01
Specialty silos
Events documented in obstetrics, gynecology, or other specialties are treated as closed episodes rather than lifelong risk markers for primary care.
Astravia Analytics
Historical clinical signals, connected in time for prevention
Astravia Analytics builds a longitudinal early-detection platform for primary care. We reconstruct siloed health history, map it onto long-term cardiometabolic and related risk, and deliver inspectable, evidence-graded reports inside the physician’s workflow.
The gap
Important history is recorded. Later care rarely uses it.
Acute or specialized events live in isolated record modules and are almost never factored into routine primary care years later. Standard calculators score today’s labs. Primary care visits are too short to reconstruct decades of chart by hand.
02
Present-day calculators
Tools such as AHA PREVENT evaluate current blood pressure, lipids, and BMI. They do not incorporate historical organ-stress biomarkers from earlier years.
03
No time to reconstruct
Physicians working in 15-minute visits cannot manually audit disparate notes to calculate long-term risk multipliers at the point of care.
Traditional CDSS / risk calculators
Reactive, present-tense, siloed
- Data horizon: today’s labs
- Manual entry on third-party tools
- Late-stage diagnosis and management
Astravia Analytics
Proactive, longitudinal, multi-system
- Data horizon: archival, cross-specialty signals
- Background extraction, then EHR-native delivery
- 10–30 year prevention outlook for primary care
Company platform
A cascade hybrid engine, reused across clinical modules.
Machine learning finds patterns in fragmented history. A deterministic rules layer converts those findings into cited, guideline-backed multipliers the physician can independently review—aligned with FDA expectations for clinical decision support.
Stage 1
Multi-agent machine learning
Specialized sub-models parse structured and unstructured records for historical risk vectors across targeted domains, then unify them into a phenotypic profile.
Stage 2
Deterministic rules engine
Stage 1 outputs become explicit risk multipliers drawn from peer-reviewed literature. Hazard ratios are cited, graded, and shown rather than collapsed into an opaque score.
Products
HerArc is the first product on this spine.
Astravia Analytics is designing clinical modules that share the same extraction, evidence grading, and EHR delivery infrastructure. Maternal and reproductive history is the first validation domain.
First product · in development
HerArc
A longitudinal early-detection engine that turns reproductive and maternal history into prevention. HerArc reconstructs pregnancy complications, reproductive events, and physiological change, then generates a clinician-ready report in which every risk multiplier is traceable to peer-reviewed evidence.
What it maps
Reproductive history → long-term risk
Preeclampsia, gestational diabetes, PCOS, endometriosis, premature menopause, and related events onto cardiovascular, metabolic, renal, and oncological outcomes.
Later modules will reuse the same architecture for additional historical risk domains.
Clinical & research inquiries
Request a company briefing.
Astravia Analytics is based in the Greater Seattle area and building for health systems nationally. We are in product development, focused on prototype validation of HerArc, clinician evaluation, and early research relationships.