Proposing Theoretical Models to Optimize Nursing Workflows during Pandemics

Abstract

Background: Pandemics pose unprecedented challenges to healthcare systems, particularly in nursing workflows, which are critical for ensuring efficient patient care and safety. The sudden surge in patient volume, resource constraints, and dynamic care protocols amplify workflow inefficiencies, leading to burnout among nurses and compromised care quality. Despite these challenges, limited research focuses on theoretical models to optimize nursing workflows during pandemics.

Aim: This paper aims to propose comprehensive theoretical models that address inefficiencies in nursing workflows, enhance adaptability, and improve both patient and staff outcomes during pandemics.

Methods: A mixed-methods approach was utilized, including a systematic literature review, analysis of case studies from recent pandemics, and the conceptual development of workflow optimization models. Key insights were derived from nursing practices during COVID-19, SARS, and H1N1 pandemics to identify common barriers and potential interventions.

Results: Four theoretical models were developed: the Adaptive Workflow Framework (AWF), designed for scalability and flexibility; the Integrated Communication Model (ICM), which streamlines information flow among teams; the Resource Allocation Optimization Model (RAOM), leveraging predictive analytics for efficient resource distribution; and the Emotional Resilience and Support Framework (ERSF), addressing psychological well-being and resilience among nursing staff. These models provide a structured approach to mitigate common workflow challenges, including communication breakdowns, resource shortages, and staff burnout.

Conclusion: The proposed models offer innovative, adaptable solutions to enhance nursing workflows during pandemics. By integrating these frameworks, healthcare institutions can improve operational efficiency, ensure equitable resource allocation, and promote staff resilience, ultimately enhancing patient care quality. Future research should focus on empirical validation and scaling these models across diverse healthcare settings.