A Neo4j-Based Framework for Integrating Clinical Data with Medical Ontologies: Performance Optimization and Quality Measure Applications in Healthcare

Electronic Health Records face a fundamental challenge: the semantic gap between relational data storage and clinical reasoning patterns. Traditional databases struggle with complex healthcare queries requiring multiple joins and temporal analysis, creating performance bottlenecks that limit real-time clinical applications.

Methods

We developed a Neo4j-based framework integrating MIMIC-IV clinical data (1,504 patients, 4,967 admissions) with SNOMED CT medical ontology through ICD-10-CM mappings. The implementation created a unified graph comprising 625,708 nodes and 2,189,093 relationships, with systematic preservation of temporal and semantic connections.

Results

Performance analysis demonstrated substantial improvements over PostgreSQL across five query types, with Neo4j showing 5.4x to 48.4x faster execution times. The framework successfully enabled three clinical applications: ventilator-associated pneumonia temporal analysis (revealing 47.79% pneumonia rates among ventilated ICU stays), hypertension semantic network mapping through multi-level SNOMED-CT relationships, and Medicare Part D quality measure monitoring. Notably, the system identified that 96.7% of eligible diabetic patients lacked statin prescriptions, demonstrating practical utility for healthcare quality improvement initiatives.

Conclusion

This graph-based approach provides a robust foundation for next-generation clinical decision support systems by bridging the gap between fragmented clinical data and integrated patient-centric analysis. The framework’s demonstrated performance advantages and practical applications in quality measure monitoring establish its potential for addressing real-world healthcare challenges while supporting the transition toward more effective, evidence-based patient care.