Samara Mohammad Najeh, Harry Kimberly D
School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA.
Healthcare (Basel). 2025 Apr 19;13(8):941. doi: 10.3390/healthcare13080941.
Healthcare systems face persistent challenges in improving efficiency, optimizing resources, and delivering high-quality care. Traditional continuous improvement methodologies often rely on subjective assessments, while data-driven approaches typically lack human-centered adaptability. This study aims to develop an integrated framework combining Kaizen principles with Process Mining capabilities to address these limitations in healthcare process optimization. This research employed a structured literature review approach to identify key concepts, methodologies, and applications of both Kaizen and Process Mining in healthcare settings. The study synthesized insights from the peer-reviewed literature published in the last two decades to develop a conceptual framework integrating these approaches for healthcare process improvement. The proposed framework combines Kaizen's employee-driven approach to eliminating inefficiencies with Process Mining's ability to analyze workflow data and identify process deviations. The integration is structured into four key phases: data collection, process analysis, Kaizen events, and continuous monitoring. This structure creates a feedback loop where data-driven insights inform collaborative problem-solving, resulting in sustained improvements validated through objective process analysis. The integration of Kaizen and Process Mining offers a promising approach to enhancing workflow efficiency, reducing operational errors, and improving resource utilization in healthcare settings. While challenges such as data quality concerns, resource constraints, and potential resistance to change must be addressed, the framework provides a foundation for more effective process optimization. Future research should focus on empirical validation, AI-enhanced analytics, and assessing adaptability across diverse healthcare contexts.
医疗保健系统在提高效率、优化资源和提供高质量护理方面面临着持续的挑战。传统的持续改进方法通常依赖主观评估，而数据驱动的方法通常缺乏以人为本的适应性。本研究旨在开发一个将改善原则与流程挖掘能力相结合的综合框架，以解决医疗保健流程优化中的这些局限性。本研究采用结构化文献综述方法，以确定改善和流程挖掘在医疗保健环境中的关键概念、方法和应用。该研究综合了过去二十年发表的同行评审文献中的见解，以开发一个整合这些方法以改善医疗保健流程的概念框架。所提出的框架将改善中员工驱动的消除低效率方法与流程挖掘分析工作流数据和识别流程偏差的能力相结合。这种整合被构建为四个关键阶段：数据收集、流程分析、改善活动和持续监控。这种结构创建了一个反馈循环，数据驱动的见解为协作解决问题提供信息，从而通过客观的流程分析验证持续改进。改善和流程挖掘的整合为提高医疗保健环境中的工作流效率、减少操作错误和提高资源利用率提供了一种很有前景的方法。虽然数据质量问题、资源限制和对变革的潜在抵制等挑战必须得到解决，但该框架为更有效的流程优化提供了基础。未来的研究应侧重于实证验证、人工智能增强的分析以及评估在不同医疗保健环境中的适应性。
