Adjusting for patient acuity in measurement of nurse staffing: two approaches

Nurs Res. Mar-Apr 2011;60(2):107-14. doi: 10.1097/NNR.0b013e31820bb0c6.


Background: Researchers who examine the relationship between nurse staffing and quality of care frequently rely on the Medicare case mix index to adjust for patient acuity, even though it was developed originally based on medical diagnoses and may not accurately reflect patients' needs for nursing care.

Objectives: The aim of this study was to examine the differences between unadjusted measures of nurse staffing (registered nurses per 1,000 adjusted patient days) and case mix adjusted nurse staffing and nurse staffing adjusted with nursing intensity weights, which were developed to reflect patients' needs for nursing care.

Method: Secondary data were used from 579 hospitals in 13 states from 2000 to 2006. Included were three measures of nurse staffing and hospital characteristics including ownership, geographic location, teaching status, hospital size, and percent Medicare inpatient days.

Results: Measures of nurse staffing differed in important ways. The differences between the measures were related systematically to ownership, geographic location, teaching status, hospital size, and percentage Medicare inpatient days.

Discussion: Without an accurate method to incorporate acuity into measurement of nurse staffing, research on the relationship between staffing and quality of care will not reach the full potential to inform practice.

Publication types

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

MeSH terms

  • Bias
  • Cross-Sectional Studies
  • Diagnosis-Related Groups / statistics & numerical data
  • Health Facility Size / statistics & numerical data
  • Health Services Needs and Demand
  • Humans
  • Length of Stay / statistics & numerical data
  • Longitudinal Studies
  • Medicare
  • Nursing Administration Research / methods*
  • Nursing Staff, Hospital / supply & distribution*
  • Ownership / statistics & numerical data
  • Personnel Staffing and Scheduling* / statistics & numerical data
  • Quality of Health Care* / statistics & numerical data
  • Regression Analysis
  • Research Design
  • Risk Adjustment / methods*
  • United States
  • Workload / statistics & numerical data