Clinical Evidence and RWE Pipelines

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.

HIPAA Security RuleALCOA+FHIR · DICOM · OMOP510(k) / De Novo / PMA Support

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.

The Customer Problem: Four Clinical Evidence Failure Patterns

Lineage lost between extract and analysis table

A study dataset is assembled by scripts on an analyst's machine. When a reviewer asks where a value came from, nobody can walk it back to the EHR export it started in.

A de-identified dataset that quietly stops being one

A Safe Harbor extract is joined back to a table that still holds full dates of service or five-digit ZIP codes, and the result no longer meets the standard it was released under.

Mappings checked on a sample, wrong on real data

An OMOP or FHIR mapping passes against a curated sample. Local codes the sample never contained land in an unmapped bucket that nobody counts.

Cleansing nobody can reproduce

Outlier and imputation rules are applied by hand, so running the pipeline again produces a different analysis table and the result cannot be defended.

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

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

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
SOC 2 TYPE 1 & 2 ATTESTED 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.

Mechanics and Data Contracts

Each boundary in the pipeline has a written contract and a check that shows it holds. The checks run in your environment; the queries are in the explorer below.

Each interface, its contract and the check that shows it holds
Interface Contract Checked by
Source extract to raw landingEach extract kept unchanged, with its SHA-256 checksum and the date it was receivedThe checksum manifest (Panel 1)
Raw data to the standard model (OMOP CDM or FHIR R4)Every source code is mapped to a standard concept or listed as unmapped with its record countThe unmapped-code query (Panel 2)
PHI boundaryIdentifiers listed per column, and the de-identification method recorded per dataset: Safe Harbor (45 CFR §164.514(b)(2)) or Expert Determination (§164.514(b)(1))A column inventory reviewed before release
Analysis table to sourceEvery row carries the keys of its source records and the extract they came fromThe lineage query (Panel 3)
Cleansing and imputation rulesRules are versioned; a rule change re-runs the pipeline and the two analysis tables are comparedA recorded re-run comparison

Audit trails for electronic records follow 21 CFR Part 11 §11.10(e) where the sponsor's validated process requires them. Data integrity is judged against ALCOA+: attributable, legible, contemporaneous, original and accurate, plus complete, consistent, enduring and available.

Provenance and Mapping Explorer

Three steps a reviewer can re-run. Each panel says whether it is a public command or a synthetic illustration, and every query is SQLite.

Panel 1: Record a checksum for every raw extract

Before any transform runs, record what arrived. The manifest is what later shows a raw copy is unchanged.

# Record a SHA-256 checksum for every raw extract before any transform runs
sha256sum extracts/*.csv extracts/*.ndjson > extracts.sha256

# Later: show each raw copy is unchanged (prints OK or FAILED per file)
sha256sum --check extracts.sha256

Public command Writes: extracts.sha256, one line per file. sha256sum is part of GNU coreutils; on macOS use shasum -a 256. A checksum shows a file is unchanged; it says nothing about whether the data in it is right.

Panel 2: Source codes with no standard mapping

Every local or source code that no concept map covers, with the number of records it affects, highest first.

-- Synthetic illustration: source codes with no mapping to a standard
-- concept, most frequent first. These are the records a sample-based
-- mapping check never sees.
CREATE TEMP TABLE IF NOT EXISTS source_observation (
  record_id TEXT PRIMARY KEY,
  source_system TEXT NOT NULL,
  source_vocabulary TEXT NOT NULL,
  source_code TEXT NOT NULL
);
CREATE TEMP TABLE IF NOT EXISTS concept_map (
  source_vocabulary TEXT NOT NULL,
  source_code TEXT NOT NULL,
  target_concept_id INTEGER NOT NULL,
  PRIMARY KEY (source_vocabulary, source_code)
);

SELECT o.source_system,
       o.source_vocabulary,
       o.source_code,
       COUNT(*) AS records
FROM source_observation o
LEFT JOIN concept_map m
  ON m.source_vocabulary = o.source_vocabulary
 AND m.source_code = o.source_code
WHERE m.target_concept_id IS NULL
GROUP BY o.source_system, o.source_vocabulary, o.source_code
ORDER BY records DESC, o.source_code;

Synthetic illustration TEMP stand-ins with an illustrative layout, not a product schema; nothing is written. Run it on the full source data, not the sample the mapping was built from. Runs as written in SQLite 3.

Panel 3: Dataset rows with no recorded source

Rows in one study dataset that cannot be traced to a source record in an extract the manifest knows about.

-- Synthetic illustration: rows in a study dataset that cannot be traced to
-- a raw extract recorded in the checksum manifest.
CREATE TEMP TABLE IF NOT EXISTS raw_extract (
  extract_sha256 TEXT PRIMARY KEY,
  file_name TEXT NOT NULL,
  received_utc TEXT NOT NULL
);
CREATE TEMP TABLE IF NOT EXISTS analysis_row (
  row_id INTEGER PRIMARY KEY,
  dataset_id TEXT NOT NULL
);
CREATE TEMP TABLE IF NOT EXISTS row_source (
  row_id INTEGER NOT NULL REFERENCES analysis_row(row_id),
  source_record_id TEXT NOT NULL,
  extract_sha256 TEXT NOT NULL,
  PRIMARY KEY (row_id, source_record_id)
);

SELECT a.dataset_id,
       a.row_id
FROM analysis_row a
WHERE a.dataset_id = 'study-001'
  AND NOT EXISTS (
    SELECT 1
    FROM row_source s
    JOIN raw_extract e ON e.extract_sha256 = s.extract_sha256
    WHERE s.row_id = a.row_id
  )
ORDER BY a.row_id;

Synthetic illustration TEMP stand-ins; nothing is written. An empty result means every row traces to a recorded extract; a reviewer still samples rows against the source. Runs as written in SQLite 3.

Verified Proof Ledger (Engineering Ground Truth)

Proof ledger: each claim, its status and what it rests on
Claim Status What it rests on
HL7 v2, FHIR R4 and C-CDA validation gate with a triage queueBuilt and testedBuilt 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 keysBuilt and testedBuilt and tested in the Healthcare Data Lake pipeline (specs/049 proof ledger).
FHIR, DICOM and OMOP mappings with recorded lineage for a study datasetDesigned and documentedEngagement method; no public client dataset to show.
Evidence packages that trace each dataset to its sources (ALCOA+)Designed and documentedEngagement method; delivered per study or submission.
A clearance, approval or study conclusionNot establishedFDA and the sponsor decide; we supply datasets and documentation.
21 CFR Part 11 compliance of the pipeline itselfNot establishedPart 11 controls belong to the validated process the pipeline runs in.

Typical Engagements

These describe the shape of an engagement in each setting. They are not named client results.

Device sponsor: real-world evidence for a 510(k)

Registry and EHR data for a predicate comparison, mapped to OMOP with every unmapped code counted and every analysis row traced to its extract before the statistician runs the analysis plan.

Academic medical center: OMOP conversion for a research network

Local codes from several source systems mapped to standard concepts, with the unmapped list reviewed at each release instead of discovered by a network query.

Validation dataset for an AI-enabled device

Training, tuning and test sets split by patient rather than by record, so the same person never appears on both sides, with the split recorded and reproducible.

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.
Data sources

Please don't send data, records or credentials in the first message; we agree how they move during scoping.

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