Objective: To develop queuing and simulation-based models to understand the relationship between ICU bed availability and operating room schedule to maximize the use of critical care resources and minimize case cancellation while providing equity to patients and surgeons.
Design: Retrospective analysis of 6-month unit admission data from a cohort of cardiothoracic surgical patients, to create queuing and simulation-based models of ICU bed flow. Three different admission policies (current admission policy, shortest-processing-time policy, and a dynamic policy) were then analyzed using simulation models, representing 10 yr worth of potential admissions. Important output data consisted of the “average waiting time,” a proxy for unit efficiency, and the “maximum waiting time,” a surrogate for patient equity.
Setting: A cardiothoracic surgical ICU in a tertiary center in New York, NY.
Patients: Six hundred thirty consecutive cardiothoracic surgical patients admitted to the cardiothoracic surgical ICU.
Measurements and Main Results: Although the shortest-processing-time admission policy performs best in terms of unit efficiency (0.4612 days), it did so at expense of patient equity prolonging surgical waiting time by as much as 21 days. The current policy gives the greatest equity but causes inefficiency in unit bed-flow (0.5033 days). The dynamic policy performs at a level (0.4997 days) 8.3% below that of the shortest-processing-time in average waiting time; however, it balances this with greater patient equity (maximum waiting time could be shortened by 4 days compared to the current policy).
Conclusions: Queuing theory and computer simulation can be used to model case flow through a cardiothoracic operating room and ICU. A dynamic admission policy that looks at current waiting time and expected ICU length of stay allows for increased equity between patients with only minimum losses of efficiency. This dynamic admission policy would seem to be a superior in maximizing case-flow. These results may be generalized to other surgical ICUs.
1 Department of Operations and Supply Chain Management, University of St. Thomas, Saint Paul, MN.
2 Department of Operations, Business Analytics, and Information Systems, University of Cincinnati, Cincinnati, OH.
3 Departments of Anesthesiology, Mount Sinai School of Medicine, New York, NY.
4 Cardiothoracic Surgery, Mount Sinai School of Medicine, New York, NY.
*See also p. 662.
The authors have not disclosed any potential conflicts of interest.
For information regarding this article, E-mail: email@example.com