Intensive care unit nursing workload estimation in smart hospitals

Abstract

Purpose

The objectives of the proposed model are: aiding nursing staff in documentation tasks, which can be onerous and stressful; and helping management by offering an estimate of the nursing workload, which can be considered for administrative purposes, such as staff scheduling.

Design/methodology/approach

An exploratory-descriptive study was conducted in order to identify, investigate, and describe the problem of documenting nursing activities and workload estimation in an intensive care unit. Technological solutions were explored, and models were proposed to address these issues.

Findings

Cross-dataset experiments were performed, and the model was able to offer an adequate estimate of the nursing workload. The results suggest that continuous retraining is essential for maintaining high accuracy. While the proposed model was considered in the context of an adult ICU, it can be adapted to other contexts, such as elderly care.

Research limitations/implications

While the proposed solution seems promising, further research is required, such as deploying this system in an ICU and facing challenges in the areas of computer security, medical ethics, and patient data privacy. More patients’ variables could also be collected to improve the workload estimates.

Originality/value

Nursing workload assessment is critical to improve the cost-benefit ratio in health care, offer high-quality patient care, and reduce unnecessary expenses, and this process is usually manual. An automated device can automatically document the amount of time spent in patient care activities in a more transparent, efficient, and accurate manner, freeing staff for more urgent activities and keeping management better informed about day-to-day nursing operations.

Citation

Nolio Santa Cruz, R., Vaz Sampaio, H., Becker Westphall, C., Dutra de Camargo, M. and Couto Carvalho Barra, D. (2024), "Intensive care unit nursing workload estimation in smart hospitals", Journal of Health Organization and Management, Vol. 38 No. 8, pp. 1146-1162. https://doi.org/10.1108/JHOM-01-2024-0019