TY - JOUR
T1 - Nursing Variables Predicting Readmissions in Patients with a High Risk
T2 - A Scoping Review
AU - Lee, Ji Yea
AU - Park, Jisu
AU - Choi, Hannah
AU - Oh, Eui Geum
N1 - Publisher Copyright:
© 2024 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2024/8/2
Y1 - 2024/8/2
N2 - 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.
AB - 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.
KW - Nursing data
KW - Nursing informatics
KW - Patient readmission
KW - Prediction model
KW - Scoping review
UR - http://www.scopus.com/inward/record.url?scp=85200556380&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85200556380&partnerID=8YFLogxK
U2 - 10.1097/CIN.0000000000001172
DO - 10.1097/CIN.0000000000001172
M3 - Review article
C2 - 39093059
AN - SCOPUS:85200556380
SN - 1538-2931
VL - 42
SP - 852
EP - 861
JO - CIN - Computers Informatics Nursing
JF - CIN - Computers Informatics Nursing
IS - 12
ER -