A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use

Area of Science:

• Healthcare Operations Research
• Health Systems Engineering
• Critical Care Medicine

Background:

• High demand for intensive care unit (ICU) services strains hospital capacity, leading to admission delays.
• Effective capacity planning requires accounting for patient acuity and optimizing bed assignment policies.
• Simulation modeling offers a robust approach to evaluate ICU bed management strategies.

Purpose of the Study:

• To establish a data-driven framework for modeling patient flow within ICUs.
• To identify key performance indicators for ICU operations, such as wait times and occupancy.
• To analyze the impact of various ICU bed allocation scenarios on patient flow outcomes.

Main Methods:

• Developed a queuing model using retrospective data from 2,710 adult patients over 12 months.
• Simulated patient flow through 36 ICU and 15 step-down unit (SDU) beds, incorporating patient acuity and transfer times.
• Evaluated scenarios including varied unit sizes, reserved beds for acute patients, and reduced transfer times.

Main Results:

• Current allocation resulted in an average ICU wait time of 1.13 hours.
• Reallocating SDU beds to ICU reduced wait times by 7.2% and increased occupancy to 84%.
• Optimizing transfer times to one hour significantly decreased ICU wait times, equivalent to adding 1-2 ICU beds.

Conclusions:

• Queuing and simulation modeling with empirical data effectively evaluate ICU bed assignment changes.
• Strategies like reallocating beds or improving transfer efficiency can reduce patient wait times and enhance occupancy.
• The study highlights trade-offs between prioritizing acute patients and expanding overall ICU capacity.