IntegrityNet onboarding: what a zero-copy health data intake actually looks like
Most health data onboarding processes copy records into a new environment, creating compliance risk and provenance gaps before analysis even begins. IntegrityNet eliminates data movement entirely, scoring and cataloging EHR data in place through a zero-copy architecture that preserves chain of custody from the first connection. Here is what that process actually looks like, step by step.
Health data onboarding is where most trust failures originate. The record leaves its source system, lands in a staging environment, gets transformed, and arrives at its destination stripped of the metadata that proves where it came from. IntegrityNet was built to prevent that first failure from ever happening.
What is health onboarding data?
Health onboarding data refers to the initial intake of clinical, administrative, and behavioral records when an institution connects its data to a new platform, model, or analytic pipeline. This includes EHR extracts, claims feeds, lab results, imaging metadata, and SDOH indicators.
The problem is not the data itself. The problem is what happens to it during transit. Traditional onboarding copies records from source to destination, often through multiple intermediate systems. Each copy introduces a provenance gap: a point where the chain of custody breaks and no one can prove the record was not altered, duplicated, or stripped of consent metadata.
IntegrityNet's zero-copy EHR data intake eliminates transit entirely. The data stays where it is. IntegrityNet connects to it, scores it, and catalogs it without ever moving a byte.
The five stages of zero-copy onboarding
People frequently ask: what are the 5 stages of the onboarding process? For IntegrityNet, each stage maps to a specific trust function.
Stage 1: Secure connector provisioning. IntegrityNet deploys a read-only connector to the source system, whether that is an Epic FHIR endpoint, a Cerner data warehouse, a flat-file claims feed, or a lab API. No data is extracted. The connector authenticates and establishes a cryptographic session.
Stage 2: Schema discovery and mapping. The connector catalogs available data elements, field types, and structural patterns. It maps source fields to the DTI scoring framework's eight dimensions: Provenance, Consent, Recency, Quality, Concordance, Validation, Breadth, and Stability. This happens in place, against the source schema.
Stage 3: DTI scoring at the source. Every record receives a 0 to 100 trust score without leaving its original environment. Provenance alone accounts for 25% of that score, which is why scoring at the source matters. A record that has been copied three times before scoring has already lost its highest-weighted dimension.
Stage 4: Consent verification through ConsentOS. IntegrityNet triggers ConsentOS to verify that each record carries valid, tier-appropriate consent for the intended use. Consent accounts for 20% of the DTI score. Records without verified consent are flagged, not discarded, preserving the full catalog while enforcing governance.
Stage 5: Catalog publication to the Data Reservoir. Scored metadata, not the records themselves, flows into the Data Reservoir. Downstream consumers query the catalog to find records that meet their DTI floor requirements. When a query matches, the zero-copy connector serves the record directly from the source, with full provenance intact.
What are the 4 stages of onboarding?
Some frameworks compress onboarding into four stages: connect, assess, govern, and activate. IntegrityNet maps cleanly to this model. Connection is stage 1. Assessment covers stages 2 and 3 (schema discovery plus DTI scoring). Governance is stage 4 (consent verification). Activation is stage 5 (catalog publication and query readiness). The difference is that IntegrityNet executes all four without a single data copy.
What are the steps of onboarding?
The steps of onboarding vary by platform, but every legitimate health data pipeline should include authentication, schema mapping, quality scoring, consent verification, and catalog registration. If any of those steps requires copying the data into a new environment, the pipeline introduces risk that did not exist before onboarding began.
Why zero-copy matters for the health data pipeline trust chain
Every data copy is a trust liability. A copied record cannot prove it was not modified in transit. A copied record may carry stale consent metadata. A copied record creates a second attack surface for breach exposure.
The imaware integration demonstrated this concretely. SuperTruth onboarded 105,000 diagnostic records and reduced processing time from 3 weeks to 2 hours, a 95% time reduction. The pipeline saved over 200 hours per month. None of that required moving the underlying lab data into a SuperTruth-owned environment.
Zero-copy architecture also satisfies emerging regulatory expectations. The ONC's information blocking rules require data access without mandating data duplication. TEFCA's interoperability framework assumes query-based exchange, not bulk transfer. IntegrityNet aligns with both.
Key statistics
What happens after onboarding
Once IntegrityNet publishes a scored catalog to the Data Reservoir, downstream systems can enforce DTI floor requirements automatically. A clinical AI model that requires Platinum-grade data (DTI 90+) will only receive records that meet that threshold. A research consortium can query across multiple IntegrityNet-connected institutions without any institution exposing raw records.
This is the foundation that makes trust-scored data a practical standard rather than a theoretical framework.
The DTI Engine scores every health data record 0 to 100 across 8 trust dimensions before your AI model sees it. If your team is evaluating data for training, compliance, or clinical use, and you want onboarding that never copies a record, contact Louis Simeonidis at louis@supertruth.ai or (215) 918-4140.
Further reading:
Jason Alan Snyder
Co-founder of SuperTruth and Artists & Robots, and an inventor on the Data Trust Index patents. Twenty-plus years building technology inside Interpublic Group. He writes here nearly every day on data trust, provenance, and what AI should be allowed to act on, and publishes essays on his Substack.
About SuperTruth · LinkedIn · Substack · jasonalansnyder.com
See it in practice
DTI scores the record, not the patient.
8 dimensions. 0 to 100. Travels with every record permanently.