Integrated Scheduling and Capacity Planning with Considerations for Patients' Length-of-Stays.

Area of Science:

• Operations Research
• Healthcare Management
• Health Systems Engineering

Background:

• Current hospital scheduling and capacity planning often isolate different units, leading to suboptimal resource allocation.
• Existing models lack integration of multi-unit capacity usage and patient flow dynamics.

Purpose of the Study:

• To introduce the first dynamic multi-day scheduling model integrating capacity usage across multiple hospital locations.
• To analyze the impact of patient length-of-stay and downstream census on scheduling decisions.
• To demonstrate the economic benefits of integrated versus localized hospital scheduling.

Main Methods:

• Developed a dynamic multi-day scheduling model incorporating multi-location capacity usage.
• Utilized a variable transformation to analyze the optimal number of patients in the system.
• Conducted numerical experiments using realistic hospital data.

Main Results:

• The optimal number of patients is positively related to system state and downstream capacity.
• Increasing capacity at one location shows diminishing returns independently.
• Integrated scheduling decisions demonstrated substantial value, reducing expenses significantly.

Conclusions:

• Integrated scheduling and capacity planning across hospital units is crucial for cost-efficiency.
• Localized decision-making can lead to substantially higher operational expenses (up to 60%).
• The proposed dynamic model offers a novel approach to optimizing hospital resource management.