Conectar
To identify and differentiate workload patterns across shifts and to provide evidence for optimizing nursing workforce allocation in emergency departments:
A cross-sectional study.
Real time data were collected from an emergency department in a general hospital in Seoul, South Korea, between October 30, 2023 to October 24, 2024. Smartphones, beacons, and smartwatches were used to capture nursing time, physical activity, work-related characteristics, and location transitions across 238 shifts. A multiclass eXtreme Gradient Boosting model was developed and evaluated to classify working shifts (day, evening, night). Shapely Additive exPlanations were applied to identify key contributing features, and shift-specific differences were examined using analysis of variance with post hoc tests.
The model demonstrated strong performance in distinguishing shifts. Key features included the number of admissions, discharges, assigned patients, and both direct and indirect nursing time, all of which varied across shifts. In contrast, location transition patterns were relatively consistent.
Shift-specific nursing workloads in emergency departments can be effectively identified using multidimensional, real-world nursing activity data.
Findings support the development of staffing strategies that account for variation in workload across shifts, with potential to improve efficiency and maintain quality of care.
This study addresses the lack of objective evidence for shift-specific workload differences in emergency nursing. It demonstrates that multidimensional activity data can distinguish workload patterns across shifts. The findings may inform staffing decisions for emergency department nurses and support improvements in workforce management and patient care.
This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.
No patients or members of the public were involved in the design, conduct, analysis, or reporting of this study.
FreshRSS 