Queue management for the assignment of anesthesia clinician breaks to increase fractions completed before the end of surgical closure and during planned time windows

Authors: Dexter F et al.

Journal of Clinical Anesthesia 110 (March 2026):112136

Summary

This operations-science study evaluates how anesthesia directors should assign 30-minute lunch breaks in surgical suites with long workdays. Using discrete-event simulations based on 15 years of real-world data (5,481 days, 53 operating rooms, 460,354 cases), the authors compared different queue-management strategies to determine which method maximizes the percentage of breaks completed:

• During active surgical time (i.e., not during closure or turnover)

• Within planned 2-hour windows (e.g., 11:00 AM–1:00 PM)

Two primary prioritization strategies were tested:

1. Assign breaks to cases with the least predicted time remaining in surgery (provided the surgery is expected to last beyond the 30-minute break).

2. Assign breaks to cases that have been ongoing the longest.

They also compared two structural models:

• Individual clinician assigned to a set of near-adjacent rooms (sequential, non-pooled)

• Single, pooled queue across rooms within a surgical suite

Key findings:

• Prioritizing cases with the least predicted time remaining resulted in 16.5% fewer successfully completed breaks than prioritizing the longest ongoing cases.
• Using a single pooled queue increased completed breaks by 7.2% overall.
• The combination of pooled queue + prioritizing longest ongoing cases produced 28.4% more completed breaks compared with sequential room assignments prioritizing shortest remaining time (95% CI 27.7–29.1%).
• Results were robust across different time windows (e.g., 11–1 vs other 2-hour windows).
• The mechanism relates to variability: because surgical times follow log-normal distributions with high variability, predicted time remaining is unreliable. When variability is high, shortest-predicted-time strategies increase incomplete breaks.
• In a hypothetical scenario where every case had identical duration, both strategies would perform similarly.

Operationally, the recommended workflow is:

1. Assign each break-giving clinician to a first room.

2. While those breaks are occurring, select the next rooms based on the longest ongoing cases (within a pooled suite-wide system).

What You Should Know

1. Break success depends on variability. In high-variability OR environments (i.e., real life), predicted remaining time is unreliable for decision-making.

2. Longest-case-first works better. The longest ongoing case strategy is more stable and yields more completed 30-minute breaks.

3. Pooling beats silos. A single queue for a suite outperforms individual room clusters.

4. Operational science applies directly to anesthesia staffing. This is a practical use of simulation modeling that can immediately improve efficiency and clinician well-being.

5. The effect size is meaningful. A 28% improvement in completed breaks is operationally large, especially in high-volume academic centers.

Key Points

• Prioritizing the longest ongoing case increases break completion rates.

• Using a pooled queue across rooms improves efficiency.

• High variability in surgical durations makes predicted time-left strategies inferior.

• Combined pooled + longest-case strategy produced 28% more completed breaks.

Thank you to the Journal of Clinical Anesthesia for allowing us to summarize and share this important work on applying operations science to anesthesia workflow and clinician well-being.