Factors associated with nursing workload in three intensive care units

How do nurses manage their work under time pressure? Occurrence of implicit rationing of nursing care in the intensive care unit: A qualitative study

OBJECTIVES

The aim of this study was to explore the experience of intensive care unit nurses under time pressure and the occurrence of implicit rationing under time pressure.

METHODS

In-depth audio-recorded interviews were conducted with 18 intensive care unit nurses. Colaizzi seven-step analysis of phenomenological data was used.

FINDINGS

Three themes emerged from the analysis: the influence of internal and external environments, perceived differences in time pressure, and broad coping styles.

CONCLUSIONS

Under the influence of various factors, including continuous or intermittent time pressure, nurses employ strategies to deal with the pressure. Sometimes, these strategies allow them to complete all their necessary work. However, with the increase in time pressure, sometimes some work that must be done is changed into work that should be done in the consciousness of nurses. In such cases, nurses choose the strategy of implicit rationing to deal with time pressure.

Impact of Wound Dressing Changes on Nursing Workload in an Intensive Care Unit

The objective of this study is to understand how the type of wound dressing changes (routine or frequent) in patients admitted to intensive care units influences nurses' workload. This study used a database of retrospective and analytical observational study from one Portuguese intensive care unit. The sample included 728 adult patients admitted between 2015 and 2019. The nursing workload was assessed by the TISS-28 scale, both at admission and at discharge. The linear regression results show that patients with frequent dressing changes are associated with a higher nursing workload, both at admission (Coef. 1.65; 95% CI [0.53; 2.77]) and discharge (Coef. 1.27; 95% CI [0.32; 2.22]). In addition, age influences the nursing workload; older people are associated with a higher nursing workload (at admission Coef. 0.07; 95% CI [0.04; 0.10]; at discharge Coef. 0.08; 95% CI [0.05; 0.10]). Additionally, an increase in nursing workload at admission would significantly increase the nursing workload at discharge (Coef. 0.27; 95% CI [0.21; 0.33]). The relative stability of the nursing workload over the studied years is also another important finding (the influence of studied years is non-significant). In conclusion, patients with frequent dressing changes presented higher TISS-28 scores when compared with patients with an exchange of routine dressings, which leads to a higher nursing workload.

Carga laboral de profesionales de enfermería en Unidad de Cuidado Intensivo según Nursing Activities Score

Highlights: La NAS es una herramienta que toma relevancia al momento de evaluar la carga de trabajo de los profesionales de enfermería. Se evidencia que el personal de enfermería está expuesto a altas cargas de trabajo que, en la mayoría de casos, supera lo sugerido por Consejos y Asociaciones de enfermería. Algunos factores que pueden afectar la carga de trabajo son el tipo de turnos de trabajo, sexo del profesional, tipo de UCI, el número de pacientes a cargo, entre otros. Es necesario evaluar la implementación de la NAS en distintos tipos de servicios para entender la situación y trabajo actual de los profesionales de enfermería. Introducción: Nursing Activities Score ha sido utilizada como un instrumento principalmente en la Unidad de Cuidados Intensivos para medir las actividades de enfermería, siendo esta la unidad que maneja pacientes de mayor complejidad para el cuidado. Objetivo: establecer la carga de trabajo, evaluada por Nursing Activities Score, y factores relacionados a la misma en Unidades de Cuidado Intensivo. Metodología: Revisión cualitativa tipo scoping Review, utilizando el método PRISMA. Búsqueda en las bases de datos CINAHL, LILACS, SCOPUS, SCIENCE DIRECT, SCIELO y PUBMED. Resultados: La muestra final se compone de 87 textos, que van desde el año 2007 hasta 2021. Se clasificaron en cinco categorías: Carga de trabajo en UCI, comparación entre unidades, carga de trabajo relacionada al personal de enfermería, carga de trabajo relacionada a las características de los pacientes y consecuencias de la carga de trabajo. Discusión: La revisión revela una carga de trabajo mayor al 50% en la mayoría de los estudios, esto debido a diferentes factores: principalmente las características particulares de los pacientes, no se observó una diferencia significativa entre unidades generales y especializadas, las cargas de trabajo elevadas suponen un factor de riesgo para la ocurrencia de eventos adversos. Conclusiones: Los resultados de esta revisión permiten evidenciar que el personal de enfermería está expuesto constantemente a altas cargas de trabajo. Esta carga de trabajo puede verse influenciada o influenciar diversos factores, como lo son las características de los pacientes a quienes se brinda atención o puede afectar positiva o negativamente la calidad de la atención de enfermería. Como citar este artículo: Cruz López Laura Nathalie, Quintero Osorio Jenny Fernanda, Melo Roa Juan David, Henao Castaño Ángela María. Carga laboral de profesionales de enfermería en Unidad de Cuidado Intensivo según Nursing Activities Score. Revista Cuidarte. 2023;14(1):e2680. http://dx.doi.org/10.15649/cuidarte.2680

Ambient intelligence-based monitoring of staff and patient activity in the intensive care unit

BACKGROUND

Caregiver workload in the ICU setting is difficult to numerically quantify. Ambient Intelligence utilises computer vision-guided neural networks to continuously monitor multiple datapoints in video feeds, has become increasingly efficient at automatically tracking various aspects of human movement.

OBJECTIVES

To assess the feasibility of using Ambient Intelligence to track and quantify allpatient and caregiver activity within a bedspace over the course of an ICU admission and also to establish patient specific factors, and environmental factors such as time ofday, that might contribute to an increased workload in ICU workers.

METHODS

5000 images were manually annotated and then used to train You Only LookOnce (YOLOv4), an open-source computer vision algorithm. Comparison of patientmotion and caregiver activity was then performed between these patients.

RESULTS

The algorithm was deployed on 14 patients comprising 1762800 framesof new, untrained data. There was a strong correlation between the number ofcaregivers in the room and the standardized movement of the patient (p < 0.0001) withmore caregivers associated with more movement. There was a significant difference incaregiver activity throughout the day (p < 0.05), HDU vs. ICU status (p < 0.05), delirious vs. non delirious patients (p < 0.05), and intubated vs. not intubated patients(p < 0.05). Caregiver activity was lowest between 0400 and 0800 (average .71 ± .026caregivers per hour) with statistically significant differences in activity compared to 0800-2400 (p < 0.05). Caregiver activity was highest between 1200 and 1600 (1.02 ± .031 caregivers per hour) with a statistically significant difference in activity comparedto activity from 1600 to 0800 (p < 0.05). The three most dominant predictors of workeractivity were patient motion (Standardized Dominance 78.6%), Mechanical Ventilation(Standardized Dominance 7.9%) and Delirium (Standardized Dominance 6.2%).

CONCLUSION

Ambient Intelligence could potentially be used to derive a single standardized metricthat could be applied to patients to illustrate their overall workload. This could be usedto predict workflow demands for better staff deployment, monitoring of caregiver workload, and potentially as a tool to predict burnout.

Nursing Activities Score at Discharge from the Intensive Care Unit Is Associated with Unplanned Readmission to the Intensive Care Unit

This study evaluated the accuracy of predicting unplanned the intensive care unit (ICU) readmission using the Nursing Activities Score (NAS) at ICU discharge based on nursing workloads, and compared it to the accuracy of the prediction made using the Stability and Workload Index for Transfer (SWIFT) score. Patients admitted to the ICU of Sapporo Medical University Hospital between April 2014 and December 2017 were included, and unplanned ICU readmissions were retrospectively evaluated using the SWIFT score and the NAS. Patient characteristics, such as age, sex, the Charlson Comorbidity Index, and sequential organ failure assessment score at ICU admission, were used as covariates, and logistic regression analysis was performed to calculate the odds ratios for the SWIFT score and NAS. Among 599 patients, 58 (9.7%) were unexpectedly readmitted to the ICU. The area under the receiver operating characteristic curve of NAS (0.78) was higher than that of the SWIFT score (0.68), and cutoff values were 21 for the SWIFT and 53 for the NAS. Multivariate analysis showed that the NAS was an independent predictor of unplanned ICU readmission. The NAS was superior to the SWIFT in predicting unplanned ICU readmission. NAS may be an adjunctive tool to predict unplanned ICU readmission.