Core Platform

Unified biomarker plane

One operational layer for all digital biomarkers. CGM, wearables, surveys and clinical data arrive normalized, timestamped and QC'd. Coordinators get one dashboard. Analysts skip ETL. New studies reuse infrastructure.

All Studies
Live
1,247
Total participants
2,489
Active devices
38
Biomarkers
Data sources syncing
CGM (Dexcom, Libre)
847 active
Wearables (Fitbit, Garmin)
1,102 active
Surveys (REDCap)
1,247 active
Clinical (HL7)
423 active

Without a unified plane

Coordinators juggle 5+ portals. Analysts wait weeks for exports. Each study rebuilds integrations and redefines biomarkers. Cross-cohort comparison is impossible when definitions drift.

Coordinator teams juggle 5+ vendor portals per study
Analysts wait weeks for custom data exports
Biomarker definitions drift across studies
Impossible to pool cohorts or reuse models
Dexcom Clarity
3 participants
Open portal
Fitbit Dashboard
Sync pending
Open portal
REDCap
7 incomplete
Open portal
Lab Portal
2 new results
Open portal
+ 3 more portals
Coordinator Dashboard
Study 001
94%
247 participants
CGM, Fitbit
Study 002
89%
156 participants
CGM, REDCap
Study 003
92%
312 participants
Garmin, REDCap
Last 24h data quality
12847 pass
23 flagged

One operational layer

All devices and studies feed into HealthGen. One dashboard for coordinators. Pre-normalized data for analysts. Shared biomarker library. Launch new studies in days by reusing infrastructure.

One dashboard for coordinators
Device status, adherence, data quality in one view
Pre-normalized signals
CGM, wearables, surveys land in consistent schema
Shared biomarker library
Glucose metrics, activity scores defined once, versioned
Instant study spin-up
New cohorts reuse integrations and pipelines

Data arrives normalized

Vendor-specific formats transform to a common schema on arrival. Every reading has standardized timestamps, provenance and QC flags. Query glucose or steps the same way across all studies, regardless of device.

CGM
Dexcom, Libre → glucose_mgdl with QC flags
Wearable
Fitbit, Garmin → steps_daily, sleep_hours
Survey
REDCap, Qualtrics → PHQ9_score, timestamp
Clinical
HL7, FHIR → HbA1c_percent, lab_date
Dexcom API
EGV: 128 mg/dL
glucose_mgdl: 128.0
PASS
Fitbit API
steps: 8247
steps_daily: 8247
PASS
REDCap API
phq9_total: 7
PHQ9_score: 7
PASS
GET /biomarkers/glucose
?cohort=study-001
&timerange=30d
→ {
"biomarker": "glucose_mgdl",
"participants": 247,
"readings": 124853,
"mean": 118.3,
"std": 22.7
}

Analysis-ready access

Query biomarkers through one API. Filter by cohort, timerange or participant. Export to Parquet, CSV or JSON. No waiting on coordinators for exports.

One API for all biomarkers across all studies
Filter by cohort, participant, device, timerange
Export to analysis-ready formats (Parquet, CSV, JSON)
Real-time queries for live dashboards or adherence alerts

Ready to unify your digital biomarkers?

See how HealthGen can help you ship faster, scale further, and get to insights without rebuilding the stack.

Book a demoExplore the platform