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战略瘤学工作流程优化的综合预测影响增强过程挖掘框架:伊朗的案例研究.

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  • 1Department of Computer Engineering and IT, Shiraz University of Technology, Shiraz 13876-71557, Iran.

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过程挖矿 (PM) 现在可以预测医疗保健工作流变化的影响. 我们的新框架量化了修复偏差如何减少周期时间和工作量,从而实现数据驱动的战略规划.

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科学领域:

  • 医疗保健 运营 研究 研究 研究
  • 过程采矿应用程序 过程采矿应用程序
  • 临床工作流的优化 临床工作流优化

背景情况:

  • 过程挖掘 (PM) 识别了医疗保健工作流程的低效率,但缺乏定量影响评估.
  • 目前的方法是回顾性或使用脱节的模拟,阻碍了基于证据的资源分配.
  • 在预测纠正工作流程偏差对整个系统的运营影响方面存在方法上的差距.

研究的目的:

  • 引入PM2-预测影响模型 (PIM) 框架,以弥合工作流偏差校正影响量化的差距.
  • 将合规性检查,预测性监测和场景分析统一到一个闭环的,流程原生方法.
  • 通过量化干预影响,使医疗保健业务的数据驱动战略规划成为可能.

主要方法:

  • 开发了PM2-预测影响模型 (PIM) 框架,整合了过程挖掘技术.
  • 模拟了一个规范性的七步路径,使用来自伊朗放射治疗和瘤中心的事件日志 (适应性=0.97,精度=1.00).
  • 确定了具有重大影响的偏差 (例如,跳过批准,重新排序),并使用PIM模拟了它们的去除.

主要成果:

  • 该PIM框架确定了偏差类型和系统性能之间的因果关系.
  • 模拟显示,在消除偏差后,循环时间 (8.00%) 和工作负载 (6.00%) 在统计学上显著减少.
  • 结果与参数不确定性 (p < 0.001) 保持一致,证实了可靠性.

结论:

  • PM2-PIM框架将追溯过程采矿诊断转化为主动的,定量化的战略规划.
  • 为瘤服务提供一种可重复的,低成本的方法,以优先考虑干预措施.
  • 能够进行富有证据的决策,从而在医疗保健运营中持续提高绩效.