Heterogeneous but Segmentable: A Data-Driven Approach to Modelling Long-Term Care Trajectories in Multiple Sclerosis

Abstract Managing chronic diseases with unpredictable care demand creates significant operational challenges for healthcare systems. Mapping long-term care trajectories is crucial for improving resource allocation, anticipating service needs, and designing efficient care pathways. We used a data-driven approach to map six-year care trajectories for 962 newly diagnosed multiple sclerosis patients, identify utilization clusters, and determine predictors of high utilization. We analyzed Event logs of remote, outpatient, emergency, and inpatient contacts from one year pre- to five years post-diagnosis using K-means clustering to identify utilization clusters, logistic regression to identify predictors of high utilization, and process mining to model variation between care trajectories. We identified two distinctive utilization clusters: a high-utilization cluster (14.1 % of patients) with persistently elevated annual encounter volumes across all care settings and low-utilization cluster (85.9 % of patients) with lower and declining use. Median service costs were €18,736 vs. €6,052 in high- and low-utilization clusters, respectively. Two or more early relapses were the strongest predictor of high utilization (OR = 6.33, 95 % CI 3.49-11.50, p < 0.001), with number of planned early remote and outpatient care contacts being also associated with future service utilization (OR = 1.07, 95 % CI 1.04–1.10, p < 0.001). High-utilization trajectories were approximately three times longer (82.4 vs 25.9 events) and more variable (3.1 vs 2.4 unique events per patient). These utilization clusters and their distinct trajectories provide a pragmatic segmentation of multiple sclerosis patients to support early identification of high-utilization subgroups and more robust capacity planning in specialist care. Highlights We tracked the care trajectories of 962 people with relapsing–remitting multiple sclerosis using a Finnish population-based specialist-care datalake covering both inpatient and outpatient neurology services. Patients fell into two distinct utilization clusters: a high-utilization cluster with frequent contacts across all care settings and a low-utilization cluster with lower and declining use. Two or more early relapses, and the number of early outpatient and remote contacts were strong predictors of a patient’s long-term affiliation in the high-utilization cluster. Segmented care trajectories showed that high-utilization patients followed longer, more varied, and acute-oriented care patterns and had much higher service encounter costs. These findings can help clinicians and managers identify potential high-utilization patients early, target resources more effectively, and plan for future healthcare demand.