Predicting patient nurse-level intensity for a subsequent shift in the intensive care unit: A single-centre prospective observational study.
Critical care
Intensive care units
Manpower
Nurses
Nursing activities score
Physicians
Statistics & numerical data
Surveys and questionnaires
Workload
Journal
International journal of nursing studies
ISSN: 1873-491X
Titre abrégé: Int J Nurs Stud
Pays: England
ID NLM: 0400675
Informations de publication
Date de publication:
Sep 2020
Sep 2020
Historique:
received:
31
05
2019
revised:
23
04
2020
accepted:
25
05
2020
pubmed:
1
7
2020
medline:
29
7
2021
entrez:
29
6
2020
Statut:
ppublish
Résumé
A dynamic optimized nurse staffing model for the Intensive Care Unit (ICU), requires a tool for monitoring nurse-level intensity with validated cut-offs to identify patients requiring 1:1, 2:1 or 3:1 patient-to-nurse ratios. We aimed to determine the Nursing Activities Score (NAS) cut-off values which can best distinguish between high, average and lower nurse-level intensity as unanimously perceived by care providers, and to evaluate whether these NAS cut-offs allow to predict nurse-level intensity in the next shift or the same shift the next day. A prospective observational study. 9 ICUs in a Belgian tertiary care center. All 3295 patients admitted between March 20, 2013, and September 12, 2013 were included. NAS was quantified at the end of each shift using automatically derived and manually entered care information. Additionally, 412 nurses, 24 nurse managers and 37 physicians rated perceived nurse-level intensity. We first assessed concordance between nurses', nurse managers' and physicians' perceptions of lower (3:1 patient-to-nurse ratio), average (2:1 patient-to-nurse ratio) and high (1:1 patient-to-nurse ratio) nurse-level intensity. Next, receiver operating characteristic (ROC) analysis was applied to determine the NAS cut-offs that best distinguish between different levels of perceived intensity for cases with concordant opinions. Last, logistic regression analysis was applied to estimate the ability of these NAS cut-offs to predict low and high perceived intensity during the next shift and during the same shift the next day. Nurses', nurse managers' and physicians' perceptions were concordant in 57.1% (n = 4693) of cases, mostly concerning lower or average intensity. Optimal NAS cut-offs for lower and high intensity patients equaled 52.7% and 69.8%, respectively. The lower intensity NAS cut-off showed 74.0% accuracy to predict lower intensity in the next shift and 75.9% accuracy to predict lower intensity for the same shift the next day. The high intensity NAS cut-off showed 67.9% accuracy to predict high intensity in the next shift and 72.0% accuracy to predict high intensity for the same shift the next day. NAS cut-offs could contribute considerably in predicting patient nurse-level intensity, and thus patient-to-nurse staffing ratios, in the next shift or day. Identification or prediction of high intensity, nevertheless, appears most complex and requires further study. Future studies need to account for the many confounding variables which complicate nurse staffing planning.
Sections du résumé
BACKGROUND
BACKGROUND
A dynamic optimized nurse staffing model for the Intensive Care Unit (ICU), requires a tool for monitoring nurse-level intensity with validated cut-offs to identify patients requiring 1:1, 2:1 or 3:1 patient-to-nurse ratios.
OBJECTIVES
OBJECTIVE
We aimed to determine the Nursing Activities Score (NAS) cut-off values which can best distinguish between high, average and lower nurse-level intensity as unanimously perceived by care providers, and to evaluate whether these NAS cut-offs allow to predict nurse-level intensity in the next shift or the same shift the next day.
DESIGN
METHODS
A prospective observational study.
SETTING
METHODS
9 ICUs in a Belgian tertiary care center.
PARTICIPANTS
METHODS
All 3295 patients admitted between March 20, 2013, and September 12, 2013 were included. NAS was quantified at the end of each shift using automatically derived and manually entered care information. Additionally, 412 nurses, 24 nurse managers and 37 physicians rated perceived nurse-level intensity.
METHODS
METHODS
We first assessed concordance between nurses', nurse managers' and physicians' perceptions of lower (3:1 patient-to-nurse ratio), average (2:1 patient-to-nurse ratio) and high (1:1 patient-to-nurse ratio) nurse-level intensity. Next, receiver operating characteristic (ROC) analysis was applied to determine the NAS cut-offs that best distinguish between different levels of perceived intensity for cases with concordant opinions. Last, logistic regression analysis was applied to estimate the ability of these NAS cut-offs to predict low and high perceived intensity during the next shift and during the same shift the next day.
RESULTS
RESULTS
Nurses', nurse managers' and physicians' perceptions were concordant in 57.1% (n = 4693) of cases, mostly concerning lower or average intensity. Optimal NAS cut-offs for lower and high intensity patients equaled 52.7% and 69.8%, respectively. The lower intensity NAS cut-off showed 74.0% accuracy to predict lower intensity in the next shift and 75.9% accuracy to predict lower intensity for the same shift the next day. The high intensity NAS cut-off showed 67.9% accuracy to predict high intensity in the next shift and 72.0% accuracy to predict high intensity for the same shift the next day.
CONCLUSIONS
CONCLUSIONS
NAS cut-offs could contribute considerably in predicting patient nurse-level intensity, and thus patient-to-nurse staffing ratios, in the next shift or day. Identification or prediction of high intensity, nevertheless, appears most complex and requires further study. Future studies need to account for the many confounding variables which complicate nurse staffing planning.
Identifiants
pubmed: 32593881
pii: S0020-7489(20)30141-3
doi: 10.1016/j.ijnurstu.2020.103657
pii:
doi:
Types de publication
Journal Article
Observational Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
103657Informations de copyright
Copyright © 2020 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest None