基于数据包络分析与床位工作效率模型的病区床位资源配置研究—以某三甲医院为例

Ward bed resource allocation using data envelopment analysis and a bed workload efficiency model: A case study of a tertiary hospital

  • 摘要: 背景 病区床位配置直接影响医院服务能力与运营效益。现有研究对病区层面的关注不足,且多侧重单项指标,难以全面反映病区内多投入、多产出之间的效率关系。目的 分析医院病区资源投入产出效率,为病区资源调整提供参考依据。方法 采集北京市某大型综合性三级甲等医院2024 年1 月1 日— 2024 年12 月31 日102 个病区运营数据,根据病区所在科室属性将病区分为非手术病区组和手术病区组。两组投入指标相同,手术病区组产出指标在非手术病区组基础上增加三四级手术人数。采用数据包络分析(Data Envelopment Analysis,DEA)的规模报酬可变模型(Banker Charnes& Cooper,BCC)分析两组病区的投入产出效率,结合床位工作效率评估病区床位配置情况。结果 41 个非手术病区DEA综合效率均值为0.900±0.069,纯技术效率均值为0.917±0.056,规模效率均值为0.982±0.058,4 个病区达到DEA有效;61 个手术病区DEA综合效率均值为0.902±0.113,纯技术效率均值为0.924±0.088,规模效率均值为0.974±0.058,19 个病区达到DEA有效。结合病区规模报酬分析结果与床位工作效率,提出维持现状、优先增床、适度减床、暂不调整4 种床位调整策略。结论 本研究构建的DEA与床位工作效率综合评价模型,可为差异化配置病区床位提供科学方法。

     

    Abstract: Background Ward bed allocation directly determines both hospital service capacity and operational efficiency. However, existing studies have predominantly focused on the institutional level, often relying on single-indicator analyses that fail to capture the complex input-output efficiency dynamics within individual wards. Objective To analyze the input-output efficiency of hospital ward resources and provide a reference for ward resource adjustment. Methods Operational data of 102 wards from a large general tertiary hospital in Beijing were collected from January 1, 2024, to December 31, 2024. Wards were categorized into non-surgical and surgical groups according to department attributes. Both groups shared the same input indicators, while the surgical ward group included the number of patients undergoing Grade Ⅲ and Ⅳ surgeries as an additional output indicator. The BCC model of data envelopment analysis (DEA) was employed to evaluate the input-output efficiency of the two groups, combined with bed efficiency analysis to assess ward bed allocation. Results Among the 41 non-surgical wards, the mean DEA overall efficiency was 0.900±0.069, the mean pure technical efficiency was 0.917±0.056, and the mean scale efficiency was 0.982±0.058, with 4 wards achieving DEA efficiency. Among the 61 surgical wards, the corresponding values were 0.902±0.113, 0.924±0.088, and 0.974± 0.058, respectively, with 19 wards achieving DEA efficiency. Based on the results of returns to scale analysis and bed efficiency, four bed adjustment strategies were proposed: maintaining the status quo, prioritizing bed expansion, moderately reducing beds, and temporarily deferring adjustment. Conclusion The integrated DEA and bed efficiency model developed in this study provides a scientific decision-making tool for achieving differentiated bed allocation.

     

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