PhysioSim Systems is an API infrastructure layer that learns each individual's physiological baseline from longitudinal, multi-signal data — and surfaces persistent, meaningful deviations.
PhysioSim does not diagnose and does not make clinical decisions. It supports earlier clinical awareness. All outputs require human review before any action is taken.
Chronic care management drives over 50% of healthcare spending — largely preventable readmissions. Clinicians lack tools to interpret multi-signal trends in real time.
PhysioSim learns each person's physiological baseline from longitudinal data — and detects persistent deviation before it becomes a clinical crisis.
Population-based alert rules don't account for individual baselines. They create false alarms or miss early drift entirely.
EHRs, devices, wearables — care teams have data streams but lack tools to interpret multi-signal trends in real time.
Noisy monitoring queues erode clinical trust. Real signals get buried under false positives.
No API product exists that learns per-individual baselines at scale and routes actionable alerts into existing workflows.
PhysioSim builds a personalized model per patient — no population averages, no one-size-fits-all thresholds.
We detect persistent deviations across weight, HR, activity, symptoms, labs — frequency-domain and waveform features when available.
Low / medium / high risk output with concise, explainable reasoning — care coordinators can act without physician review for every case.
Outputs route into existing nurse queues, care coordinator tools, and clinical systems. No new dashboard required.
⚠ PhysioSim does not diagnose medical conditions or make clinical decisions. It supports earlier clinical awareness. All outputs require human review before action.
Same individualized baseline intelligence across chronic care monitoring and clinical trial safety.
For RPM programs, payers, ACOs, and health systems managing high-risk populations.
For pharma sponsors and CROs running decentralized or hybrid trials.
Patient data flows in from existing sources — EHRs, remote monitoring tools, wearables — via standard interfaces.
PhysioSim builds a personalized baseline for each patient. Each signal is characterized relative to that individual's normal range.
We flag sustained, multi-signal deviation that exceeds the patient's historical variance — emphasizing persistence over transient noise.
Alerts with concise, explainable rationale are delivered to the first-line care team — not physicians by default.
The care team reviews the alert, contacts the patient, and escalates to a physician only when clinically warranted.
This demonstration uses illustrative, fictional patient data. No real patient information is shown.
Alerts route to nurses, care coordinators, or trial safety monitors as first-line reviewers. Physicians receive escalations only when warranted.
Every alert requires human review before any action. PhysioSim surfaces risk and rationale; clinicians own the decision.
Transient anomalies are de-emphasized. Only persistent, multi-signal deviation triggers alerts — reducing queue volume and restoring trust.
Only fields required for pilot use cases are ingested. No unnecessary data collection.
Data encrypted in transit. Encryption at rest where stored. De-identified workflows supported.
Role-based access to pilot artifacts. Access logs maintained for all interactions.
Decision support only. Care team review required before any patient action.
Pre-commercial. Built with HIPAA alignment as design principle — data minimization, access controls, encryption.
Risk detection / decision support (not diagnosis). Requirements assessed per deployment.
PhysioSim is currently in pre-commercial pilot stage. Access is by approved partner arrangement only. We are working with select healthcare systems, payers, and clinical research organizations.