Intelligent data governance and quality control for chest Bi syndrome/coronary heart disease across the prevention-treatment-rehabilitation continuum: integrating a standardized framework, adversarially-optimized trigger engine, and domain-adaptive AI.

BACKGROUND: Digital transformation in Traditional Chinese Medicine (TCM) is hindered by data silos, poor standardization, and inefficient quality control. This study developed and validated an intelligent data governance system to address these gaps. METHODS: Using Chest Bi syndrome (coronary heart disease) as a model, we established a standardized data system (9 subsets, 352 elements; 42.6% TCM-specific) based on the "Triple-Loop Coupling" theory and HL7 FHIR (Fast Healthcare Interoperability Resources)/SNOMED CT standards. The system integrates a three-tier trigger engine (128 rules) for real-time alerts and a domain-adapted LLM, fine-tuned on TCM texts and guidelines, for unstructured data processing. Efficacy was evaluated via a prospective controlled study (n = 972). CONCLUSION: The intervention group showed significant improvements (P < 0.001): data completeness rose from 82.51% to 99.59% (Δ=17.08%), and TCM-specific data elements collection improved from 67.08% to 98.97% (Δ=31.89%). Logical consistency errors dropped from 15.84% to 1.65% (Δ=-14.20%). Unstructured data conversion increased from 45.06% to 91.77% (Δ=46.71%). Quality control time per record decreased by 58.95% (from 12.48 to 5.12 min). The AI model achieved a 91.4% F1-score and 91.8% alert accuracy. CONCLUSION: The "standardization-trigger-AI" model significantly enhances data quality and efficiency for TCM. This system provides a preliminary and verifiable exploratory path for the intelligent governance of TCM clinical data, offering a scalable solution for TCM modernization.