Automated FHIR Resource Data Quality Assurance via Hyperdimensional Pattern Recognition and Recursive Validation

**Abstract:** This paper introduces a novel approach to automating FHIR (Fast Healthcare Interoperability Resources) data quality assurance (DQA). Leveraging hyperdimensional computing (HDC) and a recursive validation methodology, our system, termed "HyperQuality," detects anomalies and inconsistencies within FHIR resources with significantly enhanced speed and accuracy compared to traditional rule-based systems. HyperQuality navigates inherent data complexity through high-dimensional pattern recognition, facilitating real-time DQA critical for ensuring interoperability and data integrity in healthcare. The system's recursive structure allows continuous learning and adaptation to evolving FHIR standards and data patterns, ensuring long-term reliability and scalability.