ReviewFrontiers in medicine2026
Enhancing patient care quality and safety under the hospital-wide "One Bed" model: a FMEA-based approach.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
The trial behind it
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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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The hospital-wide "One Bed" model poses potential risks in cross-departmental patient care, affecting nursing quality and patient safety. However, systematic evaluation and improvement strategies for these risks remain limited. Objective: This study aimed to apply Failure Mode and Effects Analysis (FMEA) to identify and improve nursing quality risks under the "One Bed" model, providing actionable strategies for proactive risk management in cross-departmental patient care. Methods: Failure Mode and Effects Analysis was employed to systematically identify potential failure modes in nursing processes and quantify associated risks using Risk Priority Numbers (RPNs). Based on RPN values, priority improvement projects were implemented to optimize nursing quality and patient safety. Results: Following the interventions, RPN values of 15 identified failure modes across pre-, intra-, and post-cross-departmental management all decreased, with reductions ranging from 39.71% to 80.42%, indicating observed improvements in nursing quality and patient safety. Conclusion: Integrating FMEA into hospital-wide nursing management under the "One Bed" model provides a structured approach for proactive risk identification and workflow optimization. This study offers a practical framework for other institutions seeking to enhance cross-departmental patient care.
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