Studying Associations Between Heart Failure Self-Management and Rehospitalizations Using Natural Language Processing. (January 2017)

• Record Type: Journal Article
• Authors: Topaz, Maxim; Radhakrishnan, Kavita; Blackley, Suzanne; Lei, Victor; Lai, Kenneth; Zhou, Li
• Is Part Of: Western journal of nursing research. Volume 39:Number 1(2017)
• Page Start: 147
• Page End: 165
• Publication Date: 2017-01
• Subjects: natural language processing -- cardiovascular -- clinical focus -- heart failure -- health screening behaviors -- health behavior/symptom focus -- therapeutic medications -- health behavior/symptom focus -- self-care -- patient compliance -- diet -- sodium restricted -- medication adherence; Nursing -- Research -- Periodicals; Nursing -- Periodicals; Nursing -- Research -- West (U.S.) -- Periodicals; Nursing -- West (U.S.) -- Periodicals; Nursing -- Periodicals; Nursing Research -- Periodicals; Soins infirmiers -- Recherche -- Périodiques; Soins infirmiers -- Périodiques; Soins infirmiers -- Recherche -- États-Unis (Ouest) -- Périodiques; Soins infirmiers -- États-Unis (Ouest) -- Périodiques; Soins infirmiers; Recherche; États-Unis (Ouest); Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 610.730702
• DOI: 10.1177/0193945916668493
• Languages: English
• ISSNs: 0193-9459

Abstract

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.