Association between patient classification systems and nurse staffing costs in intensive care units: An exploratory study

Intensive Crit Care Nurs. 2018 Apr;45:78-84. doi: 10.1016/j.iccn.2018.01.007.

Abstract

Objectives: Nurse staffing costs represent approximately 60% of total intensive care unit costs. In order to analyse resource allocation in intensive care, we examined the association between nurse staffing costs and two patient classification systems: the nursing activities score (NAS) and nine equivalents of nursing manpower use score (NEMS).

Research methodology/design: A retrospective descriptive correlational analysis of nurse staffing costs and data of 6390 patients extracted from a data warehouse.

Setting: Three intensive care units in a university hospital and one in a regional hospital in Norway.

Main outcome measures: Nurse staffing costs, NAS and NEMS.

Results: For merged data from all units, the NAS was more strongly correlated with monthly nurse staffing costs than was the NEMS. On separate analyses of each ICU, correlations were present for the NAS on basic costs and external overtime costs but were not significant. The annual mean nurse staffing cost for 1% of NAS was 20.9-23.1 euros in the units, which was comparable to 53.3-81.5 euros for 1 NEMS point.

Conclusion: A significant association was found between monthly costs, NAS, and NEMS. Cost of care should be based on individual patients' nursing care needs. The NAS makes nurses' workload visible and may be a helpful classification system in future planning and budgeting of intensive care resources.

Keywords: Hospital costs; Intensive care units; NAS; NEMS; Nine equivalents of nursing manpower use score; Nursing activities score; Nursing staff; Workload.

MeSH terms

  • Adult
  • Costs and Cost Analysis
  • Female
  • Follow-Up Studies
  • Humans
  • Intensive Care Units* / economics
  • Intensive Care Units* / statistics & numerical data
  • Male
  • Middle Aged
  • Norway
  • Nurses / economics*
  • Nurses / statistics & numerical data
  • Personnel Staffing and Scheduling / economics*
  • Retrospective Studies
  • Workforce