Streamlining Patient Fall Prevention and Management Through Human-Centered AI-Based Decision Support Systems.

Area of Science:

• Healthcare Informatics
• Patient Safety
• Artificial Intelligence in Medicine

Background:

• Patient falls pose significant risks to safety, prolong hospital stays, and increase healthcare costs.
• Current fall prevention strategies often lack precision and adaptability, necessitating advanced predictive methods.
• A systems-thinking approach is crucial, viewing falls as complex outcomes within interconnected healthcare systems.

Purpose of the Study:

• To review literature on integrating human-centered artificial intelligence (AI) decision support systems for fall prevention.
• To explore AI's role in proactive risk assessment, prediction, and personalized intervention strategies.
• To highlight the application of systems thinking and causal loop analysis for dynamic fall prevention strategies.

Main Methods:

• Literature review of current research on AI and patient fall prevention.
• Exploration of human-centered AI decision support systems for risk assessment and prediction.
• Application of systems thinking principles, including causal loop analysis, for strategy development.

Main Results:

• AI-based systems can enable early identification of fall risks through real-time monitoring via sensors and wearables.
• Personalized interventions and timely alerts to caregivers can be facilitated by AI integration.
• Systems thinking provides a framework for developing dynamic, interconnected strategies to enhance fall prevention and operational efficiency.

Conclusions:

• Human-centered AI decision support systems offer a promising approach to proactive patient fall prevention.
• Integrating AI with advanced monitoring technologies can significantly improve patient safety and resource allocation.
• Adopting a systems-thinking perspective is essential for developing comprehensive and effective fall prevention strategies.