ArticleHealth services research2020
Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit.
Article in Health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- Artificial intelligence-driven decision support for patients with acute respiratory failure: a scoping review.Intensive care medicine experimental · 2025Review
- Association of Shift-Level Organizational Factors with Nosocomial Infection in the Neonatal Intensive Care Unit.Journal of pediatrics. Clinical practice · 2024Article
- Effect of the postoperative pain management model on the psychological status and quality of life of patients in the advanced intensive care unit.BMC nursing · 2024Article
- Is nurse staffing associated with critical deterioration events on acute and critical care pediatric wards? A literature review.European journal of pediatrics · 2023Review
- Characteristics of High Versus Low-Performing Hospitals for Very Preterm Infant Morbidity and Mortality.The journal of pediatrics: X · 2023Article
- Distinguishing High-Performing From Low-Performing Hospitals for Severe Maternal Morbidity: A Focus on Quality and Equity.Obstetrics and gynecology · 2022Article
- Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.Journal of medical Internet research · 2021Review
- Racial and Economic Neighborhood Segregation, Site of Delivery, and Morbidity and Mortality in Neonates Born Very Preterm.The Journal of pediatrics · 2021Article
- Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit.Health services research · 2020Article
- Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveTo 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
designRepeated-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
methodsWe linked NICU-level nurse staffing and organizational data to patient-level risk factors and outcomes using unique identifiers for NICUs and patients. PRINCIPAL
findingsAn 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.
conclusionsOrganizational 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.
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