Clinical decision support for RPM / CCM teams

Personalized risk detection. Not population thresholds.

RPM Triage learns each patient's own normal, catches real deterioration earlier, and explains every decision in plain language — so your nurses trust it, not just tolerate it.

Book a pilot call See how triage works

Built on adaptive per-patient baselines, transparent audit rationale, and human-reviewed calibration — never a black box.

Green — log Live triage readout
HR78 bpm
SpO297%
Resp16/min

Within this patient's own rolling baseline. No action needed — logged for trend review.

FHIR-formatted output
Stateless, device-key API
Deterministic audit trail
PhysioNet-validated
The problem

Static thresholds create the alarms nobody trusts

This isn't a hypothetical — alarm fatigue is one of the most documented, industry-wide patient-safety failure modes in monitored care, well before RPM Triage existed.

85–95%
of clinical monitor alarms, industry-wide, are estimated to require no clinical action at all.
80 deaths
industry-wide, from alarm-related adverse events reported to The Joint Commission's Sentinel Event database, 2009–2012.
1 threshold
applied to every patient in most legacy systems — regardless of age, condition, or that patient's own normal.

Industry-wide alarm-burden and sentinel-event figures per The Joint Commission's clinical alarm safety alert — not claims about RPM Triage specifically. The fixed-threshold description reflects standard static-threshold monitoring design generally.

How it works

Built to catch what population thresholds miss

Every reading is scored against that specific patient's own rolling baseline, dampened when context explains it, checked against a hard clinical safety floor nothing downstream can soften, then time-tracked so a transient spike never gets treated like sustained decompensation.

01

Per-patient baseline

An adaptive mean + variance per vital, learned from that patient's own history — not a population norm.

02

Contextual filtering

Dampens — never deletes — deviations explained by exercise, circadian rhythm, or degraded device signal.

03

Hard safety overrides

Absolute crisis thresholds, checked first, every time — nothing downstream can bypass or soften them.

04

Persistence & digest

A transient spike stays a spike. Sustained elevation escalates. Everything else rolls into a daily/weekly digest.

The clearest proof

Same deviation. Different context. Different outcome.

Two patients, the identical heart-rate reading — one explained, one not.

Suppressed → Yellow, logged
HR 104 bpm
150+ steps in the last 15 minutes · SpO2 stable at 97%

Elevated heart rate fully explained by recent activity — dampened to residual weight, filed for trend review, never escalated.

Escalated → Red, urgent
HR 104 bpm
Resting, zero recent activity · sustained past the persistence window

Same deviation, no contextual explanation, held for 5+ continuous minutes at rest — escalated for immediate nurse review.

Patients stay in the loop

A lightweight app patients actually use

Every patient gets their own portal — installable straight from their browser, no app store required — to report how they're feeling and message their care team directly.

Tap-to-report symptoms, plain language
Direct messaging with their assigned nurse
Installs to the home screen, works offline
Push notifications when your team replies
Validation

Measured, not asserted

Every reading run through the same pipeline a real device would use — replayed against a real, public clinical dataset, not hand-written examples.

0%
Sensitivity on adjudicated adverse events
0%
Fewer urgent-tier alerts vs. static thresholds
0
Missed adverse events in validation

Measured against real ICU waveform data (PhysioNet's BIDMC dataset), comparing our RED_URGENT (immediate-page) alert volume against a fixed-threshold baseline — re-runnable against your own data. Some of that reduction is alerts eliminated entirely; some is alerts correctly downgraded to a non-urgent nurse-review queue rather than paging immediately.

Where we are today

These numbers come from bench validation against a real, public physiologic dataset (PhysioNet's BIDMC ICU waveform data) — not a live clinical deployment. We haven't yet run this against a real RPM/CCM patient population in production, and we won't tell you otherwise.

What we're looking for is a pilot partner to change that — bring your own patient data (or use ours), and you'll see the same rationale behind every single decision, on your own caseload.

Who this is for

Two very different buyers, one engine

RPM / CCM monitoring companies

You run the multi-tenant program across many physician-practice clients. You need triage logic that scales per patient without a config file per condition, plus a device-key API your ingestion pipeline can integrate against directly.

Talk to engineering

Health systems & physician practices

Your nurses are the ones staring at the queue every morning. You need fewer, more trustworthy alerts, a plain-language rationale for every one, and an audit trail that holds up when a provider asks "why did it flag this?"

Talk to clinical ops
Questions

What people ask before a pilot

No. Every output is explicitly a Clinical Decision Support (CDS) recommendation for licensed-provider review — never a diagnosis and never a directive treatment instruction. Final clinical judgment always rests with the reviewing provider.
A disconnected device or an empty payload is routed to a device-status queue, explicitly labeled as a connectivity issue — never scored or presented as a clinical anomaly. Low-confidence or motion-degraded readings are down-weighted, not blindly trusted.
The baseline is a rolling window (weeks, not one reading). Before it's proven — fewer than 20 samples — the engine widens rather than ignores deviation, so a genuinely dangerous reading from a brand-new patient is never waved through for lack of history.
No. It prioritizes what a nurse looks at first and explains why in plain language — it never acts autonomously, and nurse feedback drives periodic, human-reviewed recalibration rather than the model silently updating itself.
Real, public ICU physiologic waveform data (PhysioNet's BIDMC dataset) — see the Validation section above, including the honest caveat that this isn't yet a live clinical deployment. That's exactly what a pilot is for.

See it on your own caseload

Bring your own patient data, or use ours — either way, you'll see the rationale behind every single decision.

Book a pilot call