Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit.

ABSTRACT:

Objective

To develop a nurse staffing prediction model and evaluate deviation from predicted nurse staffing as a contributor to patient outcomes.

Data sources

Secondary data collection conducted 2017-2018, using the California Office of Statewide Health Planning and Development and the California Perinatal Quality Care Collaborative databases. We included 276 054 infants born 2008-2016 and cared for in 99 California neonatal intensive care units (NICUs).

Study design

Repeated-measures observational study. We developed a nurse staffing prediction model using machine learning and hierarchical linear regression and then quantified deviation from predicted nurse staffing in relation to health care-associated infections, length of stay, and mortality using hierarchical log

SUBMITTER: Tawfik DS 

PROVIDER: S-EPMC7080382 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature