Clinical standards and metadata
Clinical-data ingestion and harmonization, CDISC SDTM and ADaM mappings, and metadata-driven pipelines with explicit lineage.
Pharma · Life Sciences · CRO
Clinical Data Engineering & Automation
Our primary specialization connects clinical-data standards, statistical programming and production software. We support Pharma, biotech, CROs and biometrics teams from ingestion and harmonization through controlled QC and reporting.
Statistical programming and QC
ADSL, ADAE and ADLB datasets, TLF generation, automated quality checks and Pinnacle 21 integration with controlled review of findings.
Interoperability and modernization
SAS, R and Python interoperability, legacy workflow modernization, reproducible reporting, APIs and analytical applications.
Pharma · Life Sciences · CRO
Typical deliverables
Source-to-target mapping specifications and implemented pipelineQC outputs, review records and data-lineage documentationReproducible reporting and validation-supporting evidence
Discuss a clinical-data workflowFAQ
Frequently asked questions about clinical data automation
Which clinical data workflows can be automated?
Suitable workflows can include ingestion, harmonization, quality checks, validation rules, recurring tables and reports, dashboards, exports and controlled hand-offs between systems. Automation scope depends on the study, quality system and regulatory context.
Can existing R and Python workflows be modernized?
Yes. We assess dependencies, reproducibility, test coverage, documentation and operational risks before restructuring legacy scripts into maintainable and traceable pipelines.
How is traceability supported?
Typical controls include versioned code and configuration, recorded inputs and outputs, deterministic execution where appropriate, validation evidence, access control and documented review steps.
Does automation replace quality or regulatory review?
No. Automation supports consistent execution and evidence generation, but accountable domain, quality and regulatory review remains part of the operating process.
Required inputs
Source schemas, representative approved data, metadata, applicable standards and current R/SAS interfaces.
Customer responsibilities
Provide authorized data access, clinical/statistical reviewers and quality-system requirements. The customer retains accountable quality review and formal validation responsibilities unless separately agreed.
Acceptance criteria
Agree mapping coverage, expected dataset structures, QC checks and review gates before implementation. Review exceptions, lineage and repeatability against the approved specifications.
Contact
Discuss a clinical-data workflow
Designed to support validation under the customer’s quality system. Validation responsibilities, acceptance criteria and human review are agreed explicitly; automation does not replace accountable quality review.