Clinical evidence & AI data services for regulated environments

Service · Clinical Evidence & AI Data Services

Data cataloging, governance, 21 CFR Part 11 cleansing with an audit trail, and clinical evidence compilation into a 510(k) or PMA submission dossier, for organizations where data provenance under HIPAA and ALCOA+ matters as much as model performance.

What do clinical evidence and AI data services include?

They include data cataloging, governance, 21 CFR Part 11 cleansing with an audit trail, and clinical evidence compilation into a 510(k) or PMA dossier. They serve organizations where data provenance under HIPAA and ALCOA+ matters as much as model performance.

Data cataloging, governance, 21 CFR Part 11 cleansing, and clinical evidence compilation for organizations navigating FDA premarket submissions, HIPAA boundaries, and algorithmic validation — where data provenance matters as much as model performance.

Four commitments that hold across every engagement

Provenance-First
Every dataset's lineage and PHI/ePHI boundary mapped before a single transform runs.
Audit-Ready Cleansing
Automated and expert-led cleansing validated against ALCOA+ data-integrity principles.
Protocol Interoperability
HL7 FHIR, DICOM, and OMOP mappings that hold up against a clinical system's actual data.
Submission-Grade Traceability
Evidence dossiers a reviewer can trace from raw source to submitted dataset.

Built for

  • Academic Medical Centers
  • Clinical Research Teams
  • Medical Device Companies
  • Pharmaceutical & Biotech
Clinical evidence data pipeline architecture Four-stage flow: Clinical Data Ingestion, then Automated 21 CFR Part 11 Cleansing and De-identification, then Feature and Evidence Store, then FDA 510(k)/PMA Submission Dossier, connected by directional arrows, with a dashed perimeter marking the regulated-processing boundary around the cleansing and evidence-store stages. REGULATED PROCESSING BOUNDARY Clinical Data Ingestion Metadata & Lineage Tagging PHI/ePHI Boundary Mapping 21 CFR Part 11 Cleansing Automated De-identification ALCOA+ Validation Feature & Evidence Store HL7 FHIR / DICOM / OMOP Model Validation Datasets FDA 510(k)/PMA Submission Dossier Statistical Analysis Plans Audit-Traced Evidence
DATA-SPEC 01: Clinical Evidence Pipeline — Ingestion → Cleansing → Validated State

Six capability areas

Data Discovery & Cataloging

Metadata tagging and data lineage tracking so every dataset's origin and transformation history stays visible to a reviewer.

  • Metadata Tagging
  • Data Lineage Tracking
  • PHI/ePHI Boundary Mapping

Regulatory Governance & Compliance

21 CFR Part 11, HIPAA Safe Harbor / Expert Determination, and GDPR health-data controls are applied to the pipeline itself, with an audit trail for PHI, not bolted on after.

  • 21 CFR Part 11 Controls
  • HIPAA Safe Harbor / Expert Determination
  • GDPR Health Data Controls

Automated & Expert-Led Cleansing

Anomaly detection and missing-value imputation protocols reviewed against ALCOA+ data-integrity principles, not applied as a black box.

  • Anomaly Detection
  • Missing-Value Imputation Protocols
  • ALCOA+ Data-Integrity Validation

Clinical System Interoperability

HL7 FHIR bulk data export, DICOM image metadata extraction, and OMOP common data model mapping against a client's own source systems.

  • HL7 FHIR Bulk Data Export
  • DICOM Image Metadata Extraction
  • OMOP Common Data Model Mapping

Biostatistical & Model Validation

Dataset splitting protocols, algorithmic bias testing, and performance metric reproducibility, documented for a statistical reviewer.

  • Dataset Splitting Protocols
  • Algorithmic Bias Testing
  • Performance Metric Reproducibility

Submission Evidence Dossiers

Clinical evaluation report datasets, statistical analysis plans (SAP), and audit-traced evidence packages assembled for the submission itself.

  • Clinical Evaluation Report Datasets
  • Statistical Analysis Plans (SAP)
  • Audit-Traced Evidence Packages
SOC 2 TYPE 1 & 2 CERTIFIED 21 CFR PART 11 HIPAA PRIVACY RULE

Ready to Build?

Discuss a clinical data pipeline or submission dataset for a specific regulated program.

Engineering reference only. Not formal regulatory counsel. Specific de-identification method (Safe Harbor vs. Expert Determination) and data model mappings are scoped per engagement against the client's own data governance program.

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