Studying Associations Between Heart Failure Self-Management and Rehospitalizations Using Natural Language Processing

This study developed an innovative natural language processing algorithm to automatically identify heart failure (HF) patients with ineffective self-management status (in the domains of diet, physical activity, medication adherence, and adherence to clinician appointments) from narrative discharge summary notes. We also analyzed the association between self-management status and preventable 30-day hospital readmissions. Our natural language system achieved relatively high accuracy ( F-measure = 86.3%; precision = 95%; recall = 79.2%) on a testing sample of 300 notes annotated by two human reviewers. In a sample of 8,901 HF patients admitted to our healthcare system, 14.4% ( n = 1,282) had documentation of ineffective HF self-management. Adjusted regression analyses indicated that presence of any skill-related self-management deficit (odds ratio OR = 1.3, 95% confidence interval CI = 1.1, 1.6) and non-specific ineffective self-management (OR = 1.5, 95% CI = 1.2, 2) was significantly associated with readmissions. We have demonstrated the feasibility of identifying ineffective HF self-management from electronic discharge summaries with natural language processing.