What Is the Learning Curve in Robotic General Surgery?

Luise I Pernar, MD¹, Faith Robertson², Eric G Sheu, MD, PhD¹, Ali Tavakkoli¹, David C Brooks¹, Douglas S Smink¹. ¹Brigham and Women’s Hospital, ²Harvard Medical School

Introduction: Robotic-assisted surgery is used with increasing frequency in general surgery for applications as varied as foregut, pancreatic, and hindgut surgery. In spite of this increase in usage, the technology remains novel, and the learning curve is not yet defined. The purpose of this study was to review the literature on the learning curve in robotic general surgery to inform adopters of this technology.

Methods and Procedures: A PubMed search was performed using the terms ‘learning curve’, ‘robotic’, and ‘surgery’. This returned 647 abstracts for manuscripts published between March 1999 and July 2015. Full-length articles for abstracts meeting inclusion criteria (written in English, reporting original work, focus on general surgery operations, and with explicit statistical methods), were reviewed in detail.  In total, 25 articles were reviewed for potential inclusion in analysis; one was excluded after review as it did not focus on a specific procedure.

Results: The 24 remaining articles included colorectal (10 articles, 42%), foregut or bariatric (8, 33%), solid organ (4, 17%), and biliary (2, 8%) surgery. Fifteen of 24 (63%) articles report single-surgeon experiences; the study samples varied from 32 (biliary surgery) to 200 (pancreatic surgery) cases. Cumulative sum method was the most frequent statistical method employed to determine the learning curve (17, 71%). Time was used as a variable to examine the learning curve in all studies (100%); outcomes were included in 9 studies (38%) to help delineate the learning curve. In 9 studies (38%) the authors identified three phases of the learning curve. Numbers of cases needed to achieve plateau performance were wide-ranging and ranges were similar for different kinds of operations: 25-75 cases for colorectal, 10-95 for foregut or bariatric, and 10-80 for solid organ surgery.

Conclusions: Although robotic surgery is increasingly utilized in general surgery, the literature provides few guidelines on the learning curve for adoption.  Although the sample of articles is heterogeneous, time is the most common proxy for the learning curve. The number of cases needed to achieve plateau performance varies by case type and the learning curve may have multiple phases as surgeons add more complex cases to their case mix with experience. The available literature lacks a uniform assessment of outcomes and complications, which would arguably reflect expertise in a more meaningful way than time to perform the operation alone.