跨域整合智慧驅動：打造國際首創全模組重症護理AI系統新標竿

Critically ill patients

Led by nursing professionals, our hospital integrates nursing practice, critical care medicine, information engineering, and artificial intelligence technologies to develop a Comprehensive Modular AI System for Critical Care Nursing.
The system addresses three high-risk clinical care scenarios—agitation (RASS), physical restraint risk, and acute delirium—and incorporates IoT-based real-time urine output monitoring and alerting, as well as intravenous medication Y-site compatibility assessment, to support the establishment of a comprehensive critical care and patient safety management framework.
The system automatically retrieves patients’ clinical data from the preceding eight hours and performs continuous 24-hour real-time inference and risk stratification. Through real-time alerts for abnormal urine output and medication compatibility assessments, the system provides timely clinical decision-support information, enabling nursing staff to intervene at an early stage. All modules are delivered through visualized interfaces to support clinical nursing practice.
The system has been implemented in five adult intensive care units, generating an average of over 6,200 inferences per day with a 100% system operation rate. Following implementation, the physical restraint rate decreased from 5.62% to 4.89%, the incidence of delirium declined from 27.63% to 23.85%, and nursing task time was reduced from an average of 8 minutes to 2 minutes and 34 seconds, collectively contributing to enhanced patient safety and improved care efficiency.

First-of-its-kind integration of multi-dimensional clinical data and AI technologies to perform automated RASS classification and acute delirium risk prediction, combined with a clinical decision support platform to assist in sedation medication adjustment, establish preventive mechanisms, and enhance patient safety.
Integration of AIoT-based real-time urine output monitoring and analysis, providing immediate alerts for high-risk conditions such as oliguria and polyuria.
Visualization interfaces present risk assessment results, care recommendations, and intravenous medication compatibility evaluations, supporting routine clinical nursing workflows.

Critically ill patients

Led by nursing professionals, our hospital integrates nursing practice, critical care medicine, information engineering, and artificial intelligence technologies to develop a Comprehensive Modular AI System for Critical Care Nursing.
The system addresses three high-risk clinical care scenarios—agitation (RASS), physical restraint risk, and acute delirium—and incorporates IoT-based real-time urine output monitoring and alerting, as well as intravenous medication Y-site compatibility assessment, to support the establishment of a comprehensive critical care and patient safety management framework.
The system automatically retrieves patients’ clinical data from the preceding eight hours and performs continuous 24-hour real-time inference and risk stratification. Through real-time alerts for abnormal urine output and medication compatibility assessments, the system provides timely clinical decision-support information, enabling nursing staff to intervene at an early stage. All modules are delivered through visualized interfaces to support clinical nursing practice.
The system has been implemented in five adult intensive care units, generating an average of over 6,200 inferences per day with a 100% system operation rate. Following implementation, the physical restraint rate decreased from 5.62% to 4.89%, the incidence of delirium declined from 27.63% to 23.85%, and nursing task time was reduced from an average of 8 minutes to 2 minutes and 34 seconds, collectively contributing to enhanced patient safety and improved care efficiency.

First-of-its-kind integration of multi-dimensional clinical data and AI technologies to perform automated RASS classification and acute delirium risk prediction, combined with a clinical decision support platform to assist in sedation medication adjustment, establish preventive mechanisms, and enhance patient safety.
Integration of AIoT-based real-time urine output monitoring and analysis, providing immediate alerts for high-risk conditions such as oliguria and polyuria.
Visualization interfaces present risk assessment results, care recommendations, and intravenous medication compatibility evaluations, supporting routine clinical nursing workflows.