巴西公立医院持续改进的精益DEA方法
Samuel Martins Drei1, Lidia Angulo-Meza2
1Federal Fluminense University , Niteroi, Brazil.
Journal of health organization and management
|September 4, 2025
概括
这项研究将精益医疗 (LH) 与数据环境分析 (DEA) 结合起来,以提高医院的效率和减少浪费. 这种新方法确定了医疗保健服务持续改进的最佳实践.
科学领域:
- 医疗管理
- 运营研究
- 公共卫生政策
背景情况:
- 医院面临着提高效率和减少浪费的压力,
- 精益医疗 (LH) 提供了流程优化的方法,但其对整体医院效率的影响需要严格的评估.
- 数据环境分析 (DEA) 是衡量决策单位 (DMU) 相对效率的强大工具.
研究的目的:
- 提出和验证结合精益医疗 (LH) 和数据环境分析 (DEA) 的综合方法,以提高医院的效率.
- 建立在医疗保健机构中减少浪费和改进过程的最佳实践.
- 为公共医院提供战略决策支持工具,用于资源配置和绩效提升.
主要方法:
- 开发了一种整合LH和DEA的两阶段方法.
- 第一个阶段使用非定向的Slack-Based Measure Data Envelopment Analysis (SBM-DEA) 来确定LH后实施的高效和低效的医院.
- 第二阶段将新的DEA模型应用于高效的医院套件,将理想的DMU纳入用于预测未来的效率和指导改进策略.
主要成果:
- 综合精益DEA方法有效地识别了高效和低效的医院,并提供了绩效指标.
- 该研究表明该方法能够为改善医疗保健服务提供战略指导.
- 包括理想的DMU允许动态的效率预测,并引导低效的医院转向有效的实践.
结论:
- 拟议的精益DEA模型是提高医院效率和资源配置的有价值的决策支持工具,特别是在资源有限的环境中.
- 整合LH和DEA,包括理想的DMU,为医疗保健的持续改进提供了整体和动态的方法.
- 这项研究有助于加强医疗保健系统,通过优化医院流程促进更公平的获取和更好的公共卫生结果.
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