title: Comparison of Nurse Staffing Measurements in Staffing-Outcomes Research.
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.
  
  
  METHODS
  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.
authors.affiliation: *National Database of Nursing Quality Indicators, School of Nursing, University of Kansas Medical Center, Kansas City, KS †Department of Community Health Systems, School of Nursing ‡Philip R. Lee Institute for Health Policy Studies §Department of Social and Behavioral Sciences, School of Nursing ∥Department of Community Health Systems, School of Nursing, University of California, San Francisco ¶Catalyst Systems, LLC, Novato, CA.
authors.forename: Shin Hye
authors.initials: SH
authors.lastname: Park
authors.forename: Mary A
authors.initials: MA
authors.lastname: Blegen
authors.forename: Joanne
authors.initials: J
authors.lastname: Spetz
authors.forename: Susan A
authors.initials: SA
authors.lastname: Chapman
authors.forename: Holly A
authors.initials: HA
authors.lastname: De Groot
mesh_terms: D001499:Bayes Theorem; D006302:Health Services Research; D006739:Hospital Administration; D006801:Humans; D009741:Nursing Staff, Hospital / Q000458:organization & administration* / Q000706:statistics & numerical data*; D017063:Outcome Assessment, Health Care; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration* / Q000706:statistics & numerical data*; D012044:Regression Analysis