Article

Nurse Dose: Linking Staffing Variables to Adverse Patient Outcomes

School of Nursing, University of Michigan, Ann Arbor, MI 48109, USA.
Nursing research (Impact Factor: 1.36). 06/2011; 60(4):214-20. DOI: 10.1097/NNR.0b013e31822228dc
Source: PubMed

ABSTRACT

Inconsistent findings in more than 100 studies have made it difficult to explain how variation in nurse staffing affects patient outcomes. Nurse dose, defined as the level of nurses required to provide patient care in hospital settings, draws on variables used in staffing studies to describe the influence of many staffing variables on outcomes.
The aim of this study was to examine the construct validity of nurse dose by determining its association with methicillin-resistant Staphylococcus aureus (MRSA) infections and reported patient falls on a sample of inpatient adult acute care units.
Staffing data came from 26 units in Ontario, Canada, and Michigan. Financial and human resource data were data sources for staffing variables. Sources of data for MRSA came from infection control departments. Incident reports were the data source for patient falls. Data analysis consisted of bivariate correlations and Poisson regression.
Bivariate correlations revealed that nurse dose attributes (active ingredient and intensity) were associated significantly with both outcomes. Active ingredient (education, experience, skill mix) and intensity (full-time employees, registered nurse [RN]:patient ratio, RN hours per patient day) were significant predictors of MRSA. Coefficients for both attributes were negative and almost identical. Both attributes were significant predictors of reported patient falls, and coefficients were again negative, but coefficient sizes differed.
By conceptualizing nurse and staffing variables (education, experience, skill mix, full-time employees, RN:patient ratio, RN hours per patient day) as attributes of nurse dose and by including these in the same analysis, it is possible to determine their relative influence on MRSA infections and reported patient falls.

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