Semantic Interoperability and Data Normalization as Foundational Infrastructure for Artificial Intelligence Deployment in Healthcare Systems

Semantic interoperability is a key bottleneck for deploying AI in healthcare but remains underexplored in practice. This study examines the impact of data normalization on AI performance across 142 health systems using an interoperability maturity framework based on HL7 FHIR R5, ontology mapping, and semantic consistency. Results show that systems with comprehensive normalization achieve 34.7% higher diagnostic accuracy and 62.4% fewer hallucinations. Each 10% increase in semantic consistency improves AI precision by 8.2% (r=0.78, p<0.001). FHIR R5 adoption reached 67.3%, with LOINC and SNOMED CT coverage at 89.4% and 76.8%, respectively. Despite average implementation costs of $2.3 million, systems realized ROI within 18 months. Overall, semantic interoperability is essential for reliable AI, with data normalization investments significantly improving performance and reducing errors.