architecture reference

Want to go deeper on the architecture?

Talk to us directly about how the reasoning core is built — what's specified, what's being built next, and what we'd want to validate together.

Get in touch

Tell us where you sit in this

Device vendor, hospital-at-home platform, data integration layer, or something else — tell us a little, and we'll follow up directly.

Deep dive into the architecture — how the reasoning core is built, what's specified, and what's coming next.

Product vision, roadmap, and investments — where this is headed and how to get involved.

From chronic heart and kidney disease to complex oncology care — anywhere a patient's story spans more than one specialist, and more than one system.