Precision Anesthesia: A Concept Analysis

Precision anesthesia is defined as an informatics-driven perioperative framework integrating pharmacogenomic phenotyping with multimodal clinical data and predictive modeling to deliver real-time, individualized anesthetic decision support that reduces patient-specific risk and improves outcomes. Four defining attributes were identified: individualized phenotyping, predictive stratification, dynamic data-driven medication administration and titration, and clinical actionability. Five antecedents form a dependency chain: data availability and accessibility, technical infrastructure and interoperability, multimodal clinical data integration, multidisciplinary domain expertise, and governance and ethical frameworks. Beneficial consequences include enhanced predictive accuracy, genotype-informed medication optimization, and strengthened provider decision-making. Adverse consequences include alert fatigue, algorithmic bias, and genomic privacy concerns. Empirical referents include pharmacogenomic testing rates, prediction model performance metrics, data integration latency, and clinical decision support response rates.