Development and Implementation of an Inpatient CAMEO© Staffing Algorithm to Inform Nurse- Patient Assignments in a Pediatric Cardiac Inpatient Unit

J Pediatr Nurs. 2021 Sep-Oct:60:275-280. doi: 10.1016/j.pedn.2021.07.025. Epub 2021 Aug 11.

Abstract

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: 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: 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: 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.

Keywords: Nurse-patient assignments; Nursing workload; Pediatric acuity; Pediatric cardiology.

MeSH terms

  • Algorithms
  • Child
  • Humans
  • Inpatients*
  • Nurse-Patient Relations
  • Nursing Staff, Hospital*
  • Personnel Staffing and Scheduling
  • Retrospective Studies
  • Workforce
  • Workload