在医疗保健环境中利用过程挖掘的开采:数据驱动的持续改进的概念框架
Mohammad Najeh Samara1, Kimberly D Harry1
1School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA.
Healthcare (Basel, Switzerland)
|April 26, 2025
概括
这项研究将Kaizen原则与过程采矿相结合,以提高医疗保健的效率. 综合方法使用数据分析和员工驱动的改进,以优化工作流程和更好的资源利用.
科学领域:
- 医疗保健管理的管理
- 过程改进 过程改进
- 数据分析数据分析数据分析.
背景情况:
- 医疗保健系统在效率,资源优化和质量护理方面扎.
- 传统方法缺乏客观数据或以人为中心的适应性.
- 现有的方法往往是主观的,或缺乏可适应的,数据驱动的洞察力.
研究的目的:
- 开发一个综合框架,将Kaizen和过程挖掘结合起来,以优化医疗保健过程.
- 解决医疗保健中传统的持续改进方法的局限性.
- 为提高医疗保健工作流程效率创造一种新的方法.
主要方法:
- 关于医疗保健中的Kaizen和过程采矿的结构化文献综述.
- 综合了过去二十年的同行评审文献.
- 开发一个整合两种方法的概念框架.
主要成果:
- 一个四阶段的框架:数据收集,流程分析,Kaizen事件和持续监控.
- 将员工驱动的Kaizen与数据驱动的过程挖掘集成用于工作流分析.
- 创建一个反循环,用于持续的,数据验证的流程改进.
结论:
- 凯森和过程采矿的整合为医疗保健效率提供了一个有前途的战略.
- 潜在的好处包括增强的工作流程,减少错误和改善资源利用.
- 未来的研究应该集中在经验验证和人工智能增强的分析在不同的医疗保健环境.
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