Process Mining in Healthcare Process Simulation: A Structured Narrative Review of Trends, Barriers, and Opportunities

Document Type : Systematic Review

Author
mohsen mohammadi
Associate Professor, Information Technology, Computer Department, Esfarayen Technical and Engineering Higher Education Complex, Esfarayen, Iran.
10.48305/him.2026.46060.1380
Abstract
Introduction: As healthcare systems become increasingly complex, the adoption of analytical approaches has become essential to improve operational efficiency, service quality, patient outcomes, and resource allocation. This study explores the potential of integrating process mining and process simulation as two data-driven methodologies for healthcare analysis, evaluation, and decision support
Methods: This study was conducted using a structured narrative-analytical review approach. Beyond explaining the theoretical foundations of process mining and process simulation, this research critically analyzes the synergistic framework of their integration, clinical applications, structural challenges, and future opportunities through a critical synthesis of existing evidence. The primary focus is on examining the role of process mining in extracting knowledge from event data and critically evaluating its utilization in designing and validating simulation models.
Results: The findings indicate that process mining enhances the accuracy of healthcare simulation models by discovering real-world pathways, identifying bottlenecks, and extracting empirical parameters. Conversely, simulation enables scenario evaluation, outcome prediction, and resource optimization. The integration of these two approaches reinforces evidence-based decision-making, reduces waiting times, and enhances service quality.
Conclusion: The integration of process mining and simulation provides an effective framework for analyzing and improving complex healthcare processes. However, data quality issues, privacy concerns, clinical workflow complexity, technical barriers, and organizational collaboration challenges limit its widespread implementation. Developing standardized frameworks, ensuring high-quality data, leveraging artificial intelligence, and conducting applied empirical studies represent key directions for future research.
Keywords
Process mining
simulation
process improvement
artificial intelligence
predictive analytics
event simulation
open source
Subjects
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