Identifying Key Predictors of Nursing Workload in Emergency Infusion Rooms: A Decision Tree Approach.

Area of Science:

• Healthcare Management
• Nursing Informatics
• Operations Research

Background:

• Accurate nursing workload assessment is vital for emergency infusion rooms.
• Conventional methods may not capture complex interactions affecting workload.
• Need for interpretable models for staffing and operational decisions.

Purpose of the Study:

• Identify key predictors of nursing workload intensity.
• Develop an interpretable workload stratification framework.
• Utilize a Classification and Regression Tree (CRT) model.

Main Methods:

• Collected daily operational data from an emergency infusion room (July 2023 - August 2025).
• Used chair utilization rate as a workload intensity proxy.
• Developed a CRT model with predictors including infusion duration, care encounters, and patient volume.

Main Results:

• Total infusion duration, direct care encounters, and patient volume were key predictors.
• CRT model provided interpretable workload thresholds and decision rules.
• Model demonstrated stable performance with low cross-validation risk (0.046).

Conclusions:

• CRT model effectively identified workload predictors in emergency infusion rooms.
• CRT offers interpretable rules for staffing and operational decision-making.
• Provides a practical, data-driven framework for workload assessment in infusion care.