Developing emergency department physician shift schedules optimized to meet patient demand

Area of Science:

• Emergency Medicine
• Operations Research
• Healthcare Management

Background:

• Emergency departments (EDs) face challenges in managing patient flow due to fluctuating arrival rates.
• Current physician scheduling may not optimally align with real-time patient demand, leading to inefficiencies.
• Accurate temporal analysis of patient arrivals is crucial for effective resource allocation.

Purpose of the Study:

• To analyze historical patient arrival patterns in an ED to inform optimal shift scheduling for acute care and fast-track areas.
• To evaluate an optimization planning model for aligning physician productivity with patient arrivals.
• To improve ED operational efficiency through data-driven physician scheduling.

Main Methods:

• Statistical analysis of historical ED patient arrival data across weekdays, weekends, and holidays.
• Development of Poisson-based generalized additive models to predict patient arrival rates throughout the day.
• Utilization of a mathematical programming model to generate optimized ED physician shift schedules.

Main Results:

• Patient arrival patterns varied between acute care and fast-track clinics, with distinct weekday/weekend differences in the fast-track area.
• The optimization planning model demonstrated significant reductions in unmet patient demand across examined scenarios.
• Specific reductions in unmet demand were 19%, 39%, and 69% for the tested scheduling scenarios.

Conclusions:

• The developed planning model effectively improved ED shift schedules.
• Aligning physician productivity with patient arrival rates is key to enhancing ED operational efficiency.
• This approach offers a data-driven strategy for optimizing healthcare resource allocation in emergency settings.