AI-SOFA: An EMR-integrated Nursing Informatics-driven Decision Support System for Mortality Risk-informed ICU Clinical Decision-making.
Automated machine learning-based Sequential Organ Failure Assessment scoring predicted intensive care units mortality far better than manual scoring.
AI-SOFA: An EMR-integrated Nursing Informatics-driven Decision Support System for Mortality Risk-informed ICU Clinical Decision-making.
Accurate and timely mortality prediction is essential for nursing clinical decision-making in intensive care units (intensive care units).
This study aimed to enhance intensive care units system-level safety and workflow efficiency by refining and evaluating an automated Electronic Medical Record (Electronic Medical Record)-integrated Sequential Organ Failure Assessment scoring system (AI-Sequential Organ Failure Assessment) to evaluate: (1) its predictive performance for mortality compared to traditional manual scoring; and (2) its clinical utility as a nursing informatics initiative.
A retrospective cohort study was conducted using Electronic Medical Record data from 2559 intensive care units admissions at a tertiary hospital in South Korea.
predicted death in the intensive care units far more accurately than manual scoring
Automated Sequential Organ Failure Assessment scores were generated using 11 routinely collected clinical parameters.
Intensive care units mortality increased markedly with higher Sequential Organ Failure Assessment scores, exceeding 50% at scores ≥13.
In contrast, mortality prediction based on manual Sequential Organ Failure Assessment scoring showed substantially lower accuracy (AUROC=0.64).
The AI-SOFA system serves as a nursing informatics tool that supports nursing workflows by enabling real-time risk stratification, reducing documentation burden, and facilitating timely clinical decision-making in intensive care units settings.