Constructing Patient Specific Clinical Trajectories from Electronic Healthcare Reimbursement Claims using Sequential Pattern Mining
We examine the use of electronic healthcare reimbursement claims (EHRC) for analyzing healthcare delivery and practice patterns across the United States (US). By analyzing over 1 billion EHRCs, we track patterns of clinical procedures administered to patients with heart disease (HD) using sequential pattern mining algorithms. Our analyses reveal that the clinical procedures performed on HD patients are highly varied leading up to and after the primary diagnosis. The discovered clinical procedure sequences reveal significant differences in the overall costs incurred across different parts of the US, indicating significant heterogeneity in treating HD patients. We show that a data-driven approach to understand patient specific clinical trajectories constructed from EHRC can provide quantitative insights into how to better manage and treat patients.