From Global to Context-Specific Process Views: A Configurable Process Mining Framework for Hospital Billing Analysis

Hospital billing event logs combine shared administrative routines with context-specific behavior, making a single global process model difficult to interpret. This study proposes a configurable process mining framework that complements a hospital-wide model with context-specific views derived from Hospital Billing data. Seventeen medical specialties (97,753 cases; 437,444 events) were retained and split temporally into 70% Train and 30% Test cases. Train data were used for behavioral representation, similarity analysis, hierarchical clustering, process discovery, merging, and configuration. The selected two-cluster solution (mean silhouette = 0.8960) separated specialty K from the remaining 16 specialties. Global Train analysis showed a tie among IMf thresholds 0.40, 0.50, and 0.60; 0.60 was retained as the common configuration threshold because it reduced configuration points from 25 to 20. Cluster-specific Process Trees were merged into a configurable model with 41 unique nodes and 20 configurable relations, and Genetic search produced Derived Process Trees. On held-out Test data, the Derived K model achieved fitness/balanced precision of 0.9992/0.6690, while the Derived Others model achieved 0.8598/0.9305. Both remained competitive with independently mined local baselines while preserving shared and context-dependent behavior within one process family.