How experienced robotic nurses adapt to the Hugo™ RAS system

Area of Science:

• Robotic Surgery
• Surgical Nursing
• Healthcare Systems Engineering

Background:

• The Medtronic Hugo™ Robotic-Assisted Surgery (RAS) system is a novel platform.
• Limited data exists on its team-level impact and adaptation by surgical staff.
• Understanding nurse team work patterns is crucial for efficient RAS implementation.

Purpose of the Study:

• To describe the work patterns and learning curves of an experienced robotic nurse team adapting to the Medtronic Hugo™ RAS.
• To analyze the impact of the new system on preoperative, perioperative, and postoperative phases.
• To compare operative times with the established Davinci® system.

Main Methods:

• Prospective data collection on nurse tasks during the first 30 procedures using the Hugo™ RAS.
• Descriptive analysis of recorded data, including Gantt charts for work pattern visualization.
• Comparison of operative times between the Hugo™ RAS and Davinci® systems.

Main Results:

• Preoperative phase showed improvement (median 94 min), with work patterns stabilizing after 20 surgeries.
• Perioperative (median 170 min) and postoperative (median 26 min) phases showed no significant improvement.
• Nurse teams experienced long hours and few breaks due to limited training on the Hugo™ RAS.

Conclusions:

• The Medtronic Hugo™ RAS system requires adaptation, with nurse work patterns stabilizing around 20 cases.
• While preoperative tasks improved, perioperative and postoperative efficiency did not, indicating areas for optimization.
• Addressing nurse workload and training is essential for successful integration of new robotic systems like the Hugo™ RAS.