01
Multi-Omics Ready
Source connectors for FHIR and HL7v2, with OMOP, NGS, and proteomics formats landing alongside them in one governed store.
Solutions · Healthcare & life sciencesGENEDATA / 01
Connect the evidence.Advance the work.
Bring clinical, research, and safety data into connected workflows with access controls and traceability at every step.
Shared context · Lineage · Governance
Connected
Your sources
Clinical records
Research data
Safety signals
Connected intelligenceHealthcare intelligence
Governance
Business impactResearch & evidence
Shared contextLineageGovernance
02
PHI by Design
Field-level encryption, k-anonymity de-identification, and tokenization built into the engine — auditable from clinic to publication.
03
PV Signal Detection
Disproportionality analysis, ML-driven adverse-event clustering, and ICSR-ready exports for FDA, EMA, and PMDA submissions.
Built On
The products behind this solution.
Clinical and research data carries the strictest handling requirements on the platform, so governance leads rather than follows.
GeneFlow
Ingests EHR, claims, omics, and trial data across FHIR, OMOP, and HL7v2 without moving PHI off your premises.
Learn moreGeneCatalog
Enforces PHI classification, de-identification, and consent at the point of query, with lifetime audit retention.
Learn moreCortex SQL
Runs cohort building, real-world evidence analysis, and regulatory submission outputs on de-identified data.
Learn moreCortex AI
Drives pharmacovigilance signal detection and federated learning without centralising patient records.
Learn moreIndustry use-case map
Healthcare & Life Sciences workflows across one governed operating model.
These reference workflows connect the business decision to the data, controls, platform surfaces, and people required to operate it in production.
Healthcare & Life SciencesReference workflow
Clinical data harmonization
Standardize clinical, claims, laboratory, and research records against governed terminology while retaining source meaning and patient protections.
How the work moves
- 01
Connect the signal
EHR, claims, and patient operations · Clinical trials and research data · Clinical terminology and master data
- 02
Apply control
Data quality contracts · Privacy and minimization · End-to-end lineage
- 03
Build and deliver
GeneFlow Engineering · GeneCatalog & Knowledge · Cortex SQL & Warehouse
- 04
Decide and act
Reusable clinical data products with consistent definitions and visible provenance.
Participating roles
Platform surfaces
Business outcome
Reusable clinical data products with consistent definitions and visible provenance.
Explore the full industry solutionPHI Pathway
Identified at the clinic. De-identified everywhere else.
The governing constraint in health data is that identified records must stay inside a narrow boundary while research needs breadth. Tokenisation at the boundary lets cohorts be assembled and studied across sources without PHI ever reaching the analytical layer — and re-identification, where clinically warranted, remains a controlled and audited operation.
How Research Runs
Ingest, protect, cohort, evidence.
Every stage is validated under GxP and recorded to a 21 CFR Part 11-aligned audit trail, so a study is defensible at submission.
Ingest clinical formats
FHIR, HL7v2, OMOP, NGS, and proteomics land through supplied connectors into one governed store.
Protect at the boundary
Field-level encryption and tokenisation apply as data lands, so PHI never propagates into the analytical layer.
Build cohorts
Assemble de-identified cohorts across sources and sites, with inclusion criteria versioned as part of the study record.
Generate evidence
Run RWE analyses and PV signal detection with outputs traceable to the exact cohort definition and data version.
Clinical Capabilities
What research and safety teams get.
Health data work is constrained less by analysis than by what you are permitted to move where.
PHI never leaves the boundary
Identified records stay in their compliance zone; analysis runs on tokenised equivalents with controlled re-identification.
Federated across sites
Query multiple trial sites or health systems without centralising their patient data into one custodial store.
Multi-omics native
NGS and proteomics handled alongside claims and EHR, so multi-modal cohorts do not require a bespoke pipeline.
PV signal detection
Disproportionality analysis and adverse-event clustering with ICSR-ready exports for FDA, EMA, and PMDA.
GxP validation pack
Installation, operational, and performance qualification documentation supplied to support your validated state.
Reproducible cohorts
A cohort definition plus a data version reproduces the exact population used in a published analysis.
Who Benefits
From bench to submission.
Research, clinical operations, and safety have very different obligations over the same underlying records.
Translational Researcher
Assemble a multi-modal cohort
Combine genomic, claims, and EHR data into one de-identified cohort without negotiating a bespoke extract per source.
Cohort construction in days rather than months.
Pharmacovigilance
Detect a safety signal earlier
Run disproportionality analysis continuously over real-world data rather than waiting for a periodic review cycle.
Signals surfaced closer to when they emerge.
Regulatory / QA
Defend a submission
Trace any figure in a filing back through the cohort definition and data version under a Part 11-aligned audit trail.
Inspection readiness as a standing state.
Versus Siloed Clinical Data
What changes when protection is built into the substrate.
Most health data architectures protect PHI by keeping datasets apart, which is also why research is slow.
| Dimension | Before Genedata | With Genedata |
|---|---|---|
| PHI handling | Protected by keeping datasets separated | Tokenised at the boundary, analysis on de-identified data |
| Multi-site studies | Centralise patient data under one custodian | Federated queries, data stays with each site |
| Cohort building | A bespoke extract negotiated per source | Governed queries across mapped models |
| Reproducibility | A cohort described in a methods section | A versioned definition that regenerates exactly |
| Inspection | Evidence assembled ahead of each audit | A continuous Part 11-aligned audit trail |
HIPAA Security RuleAligned To
FHIR · OMOP · HL7v2Data Models
FederatedTrial Sites Supported
LifetimeAudit Retention
The next step
Accelerate science. Protect the patient.
Genedata Healthcare & Life Science gives translational researchers, clinical operations, and pharmacovigilance teams the same governed substrate — so insights move faster without compromising the patient relationship.
FAQ
Healthcare and life science, answered.
What clinical operations, research, and compliance teams ask first.
Does PHI have to leave our environment?
No. The platform deploys inside your own environment, and federated approaches let multi-site analysis run without centralising patient records at all.
How is de-identification handled?
Field-level encryption, tokenisation, and k-anonymity are applied in the engine rather than by a preprocessing step, so a researcher queries de-identified data by default and re-identification is a separately governed privilege.
Is the platform GxP validatable?
The audit-trail and change-control primitives a 21 CFR Part 11 validation depends on are in place — tamper-evident audit, versioned pipelines — is available, and the platform's own audit record supplies much of the evidence.
Which data models are supported natively?
FHIR, OMOP, and HL7v2 with mappings supplied, so mapping to a common research model is configuration rather than a bespoke ETL project.
How does this support pharmacovigilance?
Disproportionality analysis and adverse-event clustering run against the same governed record, with ICSR-ready exports for FDA, EMA, and PMDA submission.
Related content
Compliance
HIPAA Control Mapping
Platform controls mapped to HIPAA safeguards with evidence.
ComplianceGxP validation notes
Which Part 11 requirements the platform's audit and change control support, and which need process outside it.
ArchitectureClinical Data Patterns
FHIR and OMOP mapping, cohort building, and federated analysis.
RelatedSecurity
Encryption, key custody, and the tamper-evident audit chain.
Take the next step
Start with one cohort.
Build a de-identified cohort against your own data model with a solutions engineer.