Development and Implementation of an Inpatient CAMEO© Staffing Algorithm to Inform Nurse- Patient Assignments in a Pediatric Cardiac Inpatient Unit.
Nurse-patient assignments
Nursing workload
Pediatric acuity
Pediatric cardiology
Journal
Journal of pediatric nursing
ISSN: 1532-8449
Titre abrégé: J Pediatr Nurs
Pays: United States
ID NLM: 8607529
Informations de publication
Date de publication:
Historique:
received:
15
04
2021
revised:
23
07
2021
accepted:
26
07
2021
pubmed:
14
8
2021
medline:
6
10
2021
entrez:
13
8
2021
Statut:
ppublish
Résumé
Nursing workload measurement systems are vital to determine nurse staffing for safe care. The Inpatient Complexity and Assessment and Monitoring to Ensure Optimal Outcomes (CAMEO©) acuity tool provides a standardized language to communicate the acuity and complexity of nursing care in the pediatric inpatient setting. A process improvement project was implemented on a pediatric cardiac inpatient unit to utilize the Inpatient CAMEO© tool to inform nurse-patient assignments. Development of the Inpatient CAMEO© Staffing Algorithm utilized a modified Delphi methodology. Six Delphi rounds were performed for algorithm development, addressing potential implementation barriers, educating nursing staff, piloting feasibility, and final full implementation. The cardiac inpatient unit's charge nurses' algorithm utilization was 86% (n = 12) during the feasibility pilot. The algorithm impacted and changed 28% (n = 4) of the shifts' assignments. One-year post algorithm implementation, CAMEO© documentation rates increased from 25 to 30% to >60%. A retrospective, two-week point-prevalence analysis one-year post-implementation described adherence to the Inpatient CAMEO© Staffing Algorithm for 87% (n = 375) of the nurses' patient assignments. The Inpatient CAMEO© Staffing Algorithm was developed based upon the Inpatient CAMEO© tool and the Inpatient CAMEO© Complexity Classification System to inform nurse-patient assignments and allocate nursing resources. The Inpatient CAMEO© Staffing Algorithm was feasible and sustainable for over one year following implementation at a single center's pediatric cardiac inpatient unit.
Sections du résumé
BACKGROUND
BACKGROUND
Nursing workload measurement systems are vital to determine nurse staffing for safe care. The Inpatient Complexity and Assessment and Monitoring to Ensure Optimal Outcomes (CAMEO©) acuity tool provides a standardized language to communicate the acuity and complexity of nursing care in the pediatric inpatient setting.
DESIGN AND METHODS
METHODS
A process improvement project was implemented on a pediatric cardiac inpatient unit to utilize the Inpatient CAMEO© tool to inform nurse-patient assignments. Development of the Inpatient CAMEO© Staffing Algorithm utilized a modified Delphi methodology. Six Delphi rounds were performed for algorithm development, addressing potential implementation barriers, educating nursing staff, piloting feasibility, and final full implementation.
RESULTS
RESULTS
The cardiac inpatient unit's charge nurses' algorithm utilization was 86% (n = 12) during the feasibility pilot. The algorithm impacted and changed 28% (n = 4) of the shifts' assignments. One-year post algorithm implementation, CAMEO© documentation rates increased from 25 to 30% to >60%. A retrospective, two-week point-prevalence analysis one-year post-implementation described adherence to the Inpatient CAMEO© Staffing Algorithm for 87% (n = 375) of the nurses' patient assignments.
CONCLUSIONS
CONCLUSIONS
The Inpatient CAMEO© Staffing Algorithm was developed based upon the Inpatient CAMEO© tool and the Inpatient CAMEO© Complexity Classification System to inform nurse-patient assignments and allocate nursing resources. The Inpatient CAMEO© Staffing Algorithm was feasible and sustainable for over one year following implementation at a single center's pediatric cardiac inpatient unit.
Identifiants
pubmed: 34388406
pii: S0882-5963(21)00229-3
doi: 10.1016/j.pedn.2021.07.025
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
275-280Informations de copyright
Copyright © 2021. Published by Elsevier Inc.
Déclaration de conflit d'intérêts
Declaration of Competing Interest None.