4.5 Article

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

Journal

HEART & LUNG
Volume 55, Issue -, Pages 148-154

Publisher

MOSBY-ELSEVIER
DOI: 10.1016/j.hrtlng.2022.05.004

Keywords

Heart failure; Home care services; Self-management; Natural language processing; Electronic health records; Nursing informatics

Funding

  1. Agency for Healthcare Research and Quality (AHRQ) [R01HS027742]
  2. National Institutes of Health [T32NR0769]
  3. Jonas Scholarship
  4. National Institute of Nursing Research [F31NR019919]

Ask authors/readers for more resources

Using natural language processing methods, patients with heart failure who have poor self-management can be identified in home health care. Younger age, longer length of stay in home health care, diagnosis of diabetes and depression, impaired decision-making, smoking, and shortness of breath with exertion are associated with poor self-management.
Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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. (C) 2022 Elsevier Inc. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available