Clinical evidence & AI data services for regulated environments
Consequential Data Engineering
Data cataloging, governance, data cleansing with an audit trail aligned to 21 CFR Part 11, and clinical evidence datasets and documentation that support a sponsor's 510(k), De Novo or PMA submission, 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, data cleansing with an audit trail aligned to 21 CFR Part 11, and evidence datasets that support a sponsor's 510(k), De Novo or PMA submission. They serve organizations where data provenance under HIPAA and ALCOA+ matters as much as model performance.
Data cataloging, governance, data cleansing with Part 11-aligned audit trails, and clinical evidence preparation 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
Technical Architecture: The Clinical Evidence Pipeline
What Netspective Offers: Engineering Responsibility
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
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.
Verified Proof Ledger (Engineering Ground Truth)
| Claim | Status | What it rests on |
|---|---|---|
| HL7 v2, FHIR R4 and C-CDA validation gate with a triage queue | Built and tested | Built and tested in the Healthcare Data Lake pipeline (specs/049 proof ledger). |
| Raw copies kept in your own cloud account under your KMS or Key Vault keys | Built and tested | Built and tested in the Healthcare Data Lake pipeline (specs/049 proof ledger). |
| FHIR, DICOM and OMOP mappings with recorded lineage for a study dataset | Designed | Engagement method; no public client dataset to show. |
| Evidence packages that trace each dataset to its sources (ALCOA+) | Designed | Engagement method; delivered per study or submission. |
| A clearance, approval or study conclusion | Not established | FDA and the sponsor decide; we supply datasets and documentation. |
| 21 CFR Part 11 compliance of the pipeline itself | Not established | Part 11 controls belong to the validated process the pipeline runs in. |
Scoped Pilot: The 2-week provenance and dataset readiness review
What the two weeks deliver
Week 1
Map the PHI boundary and the sources behind one study or submission question: EHR exports, FHIR APIs, DICOM imaging or an OMOP database.
Week 2
Trace one dataset from source to analysis table, then deliver a gap list against ALCOA+ expectations.
Boundaries
- Engineering and documentation support, not regulatory or legal counsel. The sponsor owns the submission and its conclusions.
- PHI stays in your environment. Data access is agreed in writing before any work starts.
- No outcome is promised: FDA, IRBs and reviewers decide.