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

As healthcare systems become more complex, advanced data-driven methods are needed to improve efficiency, patient outcomes, and resource allocation. This study examines the integration of process mining and healthcare process simulation to support data-driven decision-making.This analytical review explores the basic concepts of process mining and process simulation, their synergistic integration, healthcare applications, implementation challenges, and future research opportunities.Process mining can enhance simulation models by discovering actual care pathways, identifying bottlenecks, assessing process conformance, and extracting empirical parameters from event logs. Simulation, in turn, enables the evaluation of improvement scenarios, prediction of operational outcomes, analysis of patient flow, and optimization of resources. Together, these approaches can support evidence-based decision-making and improve healthcare service delivery.Integrating process mining and simulation provides a valuable framework for analyzing and improving healthcare processes. However, challenges related to data quality, privacy, workflow complexity, technical limitations, and organizational collaboration remain. Future research should focus on standardized frameworks, high-quality data, predictive analytics, and practical implementation studies.