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Issues And Trends In Healthcare Delivery System01:29

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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利用机器学习和规范分析来提高手术室吞吐量.

Farid Al Zoubi1, Georges Khalaf2, Paul E Beaulé3

  • 1School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada.

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概括
此摘要是机器生成的。

每天增加第五次关节整形手术可以显著减少部和膝盖手术等待时间. 这可以通过提高效率来实现,每年可能增加35-56个案例,而不会增加加班或成本.

关键词:
医疗保健的效率 医疗保健的效率高体积的外科手术.机器学习是机器学习.操作室的吞吐量是操作室的吞吐量.预测分析 预测分析规范性分析是指规范性分析.时间预测时间预测

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科学领域:

  • 整形外科手术 整形外科手术
  • 医疗保健管理的管理
  • 卫生经济学 卫生经济学

背景情况:

  • 部和膝盖关节整形手术的等待时间增加是一个重大挑战.
  • 在外科部门的加班代表了相当大的,往往没有量化,成本.
  • 目前的外科手术计划通常每天容纳四次关节整形手术.

研究的目的:

  • 为了确定第五次关节整形手术是否可以在不增加加班的情况下添加到每日计划中.
  • 评估增加手术效率对减少患者等待时间的潜在影响.
  • 评估优化外科手术吞吐量的成本效益.

主要方法:

  • 分析了460个失败的日子,以量化总额外的工作时间.
  • 隔离了301个成功日,以开发线性回归模型来预测第五个案例的可行性.
  • 使用预测模型来估计在不同成功率下可实现的额外病例的数量.

主要成果:

  • 在460个不成功的日子里,记录了465多个加班时间.
  • 完成手术的77%的成功率每年可以产生大约35个额外的病例.
  • 如果达到100%的成功率,那么每年可能会有56个额外的病例,没有额外的费用.

结论:

  • 优化手术效率提供了一种可行的策略,以增加关节整形术病例数量.
  • 将资源从加班重新分配到增加的外科手术能力可以减少等待列表.
  • 建议对最佳人员配置进行进一步的研究,以支持增加外科手术产出.