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Factors associated with poor self-management documented in home health care narrative notes for patients with heart failure
Journal article   Peer reviewed

Factors associated with poor self-management documented in home health care narrative notes for patients with heart failure

Sena Chae, Jiyoun Song, Marietta Ojo, Kathryn H. Bowles, Margaret V. McDonald, Yolanda Barrón, Mollie Hobensack, Erin Kennedy, Sridevi Sridharan, Lauren Evans, …
Heart & lung, Vol.55, pp.148-154
09/2022
DOI: 10.1016/j.hrtlng.2022.05.004
PMCID: PMC11021173
PMID: 35597164

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Abstract

•Patients with HF who have poor self-management can be identified from narrative notes using NLP.•Younger age, gender (male), smoking, and comorbid conditions are associated with poor self-management.•Knowing which patient factors are associated with poor self-management may assist HHC clinicians in developing individualized care plans. Patients with heart failure (HF) who actively engage in their own self-management have better outcomes. Extracting data through natural language processing (NLP) holds great promise for identifying patients with or at risk of poor self-management. To identify home health care (HHC) patients with HF who have poor self-management using NLP of narrative notes, and to examine patient factors associated with poor self-management. An NLP algorithm was applied to extract poor self-management documentation using 353,718 HHC narrative notes of 9,710 patients with HF. Sociodemographic and structured clinical data were incorporated into multivariate logistic regression models to identify factors associated with poor self-management. There were 758 (7.8%) patients in this sample identified as having notes with language describing poor HF self-management. Younger age (OR 0.982, 95% CI 0.976–0.987, p < .001), longer length of stay in HHC (OR 1.036, 95% CI 1.029– 1.043, p < .001), diagnosis of diabetes (OR 1.47, 95% CI 1.3–1.67, p < .001) and depression (OR 1.36, 95% CI 1.09–1.68, p < .01), impaired decision-making (OR 1.64, 95% CI 1.37–1.95, p < .001), smoking (OR 1.7, 95% CI 1.4–2.04, p < .001), and shortness of breath with exertion (OR 1.25, 95% CI 1.1–1.42, p < .01) were associated with poor self-management. Patients with HF who have poor self-management can be identified from the narrative notes in HHC using novel NLP methods. Meaningful information about the self-management of patients with HF can support HHC clinicians in developing individualized care plans to improve self-management and clinical outcomes.
Electronic Health Records Heart Failure Home care services Natural language processing Nursing informatics Self-management

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