DATA DRIVEN NURSE STAFFING IN THE NEONATAL INTENSIVE CARE UNIT

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.

Automated summary

The challenge of nurse staffing in the acute care neonatal intensive care unit is influenced by various factors at different levels. The factors include infant factors, nurse factors, and unit factors. Variability of these factors within shifts must be considered for effective staffing decisions. Technology and predictive analytics can be used to improve workload estimations and acuity measures. Decentralized decision-making and innovative care models can enhance nurse staffing decisions affected by intra-shift variability. Improving NICU staffing requires a deliberate approach, commitment from nurses, resources from management, and a patient safety-focused institutional culture.