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

Patient falls are a major concern in healthcare due to their impact on patient safety, prolonged hospital stays, and increased costs. Traditional fall prevention methods often lack precision and adaptability, emphasizing the need for predictive approaches. This study reviews the current literature and explores the integration of human-centered artificial intelligence (AI)-based decision support systems to improve fall prevention through proactive risk assessment and prediction. This system enables early identification of fall risks, facilitating personalized interventions and real-time monitoring via advanced sensors and wearable devices. These technologies may provide timely alerts to caregivers and support administrators in optimizing resource allocation. Additionally, this study highlights the importance of systems thinking, recognizing patient falls as outcomes of interconnected system failures. By leveraging causal loop analysis and feedback mechanisms, healthcare stakeholders can develop dynamic, system-wide strategies to enhance fall prevention and operational efficiency.