Analyzing Healthcare Processes with Incremental Process Discovery: Practical Insights from a Real-World Application

Area of Science:

• Computer Science
• Information Systems
• Healthcare Informatics

Background:

• Traditional process mining tools operate as "black boxes" with limited analyst interaction.
• Recent advancements focus on hybrid intelligence, incorporating domain knowledge into process mining.
• Interactive process discovery techniques are emerging but lack practical application studies.

Purpose of the Study:

• To investigate the practical application of incremental and interactive process discovery in the healthcare domain.
• To evaluate the effectiveness of hybrid intelligence in addressing healthcare-specific process mining challenges.

Main Methods:

• A case study was conducted in the healthcare sector.
• Employed incremental and interactive process discovery techniques.
• Focused on integrating domain knowledge for process analysis.

Main Results:

• The interactive process mining approach effectively addressed challenges in healthcare data.
• Demonstrated successful application of hybrid intelligence in a complex domain.
• Showcased the ability to handle high process execution variability and poor data quality.

Conclusions:

• Interactive process discovery is a viable and effective approach for healthcare process mining.
• Hybrid intelligence can significantly improve process analysis in complex, real-world settings.
• Further research into interactive techniques in specialized domains is warranted.