PhysioSim Systems is an API infrastructure layer for chronic care teams. We learn each patient's individualized baseline from longitudinal, multi-signal data — and surface persistent, meaningful deviations before clinical escalation.
PhysioSim does not diagnose and does not make clinical decisions. It supports earlier clinical awareness for care teams.
// Signal-to-action workflow
EHR / RPM
→
PhysioSim API
→
Risk + Rationale
→
Nurse Queue
→
Escalation
$108B
Annual US heart failure costs (direct + indirect)
AHA Heart Disease & Stroke Statistics, 2023
25%
HF patients readmitted within 30 days of discharge
AHRQ National Readmissions Database
$18K
Average cost per HF hospitalization in the US
HCUP Statistical Brief #228, 2022
6.7M
Americans living with heart failure today
CDC, 2023
// The problem
Patients deteriorate silently. Care teams see it too late.
Signal Overload
Care teams receive feeds from EHRs, RPM devices, wearables, vitals, and labs — but have limited time to interpret multi-signal trends. Individual signals get missed in the noise.
Static Thresholds
Population-based alert rules don't account for each patient's individual normal range. They create false alarms for stable patients and miss early drift in others.
Alert Fatigue
Noisy monitoring queues erode clinician trust. When alarms fire constantly, real signals get buried — and teams stop acting on them with appropriate urgency.
Workflow Disruption
Most monitoring tools require new dashboards, new logins, new processes. Care teams don't have capacity for another platform — they need infrastructure that fits existing operations.
PhysioSim builds a personalized physiological model per patient from longitudinal data — no population averages, no one-size-fits-all thresholds.
⟂
Multi-Signal Deviation Detection
We detect persistent, meaningful deviations across weight, HR, activity, symptoms, labs — including frequency-domain and waveform features from raw physiological signals when available.
△
Risk Stratification with Rationale
Low / medium / high risk output with a concise, explainable rationale — so care coordinators can act without physician review for every case.
⇢
Workflow-Native Delivery
Outputs route into existing nurse queues, care coordinator tools, and clinical systems. No new dashboard required. Infrastructure, not a consumer app.
⚠ PhysioSim does not diagnose heart failure exacerbation and does not make clinical decisions. It supports earlier clinical awareness. Care teams review all alerts before any action is taken.
Signal availability is source- and partner-dependent.
// How it works
Five steps from raw data to clinical action.
01
Ingest
Patient data flows in from existing sources — EHRs, remote monitoring tools, wearables, clinical data streams — via standard interfaces. Raw physiologic signals (ECG, waveforms, accelerometer) included when available.
02
Model the Individual Baseline
PhysioSim builds a personalized baseline for each patient over a defined lookback window. Each signal is characterized relative to that individual's normal range — not a population average.
03
Detect Persistent Deviation
We flag sustained, multi-signal deviation that exceeds the patient's historical variance — with emphasis on persistence over transient noise. Frequency-domain and waveform features analyzed where available.
04
Route to Nurse / Care Coordinator First
Alerts with concise, explainable rationale are delivered to the first-line care team — not physicians by default. Designed to fit existing RPM and chronic care operations staffing models.
05
Review, Contact, Escalate as Warranted
The care team reviews the alert, contacts the patient if needed, adjusts the care plan, and escalates to a physician only when clinically warranted. PhysioSim supports the decision — it doesn't make it.
// Sample demo
See the API work — with sample data only.
This demonstration uses illustrative, fictional patient data. No real patient information is shown.
POST api.physiosim.io/v1/patients/analyze
// PhysioSim Systems API — Sample Data Only
POST /v1/patients/analyze
{
"patient_id": "SAMPLE-A",
"program": "heart_failure_rpm",
"baseline_days": 60,
"signals": {
"weight_kg": 85.2,
"resting_hr_bpm": 92,
"daily_steps": 1640,
"symptom_flags": ["dyspnea"]
}
}
// Ready — press Run to simulate →
⚠ Sample data only. This is a simulated demonstration for illustrative purposes.
Sustained upward shift in weight (+2.1 kg over 6 days) with concurrent resting HR elevation (+9 bpm over 5 days) and activity decline. Symptom check-in: increased dyspnea 2 of last 3 days. Pattern persistent beyond short-term noise window. Suggested action: Nurse review → patient outreach → escalate to physician if warranted.
// Clinical workflow design
Built for RPM operations — not as a new destination.
1
Nurse-First Routing
Alerts route to nurses and care coordinators as first-line reviewers. Physicians receive escalations only when the care team determines it's clinically warranted — preserving physician time.
2
Human-in-the-Loop
Every alert requires care team review before any patient action. PhysioSim surfaces risk and rationale; clinicians own the decision. This is by design, not a limitation.
3
Noise Reduction by Design
Transient anomalies are de-emphasized. Only persistent, multi-signal deviation triggers an alert — reducing queue volume and restoring care team trust in monitoring systems.
// For investors & government partners
A large, measurable problem with a clear infrastructure solution.
Why now
RPM adoption accelerated post-pandemic; data streams exist but lack intelligent analysis layers
CMS reimbursement for RPM continues to expand, increasing payer and provider incentive to act on the data
Heart failure is the #1 driver of 30-day readmission penalties under CMS Hospital Readmissions Reduction Program
Individualized, explainable AI is now technically feasible at the patient level with longitudinal wearable data
Government payers (Medicare, Medicaid) bear disproportionate HF cost burden — strong incentive to fund early intervention infrastructure
Target buyers — US market
Commercial insurance & Medicare Advantage plans seeking to reduce avoidable admissions in high-cost HF cohorts
ACOs and value-based care organizations with shared savings incentives tied to readmission reduction
Government health programs (CMS Innovation Center pilots, VA, state Medicaid programs)
Health systems and hospital networks operating RPM programs under CMS bundled payment models
Medtech & RPM device manufacturers seeking an intelligent alerting layer on top of existing device data streams
Digital health platforms providing RPM infrastructure seeking clinical intelligence as a value-add API layer
// Market sizing (published sources)
$8.3B
Remote Patient Monitoring global market size (2023)
Grand View Research, 2024
$108B
Annual US HF costs — direct + indirect (2023)
AHA Heart Disease & Stroke Statistics
$26B+
Estimated annual cost of preventable HF readmissions
AHRQ / HCUP estimates, peer-reviewed literature
// FAQ — for healthcare teams
Common questions, direct answers.
// Security & privacy
Pilot-ready. Conservative by default.
🔒
Data Minimization
Only fields required for the agreed pilot use case are ingested. No unnecessary data collection.
🛡
Encryption
Data encrypted in transit. Encryption at rest where stored (deployment-dependent). De-identified dataset workflows supported.
👁
Access Controls
Role-based access to pilot artifacts and outputs. Access logs maintained for all data interactions.
⚕
Clinical Safety
Decision support only. Care team review is required before any patient action. No autonomous clinical decisions.
📋
HIPAA-Minded by Design
PhysioSim is pre-commercial and not yet HIPAA certified. We are built with HIPAA alignment as a design principle — data minimization, access controls, encryption, and de-identified workflows are in place. Full compliance is addressed per pilot before any real patient data is used.
⚖
Regulatory Positioning
Positioned as risk detection / decision support (not diagnosis or treatment). Regulatory requirements depend on claims and intended use; assessed per deployment.
// Licensing & access model
Built for the US market. Two ways to work with us.
PhysioSim is currently in pre-commercial pilot stage. Access is by approved partner arrangement only. We offer two primary licensing structures depending on your organization type.
For payers & health systems
Per patient-month
A recurring per-monitored-patient fee tied directly to your active HF monitoring cohort. Scales with your program — no large upfront commitment. Pricing discussed per pilot based on cohort size and data complexity.
✓ Medicare Advantage & commercial payers
✓ ACOs & value-based care programs
✓ VA & government health programs
✓ Hospital RPM programs
For medtech & RPM platforms
API platform license
An API licensing arrangement for device manufacturers and digital health platforms that want to embed PhysioSim intelligence into their existing product. PhysioSim becomes your individualized baseline layer — you own the customer relationship.
ⓘ PhysioSim is focused exclusively on the US market. API access is provided to approved partners only — no public endpoints. Pilot pricing discussed on a per-partner basis.
// Early clinical validation
What clinicians are saying.
The following are unsolicited responses from clinical professionals during early customer discovery conversations. Shared with permission, anonymized.
"By the time heart failure patients reach us in the ED, they've usually been declining physiologically for days to weeks before symptoms become obvious. We're seeing the endpoint of something that was already in motion. A system that could reliably flag sub-clinical instability around 10 days out would be very actionable — that gives outpatient teams time to adjust diuretics, review meds and labs, increase monitoring, or step in before things progress to pulmonary edema and an ER visit."
EM Resident, MPH
Yale School of Medicine · Early customer discovery, 2026
"Personalizing it to a patient's own baseline rather than population averages would make it far more clinically useful. The real challenge isn't whether the signal is there — it's making the model specific enough to minimize false alarms. From a public health perspective, this is significant. Heart failure admissions are resource-heavy and hit hardest among patients with limited access to regular outpatient care. Tools like this could reach them where traditional care often doesn't."
EM Resident, MPH
Yale School of Medicine · Early customer discovery, 2026
ⓘ These are qualitative customer discovery insights, not clinical validation data. PhysioSim has not completed a prospective clinical trial.
// From the founder
"PhysioSim is being built for real care delivery environments — high signal density, limited staffing, and the need for actionable, explainable alerts that reduce noise, not add to it."
Phoebe Garcia · Founder, PhysioSim Systems · phoebegarcia@protonmail.com
// Pilot program
Focused pilots for heart failure RPM programs — results in 4–8 weeks.
We run structured, governed pilots with health systems, RPM platforms, and payer-sponsored programs. No production integration required to start. De-identified retrospective data is sufficient for initial evaluation.
Ideal pilot characteristics
High-risk heart failure cohort with existing monitoring data
Active RPM or chronic care operations team (nurse/care coordinator staffing)
Defined escalation pathways and coverage model
Ability to share de-identified or appropriately governed data streams
Willingness to provide feedback on alert quality and workflow fit
What you'll evaluate
Earlier identification vs. existing static thresholds
Alert volume and false positive rate (vs. current system)
Nurse review burden and workflow integration
Clinical usefulness of rationale output (human-in-the-loop)
Signal-to-noise ratio across your specific patient cohort
Pilot timeline
Week 1
Alignment & Setup
Define cohort, baseline window, alert persistence thresholds, and routing (nurse-first). Align on data sharing approach and governance.
Weeks 2–3
Baseline Modeling
PhysioSim builds individualized baselines from your de-identified longitudinal data. Initial model validation and threshold calibration.
Weeks 4–6
Live Detection & Review
Deviation alerts generated and reviewed by your care team. Weekly summary of signal / noise and cases for clinical review.
Weeks 7–8
Evaluation & Readout
Structured readout: earlier identification rate, alert volume, review burden, and clinical usefulness assessment. Go / no-go for expanded deployment.
Retrospective evaluation (shorter timeline) also available for initial signal validation before prospective pilot commitment.
// Get in touch
Ready to start a pilot conversation?
We welcome enquiries from health systems, RPM platforms, payers (commercial and government), and ACOs running heart failure programs. No commitment required for an initial conversation.