The Data Trust Index (DTI) is a multidimensional scoring framework that assigns a continuous integrity score ranging from 0 to 100 to health data records before they are used in downstream AI inference. DTI evaluates eight weighted dimensions: Provenance (25%), Consent (20%), Recency (15%), Quality (10%), Concordance (10%), Validation (10%), Breadth (5%), and Stability (5%). This paper describes the theoretical basis for each dimension, the time-decay functions applied to temporal signals, and the concordance methodology used to corroborate signals across independent sources. It presents a retrospective deployment case study with imaware, a direct-to-consumer diagnostics company, in which DTI-based segmentation reduced data preparation cycles from three weeks to two hours and surfaced a customer segment accounting for 20% of revenue that was previously invisible in fragmented data views. The paper situates DTI within the regulatory landscape of the 21st Century Cures Act, the FDA's AI/ML-Based Software as a Medical Device (SaMD) guidance, and TEFCA, arguing that a standardized trust metric for health data is a prerequisite for safely deploying clinical AI at scale. A prospective multi-site validation protocol is published as a pre-registered methodological framework for future study. The DTI framework and its underlying ALDR engine are covered by U.S. patent application SuperTruth0010CP1.