from first capture to final decision

Unified, inspection-ready data

 

Kernel builds a modern data foundation for research and clinical trials. We’re vendor-agnostic and standards-first, delivering reliable, analysis-ready, submission-ready datasets at program scale.

Standards-first governance

We design every study on Clinical Data Interchange Standards Consortium models—Study Data Tabulation Model (SDTM) for collection/structure and Analysis Data Model (ADaM) for traceable outputs—then annotate eCRFs to database variables, origins, and coding. CDASH, MedDRA, WHO-DD, LOINC, and other controlled terminologies keep data harmonized for 21 CFR Part 11 and ICH E6(R2) expectations.

Core technological platforms

Risk-based oversight, end-to-end visibility

We embed risk-based quality management (RBQM) with centralized monitoring, key risk indicators, and exception workflows. Tight links to your clinical trial management system (CTMS) and electronic trial master file (eTMF) keep documents, data issues, and milestones synchronized with complete audit trails and fewer surprises.

Always analysis-ready

Builds include CRF header/navigation standards (protocol/site/subject/visit), visit windows, field-level validations, unit controls, and tooltips. Automated ETL/ELT pipelines reconcile external vendors, map legacy data to CDISC SDTM/ADaM, and keep datasets clean daily—not just pre-lock.

Privacy by design

We operate under ICH E6 Good Clinical Practice (GCP) with HIPAA & GDPR  aligned workflows, role-based access control (RBAC), encryption, lineage, and full auditability. Connected eTMF/CTMS, RBQM, and CDISC standards protect patients, preserve integrity, and satisfy regulators.

Continuity, Backup & Archival

We run mirrored/failover infrastructure with scheduled disaster-recovery tests, plus daily incremental and weekly full encrypted backups to off-site/cloud storage. At study close, we create a read-only final database with complete audit trails, retain it (typically 15 years), and routinely test restores to verify data integrit

Data Lifecycle at Kernel

Data Journey: Source to Submission

Step 1
Strategy & Standards
Step 2
Design & Build
Step 3
Collection & Ingestion
Step 4
Cleaning & Reconciliation
Step 5
Ongoing Oversight
Step 6
Analysis Readiness & Locks
Step 7
Reporting & Submission
Step 8
Archival & Retention