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

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Literature review explores AI-based systems for proactive fall risk assessment, suggesting system-wide strategies.

Implementing AI-driven systems improves early identification of patient falls and enhances safety.
The integration of advanced sensors can facilitate real-time monitoring and timely alerts for caregivers.
Human-centered design in decision support systems allows for tailored interventions based on individual risk assessments.
Using systems thinking enables healthcare providers to view patient falls as results of interconnected failures in care systems.

Rahmadani et al. (2025) studied this question.

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