Data-Driven Nurse Staffing in the Neonatal Intensive Care Unit. Issue 5 (September 2022) - Feldman, Keith; Rohan, Annie J.
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. 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, nurse factors, and unit factors influencing preshift NICU staffing is presented, along with how intrashift variability of these and other factors must be accounted for to maintain effective and efficient assignments. Potential sources of data to predict and enhance nurse staffing in the NICU are discussed.