Frontiers in public health · 2026
Identifying contributing-factor configurations in reported bed falls: a Bayesian network-based exploratory study.
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Wang Mingxuan, Zhang Yanhui, Li Jiuqun, Li Peitao, Huo Tianyi, Jiang Yuting, Qu Quanying, Li Baohua
Аннотация
OBJECTIVE: This study aimed to explore the conditional dependency structure of contributing factors related to bed falls and to identify stable dependency configurations within reported bed falls incident reports using Bayesian network analysis.
METHODS: A retrospective analysis was conducted using 102 inpatient bed-fall incident reports collected from a tertiary hospital between 2014 and 2024. Twenty-two previously identified contributing factors were encoded as binary variables. Bayesian network structure learning was performed using a hill-climbing algorithm with the Bayesian Information Criterion, without imposing prior structural constraints. Structural stability was assessed through non-parametric bootstrap resampling. Conditional inference and posterior probability analyses were used to examine probability changes and co-occurrence patterns under specific clinical conditions.
RESULTS: Bootstrap analysis identified several structurally stable directed edges in the Bayesian network. Sudden changes in patient consciousness were repeatedly connected to medication-related hypotension and safety measure-related nodes, forming a central structural component of the network. A stable directed relationship was also observed between nighttime sedative use with toileting and unassisted bed exit. In addition, delayed patient assistance was more frequently linked to delayed responses by on-site caregivers than to complete caregiver absence. Together, these results indicate that reported bed falls are associated with a limited number of recurrent and structurally consistent contributing-factor configurations.
CONCLUSION: Reported bed falls appear to arise from interactions among a small number of contributing factors under specific clinical contexts rather than from isolated causes. Bayesian network analysis offers a useful approach for identifying contributing-factor configurations and informing scenario-based fall prevention strategies in clinical nursing practice.
METHODS: A retrospective analysis was conducted using 102 inpatient bed-fall incident reports collected from a tertiary hospital between 2014 and 2024. Twenty-two previously identified contributing factors were encoded as binary variables. Bayesian network structure learning was performed using a hill-climbing algorithm with the Bayesian Information Criterion, without imposing prior structural constraints. Structural stability was assessed through non-parametric bootstrap resampling. Conditional inference and posterior probability analyses were used to examine probability changes and co-occurrence patterns under specific clinical conditions.
RESULTS: Bootstrap analysis identified several structurally stable directed edges in the Bayesian network. Sudden changes in patient consciousness were repeatedly connected to medication-related hypotension and safety measure-related nodes, forming a central structural component of the network. A stable directed relationship was also observed between nighttime sedative use with toileting and unassisted bed exit. In addition, delayed patient assistance was more frequently linked to delayed responses by on-site caregivers than to complete caregiver absence. Together, these results indicate that reported bed falls are associated with a limited number of recurrent and structurally consistent contributing-factor configurations.
CONCLUSION: Reported bed falls appear to arise from interactions among a small number of contributing factors under specific clinical contexts rather than from isolated causes. Bayesian network analysis offers a useful approach for identifying contributing-factor configurations and informing scenario-based fall prevention strategies in clinical nursing practice.