Nursing Surveillance from Invisible to Measurable to Indispensable: The CONCERN Early Warning System Trial

Area of Science:

• Nursing informatics
• Clinical decision support systems
• Artificial intelligence in healthcare

Background:

• Excessive documentation requirements detract from nurses' core competencies, particularly patient surveillance.
• Effective nursing surveillance is critical for early detection of patient deterioration and timely intervention.
• Current methods for monitoring patient status may not be sufficient to prevent adverse events.

Purpose of the Study:

• To describe the development and implementation of the CONCERN Early Warning System.
• To evaluate the potential of an AI algorithm to quantify and enhance nursing surveillance.
• To improve interdisciplinary communication regarding patient risk stratification.

Main Methods:

• Development of an artificial intelligence algorithm named CONCERN.
• The algorithm is designed to predict the deterioration of hospital patients.
• The predictive score is displayed to the interdisciplinary care team.

Main Results:

• The CONCERN system provides a predictive score for patient deterioration.
• Implementation of the system aims to support nursing surveillance.
• The AI-driven score facilitates communication among care teams.

Conclusions:

• The CONCERN Early Warning System offers a novel approach to support nursing surveillance.
• AI-powered predictive analytics can potentially mitigate the impact of documentation burdens.
• Enhancing nursing surveillance through technology can improve patient safety and outcomes.