On Learning and Visualizing Practice-based Clinical Pathways for Chronic Kidney Disease

Chronic Kidney Disease (CKD) is a costly and complex disease affecting 20 million US adults. Recent studies suggest that care delivery changes may improve clinical outcomes and quality of patient experience while reducing costs. This study analyzes the treatment data of 8,553 CKD patients to learn practice-based clinical pathways. Patients' visit history is modeled as sequences of visits containing information on visit type, date, procedures and diagnoses. We use hierarchical clustering based on longest common subsequence (LCS) distance to discover six patient subgroups, with each subgroup differing in the distribution of demographics and health conditions. Transitions of visits with high probabilities are elicited from each patient subgroup to learn common clinical pathways and treatment durations. Insights from this study can potentially result in new evidence to support patient-centered treatment approaches, empower CKD patients to better manage their disease and its complications, and provide a review guide for clinicians.