Exploring Nurses' Acceptability and Readiness for Patient-Centered Artificial Intelligence Systems in Pressure Injury...

Area of Science:

• Nursing Informatics
• Artificial Intelligence in Healthcare
• Patient Safety

Background:

• Current nurse-led risk assessment tools for pressure injuries (PI) have limitations, particularly for specific patient populations.
• Improving the accuracy and reliability of PI risk stratification is crucial for enhancing patient safety and care quality.

Purpose of the Study:

• To explore nurses' acceptability and readiness for integrating patient-centered artificial intelligence (AI) technologies for pressure injury prevention.
• To inform the design of clinically applicable AI technologies for PI prevention.

Main Methods:

• Qualitative descriptive study involving 202 international nurses from 2 countries.
• Data collected through focus group discussions and written responses.
• Thematic analysis was performed using MAXQDA software.

Main Results:

• Identified clinical challenges with the Braden Scale, including accuracy, reliability, and limitations in specific patient groups.
• Nurses expressed expectations for AI in advanced risk prediction and real-time data, alongside concerns regarding acceptability, education, data accuracy, and ethical issues.
• Explored benefits of AI-integrated systems, such as automated documentation, early warning systems, and AI-supported decision support for personalized interventions.

Conclusions:

• Existing nurse-led risk assessment systems need enhancement for diverse patient groups to ensure safety and quality of care.
• AI-based systems demonstrate potential for more accurate PI risk prediction and personalized interventions, improving clinical decision-making and outcomes.
• Nurses are prepared for AI adoption, but targeted education is essential for successful integration and optimized patient care.