Type of admission and nursing workload of critical patients: a cross-sectional study.

BACKGROUND

According to the perception of nurses in the intensive care unit (ICU), surgical patients need more nursing care, thus requiring higher nursing workloads for these patients than those admitted as clinical patients. However, some study results on the relationship between the type of admission and the nursing workload are considered contradictory.

AIMS AND OBJECTIVES

To identify if the type of admission (clinical, emergency surgery or elective surgery) is a predictive factor of the nursing workload required by patients on the first day or throughout their stay in the ICU.

DESIGN

This was a quantitative cross-sectional study comprised of a retrospective analysis of clinical records of critical patients.

METHODS

Data were collected from 1 May 2015 to 30 September 2015 in a hospital located in São Paulo, Brazil. Nursing workload was measured using the Nursing Activities Score. The type of admission and the demographic and clinical variables of the patients were investigated. Multiple linear regression was used to identify nursing workload predictive factors, with 5% significance level.

RESULTS

In the analysed sample (n = 211; mean age of 60·3 ± 18·7 years), there was a prevalence of male gender (56·9%). A statistically significant difference (p = 0·025) was found between the type of admission and the nursing workload required for patients on the first ICU day. The Simplified Acute Physiologic Score (p = 0·009) was a predictor of nursing workload on the first day in the ICU, and the Logistic Organ Dysfunction System (p = 0·026) and mortality (p < 0·001) were predictors throughout the ICU stay.

CONCLUSIONS

The type of admission was not a predictive factor of the nursing workload required by critical patients.

RELEVANCE TO CLINICAL PRACTICE

Identifying the predictive factors of nursing workload favours the appropriate staffing of the critical unit by nurses. However, nurses should not consider the type of admission in predicting the nursing workload required by patients in the ICU.