ArticleJournal of the American Medical Informatics Association : JAMIA2023
Uncovering hidden trends: identifying time trajectories in risk factors documented in clinical notes and predicting hospitalizations and emergency department visits during home health care.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- A clustering-based trajectory analytics of functional loss and recovery among older adults.PloS one · 2026Article
- Standardized nursing terminologies come of age: advancing quality of care, population health, and health equity across the care continuum.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A Dynamic Time Warping Extension to Consensus Weight-Based Cachexia Criteria Improves Prediction of Cancer Patient Outcomes.JCSM communicationsArticle
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Authors and funding
12 authors.
Funding
Abstract
objectiveThis study aimed to identify temporal risk factor patterns documented in home health care (HHC) clinical notes and examine their association with hospitalizations or emergency department (ED) visits. MATERIALS AND
methodsData for 73 350 episodes of care from one large HHC organization were analyzed using dynamic time warping and hierarchical clustering analysis to identify the temporal patterns of risk factors documented in clinical notes. The Omaha System nursing terminology represented risk factors. First, clinical characteristics were compared between clusters. Next, multivariate logistic regression was used to examine the association between clusters and risk for hospitalizations or ED visits. Omaha System domains corresponding to risk factors were analyzed and described in each cluster.
resultsSix temporal clusters emerged, showing different patterns in how risk factors were documented over time. Patients with a steep increase in documented risk factors over time had a 3 times higher likelihood of hospitalization or ED visit than patients with no documented risk factors. Most risk factors belonged to the physiological domain, and only a few were in the environmental domain. DISCUSSION: An analysis of risk factor trajectories reflects a patient's evolving health status during a HHC episode. Using standardized nursing terminology, this study provided new insights into the complex temporal dynamics of HHC, which may lead to improved patient outcomes through better treatment and management plans.
conclusionIncorporating temporal patterns in documented risk factors and their clusters into early warning systems may activate interventions to prevent hospitalizations or ED visits in HHC.
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