Observational studyJournal of advanced nursing2026
Shift-Specific Patterns of Nursing Workloads in the Emergency Department: AI Powered Analysis.
Observational study in Journal of advanced nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
aimTo identify and differentiate workload patterns across shifts and to provide evidence for optimizing nursing workforce allocation in emergency departments:
designA cross-sectional study.
methodsReal 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.
resultsThe 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.
conclusionShift-specific nursing workloads in emergency departments can be effectively identified using multidimensional, real-world nursing activity data. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: 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. IMPACT: 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. REPORTING
methodThis study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patients or members of the public were involved in the design, conduct, analysis, or reporting of this study.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.