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Robotics in Surgery: A Modular Robotic Platform Driven Gastric Wedge Resection

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Learning Curve for Robotic Colorectal Surgery.

Neng Wei Wong¹, Nan Zun Teo¹, James Chi-Yong Ngu¹

¹Department of Surgery, Changi General Hospital, Singapore 529889, Singapore.

|October 16, 2024

Summary

Understanding the robotic surgery learning curve is crucial for safe adoption. Cumulative sum (CUSUM) analysis helps assess surgical process and patient outcomes, identifying factors for effective training strategies.

Keywords:

colorectal cancer learning curve robotic

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Area of Science:

Surgical Technology
Medical Education
Biostatistics

Background:

Robotic surgery adoption is increasing in clinical practice.
Institutions need to understand the learning curve for safe implementation.
Effective strategies are required to support surgical teams without compromising patient care.

Purpose of the Study:

To analyze the learning curve in robotic surgery.
To identify key variables for assessing surgical performance and patient outcomes.
To discuss factors influencing the robotic surgery learning curve.

Main Methods:

Utilized cumulative sum (CUSUM) analysis, a common statistical method for learning curve analysis.
Classified variables into surgical process measures (e.g., operative time, pathological quality) and patient outcome measures (e.g., postoperative complications).

Main Results:

Learning curves can be analyzed using various statistical methods, with CUSUM analysis frequently cited.
Performance thresholds in learning curve interpretation show heterogeneity.
Factors influencing the learning curve include prior experience, unit maturity, case complexity, simulation, and structured training.

Conclusions:

Understanding the robotic surgery learning curve is essential for developing safe adoption strategies.
Analysis of surgical process and patient outcomes using methods like CUSUM is vital.
Addressing influencing factors through training and support is key for successful robotic surgery implementation.