|Title||Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit.|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Tawfik DS, Profit J, Lake ET, Liu JB, Sanders LM, Phibbs CS|
|Journal||Health Serv Res|
|Date Published||2020 04|
|Keywords||Adult, California, Female, Humans, Intensive Care Units, Neonatal, Male, Middle Aged, Nurses, Neonatal, Nursing Staff, Hospital, Personnel Staffing and Scheduling, Prospective Studies, Quality of Health Care, Workload|
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 logistic and linear regression.
DATA COLLECTION METHODS: We linked NICU-level nurse staffing and organizational data to patient-level risk factors and outcomes using unique identifiers for NICUs and patients.
PRINCIPAL FINDINGS: An 11-factor prediction model explained 35 percent of the nurse staffing variation among NICUs. Higher-than-predicted nurse staffing was associated with decreased risk-adjusted odds of health care-associated infection (OR: 0.79, 95% CI: 0.63-0.98), but not with length of stay or mortality.
CONCLUSIONS: Organizational and patient factors explain much of the variation in nurse staffing. Higher-than-predicted nurse staffing was associated with fewer infections. Prospective studies are needed to determine causality and to quantify the impact of staffing reforms on health outcomes.
|Alternate Journal||Health Serv Res|
|PubMed Central ID||PMC7080382|
|Grant List||R01 HD083368 / HD / NICHD NIH HHS / United States |
R01 HD084667 / HD / NICHD NIH HHS / United States