Our Core Technology - MOTUS
The Contextualized Engine that Turns Raw Signal into Clinical Signal + Story
The Problem
Modern devices stream more data every day than any care team can absorb – and the bottleneck is not volume. It is context.
Vitals tell us what. Symptoms tell us where. The care team needs the why.
CAPTURED
What
Vitals – BP, HR, glucose, SpO2, weight – streaming from devices and wearables
CAPTURED
Where
Symptoms – self-reported events, side effects, flags from the patient
MISSING
Why
The behavior & context behind the reading. Without it, every alert is a guess
The volume is not the problem. The story is.
When one daily BP reading became a continuous feed across vitals, wearables, symptoms, and environment, the bottleneck stopped being measurement and became interpretation. The care team cannot absorb tens of thousands of data points per patient – not even with dashboards and insight analysis – until each signal arrives with the behavior that explains it.
YESTERDAY
1
BP reading / day
TODAY
10,000+
data points / patient / day
How MOTUS Works
A continuous, adaptive loop – not a one-shot model.
Every reading enters a personalized risk model. When something deviates from your normal — not a population average — MOTUS opens a conversation with the patient to capture the behavior behind it. The model updates. The story attaches to the data. The care team receives both.
1
INGEST
Vitals, symptoms, environment & convo — ingested continuously, not in snapshots.
2
EVOLUTION
A per-patient risk model reshapes itself with every new reading and every conversation.
CAPABILITY
Evolution · the personalized model
MOTUS builds a personalized health-risk model for each individual and reshapes it with every new data stream and conversation. "Normal" isn't a population average — it's you, this week. A reading that's fine for one patient may be a deviation for another, and MOTUS knows the difference.
PERSONAL BASELINE · LAST 14 DAYS
Normal range
Personal
Off-baseline
3
CONTEXTUALIZE
When a reading drifts off the personal baseline, MOTUS asks the patient what changed.
4
SIGNAL + STORY
Risk score, context and care priority hand off to PEL — ready to drive behavior change.
CAPABILITY
Contextualization · the story behind the data
When MOTUS detects a deviation, it doesn't just label the abnormality — it talks to the patient to capture the behavior behind it. The conversation runs over the channel the patient already uses (SMS), so what reaches the care team is the reading and the why.
MOTUS · ON DEVIATION
TODAY · 7:42 PM
Your evening BP is higher than usual today. Anything different?
Forget my meds, and had a stressful call.
Got it — I'll flag this with your care team with that context. Let's set a reminder for tomorrow morning.
CAPABILITY
From streams
story
behavior change
INPUTS
Streams, not snapshots
-
Vitals — continuous device feed
-
Symptoms — self-reported events
-
Environment — weather, activity, location
-
Conversation — patient responses
ENGINE
Contextualization
-
Evolution — per-patient risk model that reshapes from new data & conversations
-
Contextualization — on deviation, asks the patient the question that captures behavior
OUTPUTS
Risk & story together
PEL
-
Risk scores — personalized evolving
-
Context — the why behind the reading
-
Care priorities — ranked & up-to-date
-
PEL — turns it into a behavior-change plan
Why MOTUS Is Different
Legacy patient-engagement tools push messages on a schedule. MOTUS treats each patient as an evolving model and each reading as a question.
DIFFERENTIATOR · 01
Hyper-Personalized — continuously evolving
Patients change. Their behavior, vitals, recovery trajectory and tolerance all drift. MOTUS doesn't just personalize once at onboarding — it continuously re-fits a per-individual model from every reading and every conversation. A signal flagged for one patient may be entirely normal for another, and MOTUS adapts in real time.
LEGACY TOOLS
Population thresholds. One model fits all. Static rules.
MOTUS
Per-patient baseline that re-fits every day. Your normal is your normal.
DIFFERENTIATOR · 02
Contextualization — data with the story attached
Data is just signal. It's meaningless without the story behind it. MOTUS is built specifically to extract context from the patient at the moment it matters — not at the next clinic visit, not via a survey, but in the conversation that follows a deviation. The output is data the care team can act on.
LEGACY TOOLS
Raw data dumps. Alert fatigue. Numbers without reasons.
MOTUS
Every flagged reading arrives with the behavior that drove it.
Clinical Validation
Outcome from real-world deployment with post-acute cardiac patients.
ENGAGEMENT ADHERENCE
From check-the-box to consistently engaged.
36%
96%
Patient adherence to the engagement program after MOTUS personalization & contextualization layered on top — a near-3× improvement over legacy program baselines.
QUALITY OF LIFE
Recovery patients feel.
2x
Improvement in patient-reported Quality of Life across the engaged cohort — sustained, not at-onboarding only.
Systolic BP reduction, by risk tier
Effect size scales with patient risk – the highest-risk patients see the largest absolute reduction.
HIGH RISK
CHF Cohort
Post-acute congestive heart failure
11.4
MMHG
MEDIUM RISK
Hypertensive Patients
Active management cohort
6.2
MMHG
LOW RISK
Healthy Population
Preventative program participants
4.5
MMHG