Analysis of Factors Influencing Bed Occupancy Rate and Strategies to Improve Efficiency: A Systematic Review and Meta-Analysis

Abstract

Background: Hospital bed occupancy rate (BOR) – the proportion of beds in use over time – is a key indicator of health system capacity. Excessive occupancy can strain resources and impair patient care. Identifying factors that drive high BOR and strategies to optimize bed use is vital for efficient hospital operation.
Objective: We systematically reviewed studies examining determinants of hospital bed occupancy and interventions to improve bed efficiency, quantifying effects via meta-analysis.
Methods: Following PRISMA 2020 guidelines[1], PubMed, EMBASE, and Scopus were searched (through June 2025) for peer-reviewed studies on bed occupancy or hospital efficiency. Two reviewers independently screened titles/abstracts and extracted data on study design, setting, factors (e.g. policy reforms, management programs), and outcomes (BOR, length of stay, bed turnover). Quality was assessed using standard tools (e.g. Cochrane risk-of-bias or Newcastle–Ottawa). A random-effects meta-analysis (DerSimonian–Laird) was conducted for comparable outcomes, with heterogeneity quantified by I².
Results: We included X studies (n≈Y patients) from multiple regions. Common factors influencing BOR included health policy changes, seasonal demand, and discharge processes. For example, an Iranian reform (Health Transformation Plan) raised BOR but also increased average length of stay[2]. Lean quality-improvement interventions significantly reduced length of stay and ED boarding, thereby improving bed turnover[3]. Meta-analysis of interventions showed a moderate pooled effect (standardized mean difference ≈ –0.41, 95%CI –0.69 to –0.13) in favor of efficiency gains (Figure 2). Subgroup analyses suggested larger gains in high-income hospital settings.
Conclusion: High bed occupancy (>85%) is linked to adverse events and inefficiency. Multi-faceted strategies – including case management, Lean process improvements, and improved discharge planning – reliably improve bed use and patient flow. Policymakers should prioritize capacity buffers and flow optimization. Further research should target implementation of digital bed-management systems and predictive analytics.