Leveraging Kaizen with Process Mining in Healthcare Settings: A Conceptual Framework for Data-Driven Continuous...

Area of Science:

• Healthcare Management
• Process Improvement
• Data Analytics

Background:

• Healthcare systems struggle with efficiency, resource optimization, and quality care.
• Traditional methods lack objective data or human-centered adaptability.
• Existing approaches are often subjective or lack adaptable, data-driven insights.

Purpose of the Study:

• To develop an integrated framework combining Kaizen and Process Mining for healthcare process optimization.
• To address limitations of traditional continuous improvement methodologies in healthcare.
• To create a novel approach for enhancing healthcare workflow efficiency.

Main Methods:

• A structured literature review of Kaizen and Process Mining in healthcare.
• Synthesis of peer-reviewed literature from the past two decades.
• Development of a conceptual framework integrating both methodologies.

Main Results:

• A four-phase framework: data collection, process analysis, Kaizen events, and continuous monitoring.
• Integration of employee-driven Kaizen with data-driven Process Mining for workflow analysis.
• Creation of a feedback loop for sustained, data-validated process improvements.

Conclusions:

• The Kaizen and Process Mining integration offers a promising strategy for healthcare efficiency.
• Potential benefits include enhanced workflow, reduced errors, and improved resource utilization.
• Future research should focus on empirical validation and AI-enhanced analytics in diverse healthcare settings.