Intensive care unit nursing workload estimation in smart hospitals.

Area of Science:

• Healthcare Informatics
• Nursing Management
• Clinical Workflow Optimization

Background:

• Nursing documentation and workload estimation are often manual, time-consuming, and stressful for staff.
• Accurate workload assessment is vital for healthcare cost-benefit ratios, quality patient care, and efficient resource allocation.
• Current manual processes can lead to inefficiencies and may not fully inform management decisions.

Purpose of the Study:

• To develop a model that aids nursing staff with documentation tasks.
• To provide management with an estimate of nursing workload for administrative purposes, including staff scheduling.
• To explore technological solutions for improving nursing documentation and workload estimation.

Main Methods:

• An exploratory-descriptive study was conducted to investigate the challenges in nursing documentation and workload estimation within an intensive care unit (ICU).
• Technological solutions and predictive models were explored and proposed to address identified issues.
• Cross-dataset experiments were performed to evaluate the model's performance.

Main Results:

• The proposed model demonstrated an adequate ability to estimate nursing workload.
• Continuous retraining of the model is essential for maintaining high accuracy in workload estimation.
• The model, initially developed for adult ICUs, shows potential for adaptation to other healthcare settings, such as elderly care.

Conclusions:

• The automated system offers a more transparent, efficient, and accurate method for documenting patient care time compared to manual processes.
• Further research is needed, including real-world ICU deployment, to address computer security, medical ethics, and patient data privacy concerns.
• Incorporating additional patient variables could further enhance the accuracy of workload estimates.