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Updated: Nov 11, 2025

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

Simulation-based evaluation of operating room management policies.

Jan Schoenfelder^1,2, Sebastian Kohl^3,4, Manuel Glaser^3,4

¹Chair of Health Care Operations/Health Information Management, Faculty of Business and Economics, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany. jan.schoenfelder@uni-a.de.

BMC Health Services Research

|March 25, 2021

Summary

Optimizing operating room (OR) management through simulation reveals trade-offs in patient wait times and surgery deferrals. Implementing parallel anesthesia induction can increase patient throughput but may require additional staff to mitigate deferrals.

Keywords:

Capacity management Operating room management Patient flow Patient scheduling Simulation

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Area of Science:

Healthcare Operations Research
Hospital Management Systems
Surgical Process Optimization

Background:

Operating rooms (ORs) are critical hospital resources, impacting efficiency and revenue.
High surgical admission rates (60-70%) and inflexible staffing necessitate advanced OR management tools.
Complex OR processes require continuous evaluation and decision support systems.

Purpose of the Study:

To evaluate various operating room management policies using simulation.
To assess the impact of different policies on key performance indicators at a university hospital.
To provide data-driven insights for improving OR efficiency and patient flow.

Main Methods:

A detailed simulation model was developed based on 12,946 surgeries.
Evaluated policies included parallel anesthesia induction, dedicated emergency rooms, extended OR hours, and elective patient sequencing.
Performance metrics included patient waiting time, idle time, staff overtime, and surgery deferrals.

Main Results:

Extending OR hours created trade-offs between wait times for different patient urgency levels.
Parallel anesthesia induction increased patient scheduling but also potential deferrals, mitigated by extra staff.
Altering elective patient sequencing based on duration impacted multiple performance indicators.

Conclusions:

Simulation allows testing OR management changes without workflow disruption.
Collaborative design of simulation tools yielded immediate improvements in scheduling and data collection.
The approach empowers OR managers with a risk-free method for process optimization.