ArticleComputers, informatics, nursing : CIN2020
Factors Affecting Patient Prioritization Decisions at Admission to Home Healthcare: A Predictive Study to Develop a Risk Screening Tool.
Article in Computers, informatics, nursing : CIN, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Machine learning for predicting institutionalization and mortality risks among older home care recipients with routinely collected need assessment data: explainable AI for long-term care.BMC medical informatics and decision making · 2026Article
- Identifying and Reducing Stigmatizing Language in Home Health Care With a Natural Language Processing-Based System (ENGAGE): Protocol for a Mixed Methods Study.JMIR research protocols · 2025Article
- Factors Associated with Timing of the Start-of-Care Nursing Visits in Home Health Care.Journal of the American Medical Directors Association · 2021Article
- Planning the Episode: Home Care Admission Nurse Decision-Making Regarding the Patient Visit Pattern.Home health care management & practice · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
There is a lack of evidence on how to identify high-risk patients admitted to home healthcare. This study aimed (1) to identify which disease characteristics, medications, patient needs, social support characteristics, and other factors are associated with patient priority for the first home health nursing visit; and (2) to construct and validate a predictive model of patient priority for the first home health nursing visit. This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults. The study found that nurses were more likely to prioritize patients who had wounds (odds ratio = 1.88), comorbid condition of depression (odds ratio = 1.73), limitation in current toileting status (odds ratio = 2.02), higher number of medications (increase in odds ratio for each medication = 1.04), and comorbid conditions (increase in odds ratio for each condition = 1.04). This study developed one of the first clinical decision support tools for home healthcare called "PREVENT". (PRiority home health Visit Tool). Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes.
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Registered trials
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