Factors That Influence Nurse Staffing Levels in Acute Care Hospital Settings

Abstract

Purpose: To identify which patient and hospital characteristics are related to nurse staffing levels in acute care hospital settings.

Design: A cross-sectional design was used for this study.

Methods: The sample comprised 1,004 patients across 10 hospitals in the Andalucian Health Care System (southern Spain) in 2015. The sampling was carried out in a stratified, consecutive manner on the basis of (a) hospital size by geographical location, (b) type of hospital unit, and (c) patients' sex and age group. Random criteria were used to select patients based on their user identification in the electronic health record system. The variables were grouped into two categories, patient and hospital characteristics. Multilevel linear regression models (MLMs) with random intercepts were used. Two models were fitted: the first was the null model, which contained no explanatory variables except the intercepts (fixed and random), and the second (explanatory) model included selected independent variables. Independent variables were allowed to enter the explanatory model if their univariate association with the nurse staffing level in the MLM was significant at p < .05.

Results: Two hierarchical levels were established to control variance (patients and hospital). The model variables explained 63.4% of the variance at level 1 (patients) and 71.8% at level 2 (hospital). Statistically significant factors were the type of hospital unit (p = .002), shift (p < .001), and season (p < .001). None of the variables associated with patient characteristics obtained statistical significance in the model.

Conclusions: Nurse staffing levels were associated with hospital characteristics rather than patient characteristics.

Clinical relevance: This study provides evidence about factors that impact on nurse staffing levels in the settings studied. Further studies should determine the influence of patient characteristics in determining optimal nurse staffing levels.

Keywords: Hospitals; hospital; personnel management; inpatients; multilevel analysis; nursing staff.