Cited 0 times in

Nursing Variables Predicting Readmissions in Patients With a High Risk

Authors
 Ji Yea Lee  ;  Jisu Park  ;  Hannah Choi  ;  Eui Geum Oh 
Citation
 CIN-COMPUTERS INFORMATICS NURSING, Vol.42(12) : 852-861, 2024-12 
Journal Title
CIN-COMPUTERS INFORMATICS NURSING
ISSN
 1538-2931 
Issue Date
2024-12
MeSH
Humans ; Nursing Assessment* ; Nursing Diagnosis ; Patient Discharge ; Patient Readmission* / statistics & numerical data ; Risk Assessment ; Risk Factors
Abstract
Unplanned readmission endangers patient safety and increases unnecessary healthcare expenditure. Identifying nursing variables that predict patient readmissions can aid nurses in providing timely nursing interventions that help patients avoid readmission after discharge. We aimed to provide an overview of the nursing variables predicting readmission of patients with a high risk. The authors searched five databases-PubMed, CINAHL, EMBASE, Cochrane Library, and Scopus-for publications from inception to April 2023. Search terms included "readmission" and "nursing records." Eight studies were included for review. Nursing variables were classified into three categories-specifically, nursing assessment, nursing diagnosis, and nursing intervention. The nursing assessment category comprised 75% of the nursing variables; the proportions of the nursing diagnosis (25%) and nursing intervention categories (12.5%) were relatively low. Although most variables of the nursing assessment category focused on the patients' physical aspect, emotional and social aspects were also considered. This study demonstrated how nursing care contributes to patients' adverse outcomes. The findings can assist nurses in identifying the essential nursing assessment, diagnosis, and interventions, which should be provided from the time of patients' admission. This can mitigate preventable readmissions of patients with a high risk and facilitate their safe transition from an acute care setting to the community.
Full Text
https://journals.lww.com/cinjournal/fulltext/2024/12000/nursing_variables_predicting_readmissions_in.3.aspx
DOI
10.1097/CIN.0000000000001172
Appears in Collections:
3. College of Nursing (간호대학) > Dept. of Nursing (간호학과) > 1. Journal Papers
Yonsei Authors
Oh, Eui Geum(오의금) ORCID logo https://orcid.org/0000-0002-6941-0708
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/201622
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links