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The identification of clusters of risk factors and their association with hospitalizations or emergency department visits in home health care

Authors :
Jiyoun Song
Sena Chae
Kathryn H. Bowles
Margaret V. McDonald
Yolanda Barrón
Kenrick Cato
Sarah Collins Rossetti
Mollie Hobensack
Sridevi Sridharan
Lauren Evans
Anahita Davoudi
Maxim Topaz
Source :
J Adv Nurs
Publication Year :
2022
Publisher :
Wiley, 2022.

Abstract

AIMS: To identify clusters of risk factors in home health care and determine if the clusters are associated with hospitalizations or emergency department visits. DESIGN: A retrospective cohort study. METHODS: This study included 61,454 patients pertaining to 79,079 episodes receiving home health care between 2015 and 2017 from one of the largest home health care organizations in the United States. Potential risk factors were extracted from structured data and unstructured clinical notes analysed by natural language processing. A K-means cluster analysis was conducted. Kaplan–Meier analysis was conducted to identify the association between clusters and hospitalizations or emergency department visits during home health care. RESULTS: A total of 11.6% of home health episodes resulted in hospitalizations or emergency department visits. Risk factors formed three clusters. Cluster 1 is characterized by a combination of risk factors related to “impaired physical comfort with pain,” defined as situations where patients may experience increased pain. Cluster 2 is characterized by “high comorbidity burden” defined as multiple comorbidities or other risks for hospitalization (e.g., prior falls). Cluster 3 is characterized by “impaired cognitive/psychological and skin integrity” including dementia or skin ulcer. Compared to Cluster 1, the risk of hospitalizations or emergency department visits increased by 1.95 times for Cluster 2 and by 2.12 times for Cluster 3 (all p < .001). CONCLUSION: Risk factors were clustered into three types describing distinct characteristics for hospitalizations or emergency department visits. Different combinations of risk factors affected the likelihood of these negative outcomes. IMPACT: Cluster-based risk prediction models could be integrated into early warning systems to identify patients at risk for hospitalizations or emergency department visits leading to more timely, patient-centred care, ultimately preventing these events. PATIENT OR PUBLIC CONTRIBUTION: There was no involvement of patients in developing the research question, determining the outcome measures, or implementing the study.

Subjects

Subjects :
Article
General Nursing

Details

ISSN :
13652648 and 03092402
Volume :
79
Database :
OpenAIRE
Journal :
Journal of Advanced Nursing
Accession number :
edsair.doi.dedup.....a70e72c309338c857984e33de1faeb28