Optimizing Robotic Fleet Size and Composition for Operating Room Assistance

Overview

This study presents a simulation-based analysis to determine the optimal size and composition of mobile robotic fleets in operating room (OR) wings. Using real clinical workflow data from laparoscopic cholecystectomies and a detailed OR wing model, the research evaluates how robotic assistance can balance cost, space, and performance to support surgical teams effectively.

Background

Healthcare systems worldwide face critical personnel shortages, particularly in operating room wings where qualified nurses and assistants are scarce. The COVID-19 pandemic has further strained surgical resources, necessitating innovative solutions. Mobile robotic assistance systems offer promise by autonomously performing repetitive or physically demanding tasks, potentially improving work ergonomics and alleviating staff workload. The AURORA project aims to develop robotic circulating nurses to operate in non-sterile OR areas, with a focus on fleet management to optimize workload distribution and operational efficiency.

Data Highlights

The simulation environment was modeled after a German university hospital's OR wing, including eight operating rooms and associated storage and equipment locations. Task data were collected from 20 laparoscopic cholecystectomy procedures, capturing preparation, intraoperative, and postoperative tasks with precise timing metrics (request, start, and end times). The simulation software, built on ROS and Python, integrates the OR layout, surgical workflows, and robotic fleet behavior to evaluate performance across different fleet sizes and compositions.

Key Findings

• Mobile robotic fleets can effectively support circulating nurse tasks in the OR wing, potentially matching or exceeding human-only performance.
• Optimal fleet size balances workload capacity with constraints on space and operational costs, avoiding excessive resource allocation.
• Fleet composition, including robot capabilities and driving speeds, critically influences task completion times and overall efficiency.
• Simulation based on real clinical workflows provides more accurate insights compared to prior studies using generic tasks or non-OR environments.
• Robotic assistance can reduce the physical burden on nursing staff by automating transport of heavy objects and repetitive material collection.
• Context-dependent management of robotic resources enables dynamic allocation to areas of highest demand within the OR wing.

Clinical Implications

Implementing optimally sized and composed robotic fleets in OR wings can enhance surgical workflow efficiency and reduce nursing workload, addressing critical personnel shortages. Careful consideration of robot capabilities and fleet management strategies is essential to ensure safety, hygiene, and space requirements are met without compromising performance. This approach may improve job attractiveness and patient throughput in surgical clinics.

Conclusion

This simulation-based study demonstrates that tailored mobile robotic fleets can robustly support circulating nurse functions in OR wings, balancing cost, space, and performance. These findings provide a foundation for clinical translation of robotic assistance to improve surgical workflow and mitigate healthcare staffing challenges.

References

1. Jeon et al. 2020 -- Simulation Study of Hospital Delivery Robots
2. AURORA Project Documentation
3. Clinical Workflow Analysis of Laparoscopic Cholecystectomies