ABSTRACT
BACKGROUND: Investigators have used a variety of operational definitions of nursing hours of care in measuring nurse staffing for health services research. However, little is known about which approach is best for nurse staffing measurement. OBJECTIVE: To examine whether various nursing hours measures yield different model estimations when predicting patient outcomes and to determine the best method to measure nurse staffing based on the model estimations. DATA SOURCES/SETTING: We analyzed data from the University HealthSystem Consortium for 2005. The sample comprised 208 hospital-quarter observations from 54 hospitals, representing information on 971 adult-care units and about 1 million inpatient discharges. METHODS: We compared regression models using different combinations of staffing measures based on productive/nonproductive and direct-care/indirect-care hours. Akaike Information Criterion and Bayesian Information Criterion were used in the assessment of staffing measure performance. RESULTS: The models that included the staffing measure calculated from productive hours by direct-care providers were best, in general. However, the Akaike Information Criterion and Bayesian Information Criterion differences between models were small, indicating that distinguishing nonproductive and indirect-care hours from productive direct-care hours does not substantially affect the approximation of the relationship between nurse staffing and patient outcomes. CONCLUSIONS: This study is the first to explicitly evaluate various measures of nurse staffing. Productive hours by direct-care providers are the strongest measure related to patient outcomes and thus should be preferred in research on nurse staffing and patient outcomes.
Subject(s)
Nursing Staff, Hospital/organization & administration , Nursing Staff, Hospital/statistics & numerical data , Personnel Staffing and Scheduling/organization & administration , Personnel Staffing and Scheduling/statistics & numerical data , Bayes Theorem , Health Services Research , Hospital Administration , Humans , Outcome Assessment, Health Care , Regression AnalysisABSTRACT
High patient turnover (patient throughput generated by admissions, discharges, and transfers) contributes to increased demands and resources for care. We examined how the relationship between registered nurse (RN) staffing and failure-to-rescue (FTR) varied with patient turnover levels by analyzing quarterly data from the University HealthSystem Consortium. The data included 42 hospitals, representing 759 nursing units and about 1 million inpatients. Higher RN staffing was associated with lower FTR. When patient turnover increased from 48.6% to 60.7% on nonintensive units (non-ICUs), the beneficial effect of non-ICU RN staffing on FTR was reduced by 11.5%. RN staffing should be adjusted according to patient turnover because turnover increases patient care demand beyond that presented by patient count, and outcomes may be adversely affected.
