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Queuing theory accurately models the need for critical care resources
Michael L McManus1, Michael C Long, Abbot Cooper
1Department of Anesthesia, Pain and Perioperative Medicine, Children's Hospital, Boston, Massachusetts, USA. michael.mcmanus@childrens.harvard.edu
Anesthesiology
|April 29, 2004
Summary
Queuing theory accurately models intensive care unit (ICU) patient flow, predicting bed needs. This helps hospital administrators optimize scarce resources and avoid patient turn-away rates when demand is random.
Area of Science:
- Healthcare management
- Operations research
- Critical care medicine
Background:
- Hospital resource allocation is a growing challenge.
- Intensive care units (ICUs) are critical but costly.
- Evaluating ICU bed capacity models is essential.
Purpose of the Study:
- To prospectively evaluate a mathematical model for ICU bed capacity.
- To assess the accuracy of queuing theory in predicting ICU patient flow and resource needs.
Main Methods:
- Collected 2 years of ICU admission, discharge, and turn-away data.
- Developed a queuing theory model of patient flow.
- Compared model predictions to observed unit performance and analyzed sensitivity to bed availability.
Main Results:
- The queuing model accurately predicted ICU turn-away rates (correlation=0.89).
- Turn-away rates increased exponentially above 80-85% utilization.
- Small changes in bed availability drastically impacted system performance.
Conclusions:
- Queuing theory accurately determines ICU bed supply for random patient arrivals.
- Planners may underestimate ICU resource needs due to stochastic patient flow.
- This model aids in optimizing ICU resource allocation.
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