Learning Patient-Specific Event Sequence Representations for Clinical Process Analysis

Abstract Healthcare system performance evaluation is constrained by episodic performance indicators and process mining techniques that fail to accommodate the scale, heterogeneity, and temporal complexity of real-world clinical pathways. Electronic health records enable reconstructing patient journeys that capture how care processes unfold across fragmented healthcare services. Here we present ClinicalTAAT, a time-aware transformer that bridges clinical sequence modeling and process mining by integrating contextual and time-varying information to learn interpretable patient-specific representations from inherently sparse, irregular and high-dimensional clinical event sequences. Evaluated on a large pediatric emergency cohort, ClinicalTAAT outperforms existing models in acuity and diagnosis classification, identifies clinically meaningful patient subgroups in heterogenous population with distinct acuity, resource utilization and diagnostic patterns, and detects anomalies in individual care trajectories. These findings demonstrate that time-aware transformers can complement existing process mining methodologies and serve as foundation models for clinical process analysis, providing a scalable framework for data-driven healthcare evaluation and optimization.