Improving Intensive Care Unit and Ward Utilization by Adapting Master Surgery Schedules.

Area of Science:

• Healthcare Management
• Operations Research
• Hospital Administration

Background:

• Hospitals face increasing financial and organizational pressures, necessitating thorough surgical treatment planning.
• Effective planning must consider not only operating room resources but also downstream unit capacity, including wards and intensive care units (ICUs).
• The master surgery schedule (MSS) plays a crucial role in resource allocation and patient flow.

Purpose of the Study:

• To analyze the relationship between the master surgery schedule (MSS) and downstream bed demand using an analytical model.
• To develop and apply an algorithm for predicting bed demand based on MSS for surgical subspecialties.
• To evaluate the impact of different MSS designs on ward and ICU bed utilization.

Main Methods:

• Utilized historical clinical data and a patient flow model.
• Applied a novel algorithm to predict bed demand based on MSS.
• Conducted simulations with three distinct MSS scenarios for three surgical subspecialties.
• Validated the algorithm using 12 months of real-world hospital data.

Main Results:

• Simulations demonstrated that optimized MSS can level daily workload and reduce weekend utilization.
• One tested MSS decreased weekend ICU utilization by 20%.
• Another MSS reduced maximum ward bed demand by 7%.
• Algorithm application provided detailed insights into MSS impact on downstream bed needs.

Conclusions:

• The developed algorithm and modeling approach offer valuable tools for hospitals to optimize MSS design.
• Strategic MSS planning can effectively mitigate peaks in bed demand and reduce high weekend occupancy in ICUs and wards.
• This approach supports improved hospital operational efficiency and financial management through better resource allocation.