Data-Driven Nurse Staffing in the Neonatal Intensive Care Unit.
Document Type
Article
Publication Date
9-2022
Identifier
DOI: 10.1097/NMC.0000000000000839
Abstract
ABSTRACT: The challenge of nurse staffing is amplified in the acute care neonatal intensive care unit (NICU) setting, where a wide range of highly variable factors affect staffing. A comprehensive overview of infant factors (severity, intensity), nurse factors (education, experience, preferences, team dynamics), and unit factors (structure, layout, shift length, care model) influencing pre-shift NICU staffing is presented, along with how intra-shift variability of these and other factors must be accounted for to maintain effective and efficient assignments. There is opportunity to improve workload estimations and acuity measures for pre-shift staffing using technology and predictive analytics. Nurse staffing decisions affected by intra-shift factor variability can be enhanced using novel care models that decentralize decision-making. Improving NICU staffing requires a deliberate, systematic, data-driven approach, with commitment from nurses, resources from the management team, and an institutional culture prioritizing patient safety.
Journal Title
MCN. The American journal of maternal child nursing
Volume
47
Issue
5
First Page
249
Last Page
264
MeSH Keywords
Humans; Infant, Newborn; Intensive Care Units, Neonatal; Nursing Staff, Hospital; Personnel Staffing and Scheduling; Workforce; Workload
Keywords
Neonatal Intensive Care Units; Hospital Nursing Staff; Personnel Staffing and Scheduling; Workforce; Workload
Recommended Citation
Feldman K, Rohan AJ. Data-Driven Nurse Staffing in the Neonatal Intensive Care Unit. MCN Am J Matern Child Nurs. 2022;47(5):249-264. doi:10.1097/NMC.0000000000000839
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