Evaluating multiple performance measures across several dimensions at a multi-facility outpatient center.

Area of Science:

• Healthcare Management
• Operations Research
• Simulation Modeling

Background:

• Healthcare delivery systems face increasing pressure for efficiency.
• Simulation is used to analyze complex hospital systems, but lacks guidelines for multi-measure analysis.
• Analyzing performance across multiple dimensions (e.g., day, patient type) is crucial for identifying operational issues.

Purpose of the Study:

• To address the challenge of multiple responses in healthcare simulation experiments.
• To develop a stratification framework and evaluation construct for comparing outpatient systems.
• To enable managers to analyze performance across multiple dimensions.

Main Methods:

• Developed a stratification framework and evaluation construct for simulation analysis.
• Applied the approach to a discrete-event simulation model of a large-scale oncology center.
• Evaluated operational performance changes due to scheduling, process flow, and resource level improvements.

Main Results:

• Demonstrated a reduction in patient wait times.
• Showed a decrease in resource overtime.
• Achieved improvements across multiple patient classes, facilities, and days of the week.

Conclusions:

• The developed framework effectively analyzes multi-dimensional performance in healthcare simulations.
• The approach provides critical insights for operational problem-solving in clinics.
• Implementation of recommendations led to system-wide performance improvements at the oncology center.