Glossary
What is data trust?
Updated October 10, 2026
Data trust is the decision to act on a record, made on evidence instead of assumption. The evidence is five documented answers: where the record came from, whether consent covers this use, how current it is, whether independent sources agree, and whether outcomes validate it. SuperTruth measures it one record at a time with the Data Trust Index (DTI), a patented score of 0 to 100 across eight weighted dimensions, with published weights calibrated in health.
The DTI methodology is published and citable: "The Data Trust Index: A Multidimensional Framework for Evaluating Health Data Integrity in AI Systems," SuperTruth Inc., 16 April 2026, DOI 10.5281/zenodo.19601616.
Trust is a decision, not a property of the warehouse. Cloud platforms secure the perimeter and call that trust; the data inside is exactly as reliable as it was when it arrived. Data trust is scored on the record itself, before it reaches any warehouse or model, and the score travels with the record wherever it goes.
Most organizations measure quality because quality is measurable inside the record itself. Trust needs the context the record does not carry on its own: chain of custody, consent scope, corroboration, and clinical linkage. A record can be perfectly clean and completely untrustworthy.
The five questions
Every trust decision reduces to five questions, and a record earns trust when each has a documented answer. Where did it come from? The source that produced the value and every hand it passed through. Was consent obtained for this use? Not for some use once, but for this use now. How current is it? Current for its kind: a blood count ages differently than a home address. Does it agree with other sources? A lab result that matches the wearable, a chart that matches the claim. Has it been validated against outcomes? The check that closes the loop between the record and the result it predicts. These are the data truth questions with validation added, and a data quality check never reaches them.
Data trust versus data quality
Data quality asks what a record contains: is it complete, correctly formatted, does it pass validation rules. Data trust asks whether you should act on it. The two are not rivals; a record needs both. But quality is measured inside the record, and trust needs the context around it, which is why claims data can pass every quality check and still mislead a model. The short version is Data trust vs. data quality, and the worked argument is Data quality vs data trust: what is the difference and why it matters for healthcare AI (April 2026).
Where data trust breaks
It breaks where the evidence is thinnest. Health plans feel it in Star ratings: a single missed mammogram record or a misaligned value set can drop a plan's rating by half a star, costing millions in Quality Bonus Payments. That is why HEDIS measure data quality: what NCQA requires for star rating accuracy (September 2026) starts from the reporting, not the score. Providers feel it in the chart, where billing codes carry reimbursement incentives into clinical predictions: Why EHR data needs a trust score before any AI model trains on it (April 2026).
How data trust is measured
SuperTruth measures it with the Data Trust Index, a patented score of 0 to 100 on any record across eight weighted dimensions: Provenance 25 percent, Consent 20 percent, Recency 15 percent, Quality 10 percent, Concordance 10 percent, Validation 10 percent, Breadth 5 percent and Stability 5 percent (default weights, DTI white paper, DOI 10.5281/zenodo.19601616, 2026). Scores map to four tiers, Bronze, Silver, Gold and Platinum, plus a fail band below 55 that is held for remediation. DTI scores the record, not the patient. The full article is Data Trust Index; the engine that runs it is the DTI Engine.
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