The association of shift-level nurse staffing with adverse patient events

J Nurs Adm. 2011 Feb;41(2):64-70. doi: 10.1097/NNA.0b013e31820594bf.

Abstract

Objective: The objective of this study was to demonstrate the association between nurse staffing and adverse events at the shift level.

Background: Despite a growing body of research linking nurse staffing and patient outcomes, the relationship of staffing to patient falls and medication errors remains equivocal, possibly due to dependence on aggregated data.

Methods: Thirteen military hospitals participated in creating a longitudinal nursing outcomes database to monitor nurse staffing, patient falls and medication errors, and other outcomes. Unit types were analyzed separately to stratify patient and nurse staffing characteristics. Bayesian hierarchical logistic regression modeling was used to examine associations between staffing and adverse events.

Results: RN skill mix, total nursing care hours, and experience, measured by a proxy variable, were associated with shift-level adverse events.

Conclusions: Consideration must be given to nurse staffing and experience levels on every shift.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Accidental Falls / prevention & control
  • Accidental Falls / statistics & numerical data*
  • Bayes Theorem
  • Databases, Factual
  • Hospitals, Military
  • Humans
  • Logistic Models
  • Longitudinal Studies
  • Medication Errors / nursing
  • Medication Errors / prevention & control
  • Medication Errors / statistics & numerical data*
  • Military Nursing
  • Multivariate Analysis
  • Nurse Administrators
  • Nursing Administration Research
  • Nursing Staff, Hospital / education
  • Nursing Staff, Hospital / supply & distribution*
  • Outcome Assessment, Health Care
  • Personnel Staffing and Scheduling / organization & administration*
  • Quality Indicators, Health Care / organization & administration
  • Risk Management / statistics & numerical data
  • United States / epidemiology
  • Workforce
  • Workload / statistics & numerical data
  • Wounds and Injuries / epidemiology
  • Wounds and Injuries / etiology