ReviewRisk management and healthcare policy2025
Streamlining Patient Fall Prevention and Management Through Human-Centered AI-Based Decision Support Systems.
Review in Risk management and healthcare policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.