What makes health data Platinum-grade for FDA regulatory submission
The FDA rejects submissions with incomplete provenance, inconsistent formatting, or unverifiable consent chains. Platinum-grade health data meets every dimension of trust before it reaches a reviewer's desk. SuperTruth's Data Trust Index scores each record across eight dimensions so sponsors know exactly where their data stands.
The FDA does not grade on a curve
A single flawed dataset can delay a drug approval by 6 to 12 months. The FDA's Center for Drug Evaluation and Research (CDER) rejected or issued refuse-to-file letters on roughly 15% of NDA submissions in recent cycles, often citing data integrity and standardization failures. When the stakes are a $2.6 billion average drug development cost, "good enough" data is not good enough.
Platinum-grade health data is the standard that eliminates those risks before submission day.
What is regulatory-grade data?
Regulatory-grade data refers to health information that meets the accuracy, completeness, traceability, and compliance thresholds required by governing bodies like the FDA, EMA, or PMDA. It goes beyond clinical correctness. Every record must carry verifiable provenance, documented consent, and standardized formatting that a regulator can independently audit and reproduce.
Most ranked definitions stop there. But regulatory grade is a binary: pass or fail. Platinum grade adds a continuous score. SuperTruth's Data Trust Index (DTI) rates every health data record from 0 to 100 across eight dimensions, giving sponsors a precise, quantified view of submission readiness rather than a checkbox.
The eight dimensions the FDA actually cares about
The DTI maps directly to FDA expectations. Provenance, weighted at 25%, captures where data originated and every transformation it underwent. Consent, at 20%, verifies that patient authorization is documented, granular, and current. Recency (15%) flags stale records that no longer reflect patient status. Quality (10%) measures completeness, accuracy, and format compliance. Concordance (10%) checks agreement across sources. Validation (10%) confirms third-party or algorithmic verification. Breadth (5%) and Stability (5%) round out the score by evaluating coverage and consistency over time.
No single dimension is sufficient alone. A record with perfect provenance but expired consent is not submittable. A record that is recent but fails concordance across two lab systems is a liability. The DTI forces sponsors to see every gap.
Does the FDA require CDISC?
Yes, functionally. Since December 2016, the FDA has required study data in CDISC standards (SDTM for clinical, SEND for nonclinical, ADaM for analysis) for all NDA, ANDA, and BLA submissions. The FDA Data Standards Catalog specifies which versions are currently supported. Submissions that arrive in non-CDISC formats face delays, information requests, or outright rejection.
CDISC compliance is a formatting requirement. It does not verify that the underlying data is trustworthy. A perfectly formatted SDTM dataset built from records with broken provenance chains will still fail FDA scrutiny during review. Format and trust are separate problems.
What are two criteria the FDA tests for during clinical trials?
The FDA evaluates two primary criteria: safety and efficacy. Safety analysis examines adverse events, dose-limiting toxicities, and risk-benefit profiles across trial phases. Efficacy analysis measures whether the intervention produces a statistically significant clinical outcome against its endpoints.
Both analyses depend entirely on the underlying data quality. If 8% of records in a pivotal Phase III trial contain unresolvable discrepancies, the statistical power of the entire study is compromised. Platinum-grade data protects the integrity of both safety and efficacy conclusions.
What are the different types of RWE studies?
Real-world evidence studies fall into several categories: retrospective observational studies using claims or EHR data, prospective registries, pragmatic clinical trials conducted in routine care settings, and hybrid designs that supplement RCT data with real-world controls. The FDA's 2018 framework for RWE explicitly states that the reliability and relevance of real-world data (RWD) determines whether it can support regulatory decisions.
This is where health data provenance becomes non-negotiable for FDA purposes. RWD sourced from fragmented EHR systems, patient-reported apps, or aggregated claims databases must prove its lineage. Without provenance, RWE studies cannot meet the FDA's standard for "fit for purpose" data.
Key statistics
Why provenance is the make-or-break dimension
The FDA's own guidance on electronic source data states that sponsors must be able to trace any data point back to its origin. This includes the original capture system, any transformations, who authorized each step, and timestamps at every handoff. Manual provenance tracking across multi-site trials with thousands of patients is where most sponsors fail.
SuperTruth's DTI Engine automates provenance verification at the record level. When imaware needed to standardize 105,000 diagnostic records for analysis and downstream use, the DTI Engine compressed a three-week manual process into two hours. CEO Brodie Flanders said it plainly: "The lab industry has never had a trust standard. DTI created one."
From regulatory grade to Platinum grade
Regulatory grade means you can submit. Platinum grade means you can submit with confidence that no reviewer will find a provenance gap, a consent lapse, or a concordance failure buried in record 47,319. The difference is not philosophical. It is measurable, and the DTI measures it.
Sponsors preparing FDA submissions, CROs managing multi-site data, and real-world evidence teams building RWD packages all face the same question: can you prove every record is trustworthy before the FDA asks?
Further reading: See our pharma and life sciences solution To find out where your data stands, contact Louis Simeonidis, SVP Commercial Operations, at louis@supertruth.ai or (215) 918-4140.
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.
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See it in practice
DTI scores the record, not the patient.
8 dimensions. 0 to 100. Travels with every record permanently.