Journal of Engineering Research and Reports

Healthcare systems globally face persistent inefficiencies in clinical and administrative workflows due to complex, variable processes and limited visibility into real executions. This study developed an AI-enabled process mining framework designed for the automated discovery and improvement of these workflows through remote analysis of publicly accessible event logs. Using the Sepsis Cases - Event Log.xes as the primary dataset, the study applied AI-assisted discovery techniques including Heuristics Miner, Fuzzy Miner, and trace clustering to model clinical pathways and administrative activities. Conformance checking via optimal alignments assessed adherence to expected guidelines, highlighting deviations and bottlenecks. Predictive optimization was achieved using a Long Short-Term Memory (LSTM) neural network for next-activity forecasting and bottleneck detection, producing simulated throughput time reductions of approximately 18%. Performance metrics indicated moderate but informative conformance results: fitness of 0.609, precision of 0.443, composite F1-score of 0.64, and an AUC-ROC of 0.931 for predictive monitoring. The framework effectively enabled remote, reproducible analysis without direct data access, providing a practical blueprint for data-driven workflow improvement in resource-constrained environments. Despite limitations involving dataset specificity and noise sensitivity, the approach advances process mining applications in healthcare by integrating artificial intelligence for actionable insights. Future work should emphasize multi-dataset validation, explainability, and real-time deployment to further strengthen scalability and clinical decision support impact.