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Shift-Specific Patterns of Nursing Workloads in the Emergency Department: AI Powered Analysis.

Younhee Kang¹, Hyunggon Park², Inkyung Park¹

¹College of Nursing, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea.

Journal of Advanced Nursing

|May 30, 2026

Summary

Emergency department nursing workload varies significantly by shift. Objective data analysis can identify these patterns, informing better staffing and improved patient care efficiency.

Keywords:

SHAP emergency department explainable artificial intelligence machine learning nursing workloads shift work

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Area of Science:

Nursing informatics
Healthcare management
Operations research

Background:

Emergency departments (EDs) experience fluctuating patient volumes and acuity.
Accurate assessment of nursing workload across different shifts is crucial for effective staffing.
Existing methods for workload assessment often lack objectivity and real-time data integration.

Purpose of the Study:

To identify and differentiate workload patterns among day, evening, and night shifts in an ED.
To provide objective evidence for optimizing nursing workforce allocation in ED settings.
To explore the utility of multidimensional, real-world activity data in characterizing nursing workload.

Main Methods:

A cross-sectional study utilizing real-time data from an ED in Seoul, South Korea.
Smartphones, beacons, and smartwatches collected data on nursing time, physical activity, and location transitions over 238 shifts.
An eXtreme Gradient Boosting model classified shifts, with Shapely Additive exPlanations identifying key workload drivers.

Main Results:

The developed model accurately distinguished between day, evening, and night shifts.
Key workload indicators, including admissions, discharges, patient assignments, and nursing time (direct and indirect), varied significantly across shifts.
Location transition patterns among nurses remained relatively consistent regardless of the shift.

Conclusions:

Multidimensional, real-world nursing activity data can effectively identify and differentiate shift-specific workload patterns in EDs.
Findings support the development of data-driven staffing strategies to enhance ED efficiency and care quality.
This study provides objective evidence to inform workforce management decisions for emergency nursing.