Modeling hospital-acquired pressure ulcer prevalence on medical-surgical units: nurse workload, expertise, and clinical processes of care

Health Serv Res. 2015 Apr;50(2):351-73. doi: 10.1111/1475-6773.12244. Epub 2014 Oct 7.

Abstract

Objective: This study modeled the predictive power of unit/patient characteristics, nurse workload, nurse expertise, and hospital-acquired pressure ulcer (HAPU) preventive clinical processes of care on unit-level prevalence of HAPUs.

Data sources: Seven hundred and eighty-nine medical-surgical units (215 hospitals) in 2009.

Study design: Using unit-level data, HAPUs were modeled with Poisson regression with zero-inflation (due to low prevalence of HAPUs) with significant covariates as predictors.

Data collection/extraction methods: Hospitals submitted data on NQF endorsed ongoing performance measures to CALNOC registry.

Principal findings: Fewer HAPUs were predicted by a combination of unit/patient characteristics (shorter length of stay, fewer patients at-risk, fewer male patients), RN workload (more hours of care, greater patient [bed] turnover), RN expertise (more years of experience, fewer contract staff hours), and processes of care (more risk assessment completed).

Conclusions: Unit/patient characteristics were potent HAPU predictors yet generally are not modifiable. RN workload, nurse expertise, and processes of care (risk assessment/interventions) are significant predictors that can be addressed to reduce HAPU. Support strategies may be needed for units where experienced full-time nurses are not available for HAPU prevention. Further research is warranted to test these finding in the context of higher HAPU prevalence.

Keywords: Nursing; acute inpatient care; modeling; quality of care/patient safety (measurement).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Clinical Protocols
  • Female
  • Hospital Administration / statistics & numerical data*
  • Humans
  • Male
  • Middle Aged
  • Nursing Staff, Hospital / statistics & numerical data*
  • Personnel Staffing and Scheduling / statistics & numerical data
  • Pressure Ulcer / epidemiology*
  • Prevalence
  • Quality Indicators, Health Care
  • Quality of Health Care / statistics & numerical data*
  • Risk Assessment
  • Workload / statistics & numerical data*