Turning vital signs into life-saving intelligence.

Trained on what the monitor saw.

A Physiological Pattern Learning Model evaluates vital trajectories continuously, detects deterioration signals, and produces ranked clinical differentials with evidence links, going beyond threshold logic.

A model of the patient, not a threshold on a number.

Every vital is read as a trajectory — a thousand samples per signal in the window, not a single value — and the model learns how those trajectories move together before anything crosses a line.

Five independent reviewers. One answer.

A rapid assessment returns in seconds; a deep review runs five independent model reviewers and trained specialist screens — early sepsis, shock-perfusion — and reports where they agree and where they do not.

It says what comes next, and how sure it is.

Guarded forecasts of each vital at +15 and +30 minutes, with the uncertainty shown rather than hidden — so a ward doctor sees the fluctuation coming, not the alarm after it.

Rate, rhythm, perfusion — read as one system.

Heart rate, blood pressure and mean arterial pressure are learned together, not thresholded apart. The ECG strip is found in the frame and its beats are marked for clinician review — beat spacing, not a diagnosis.

HR · NIBP · MAP · ECG lead II

Oxygen and breath, with the context a nurse would add.

SpO₂ and respiratory rate off the glass; oxygen flow, FiO₂, PEEP and lung findings added at the bedside in one tap. The model asks for what would change its answer, and only that.

SpO₂ · RR · O₂ L/min · FiO₂ · crackles / wheeze

The screens that watch for what kills on a ward.

Trained specialist screens — early sepsis, shock-perfusion — each return a target-specific score that is reported on its own, never folded into a single number. A screen that has nothing to say abstains, visibly.

early sepsis · shock-perfusion · post-operative · cardiac · respiratory

The alert arrives before the line is crossed.

Context-enriched alerting built on physiological trajectory, engineered to reduce alarm fatigue while surfacing the signals that demand clinical attention.

forecast paths · time-to-threshold · confidence interval

The whole ward, live, on one screen.

Nursing and clinical leadership gain a structured, real-time view of every monitored bed across the ward, for handover, continuous oversight, and escalation decisions.

live bed status · NEWS2 · thresholds · critical alerts · image & video review

Hospital to ward to patient to bedside — one graph.

Unit, ward, and patient-level dashboards built for nursing and clinical leadership, real-time across all monitored beds, not just the IT team. Click a node to enter the next layer: hospital to wards, ward to patients, patient to the assessment, the pattern match, the image, the vitals.

1 hospital · wards · beds · active patients · pattern match

Additive to the hospital's systems. Never a prerequisite.

HL7/FHIR-aligned. Connects with existing EMR, escalation protocols, and ward workflows. No hospital IT integration is required for monitor reading; connection is additive, not a prerequisite.

HMS connections · FHIR bundles · versioned, tenant-scoped APIs