Learning Curves in Robotic Surgery: Definition, Measurement, and Clinical Implications

Robotic surgery (RS) is defined as using robots to aid surgeons to perform surgical procedures ⁴. This technique has proved its efficiency in various specialties such as Urology, Gynecology, and General Surgery ⁴. RS is mainly involved in common procedures such as prostatectomy, hysterectomy, and heart valve repair, less common, general surgery operations like hernia repair, cholecystectomy, and colorectal surgeries ⁴. In robot-assisted surgeries, robots act as remote extensions of the surgeon, running on systems like the Da-Vinci Surgical System ⁴,⁵. Compared with the other surgical techniques, RS showed multiple positive results and advantages such as 3D visualization, reduced blood loss, reduced post-operative pain and scarring, accelerating recovery phase and reducing complications and also shorter hospital stay ⁴,⁵. However, disadvantages of this approach stem from limited resources as a result of high cost, and most importantly the steep learning curve of this newly emerging surgical approach in addition to the need for structured educational programs and experience ⁴,⁵.

Learning curve in robotic surgery is a tool used to measure the progression of surgeons in acquiring skills and experience throughout their training and procedures ⁵,¹. As a highly useful tool, it indicates both the effectiveness of training programs and the complexity of procedures ⁵,¹. Generally, it is measured by monitoring progress in some operative variables such as operation time, success rate, post-operative complications, and performance metrics ⁵. Learning curve can be defined in various ways, some experts define learning curve as the time and skills needed to reach a certain threshold, which is the point where surgeons’ performance plateaus, others identify it by the number of procedures required to reach certain proficiency level, and others use statistical tools and analysis to identify it such as CUSUM (cumulative sum) ^{5 1}.

Deep understanding of learning curves is crucial and has a direct significant impact on surgical outcomes ⁵. By having a standard learning curve, there is a structured system by which hospitals can rate their doctors, document efficacy of their training programs and provide the best medical service possible ⁵. As with any new technology or approach, complications and unsatisfactory outcomes are expected. Therefore, this helps hospitals to choose the most suitable surgical team for the procedure according to its risks and complexity ⁵,¹.

Variable methods and metrics are used to measure the learning curve depending on the study or institution preferences ^{5 2}. These metrics include:

4.1 Time-based metrics

Time-based evaluation is the most common method in which learning curve is based on total operation time from setup to finishing the procedure ^{5 1 2}. By time, more experience, skills are acquired reducing operation time and hospital stay. Time-based evaluation can has in itself diverse approaches as some studies had chronological evaluation of learning while proficiency is guaranteed when operation time is decreased gradually then plateaued. Others use moving average method that depends on average of number of procedures’ time. Some studies use CUSUM method which monitors mainly differences between every case performance and the surgeon is considered proficient when these differences are more stabilized ^{1 5} .

4.2 Performance threshold and cutoff points:

Some studies define the learning curve by setting specific thresholds to be reached to guarantee proficiency. These thresholds might be a certain number of cases, achieving certain skill determined by an expert, reduction in complication rates or even meeting an outcome parameter ^{2 5}.

4.3 Graphical and statistical analysis

Turning the learning curve into graphs makes it easier to interpret and identify stages of learning into (initial phase/ progression/ plateau or competency point) ^{1 2 5} .

Beyond graph inspection, it can be expressed through statistics by CUSUM charts or risk-adjusted CUSUM. As previously explained, CUSUM charts focus mainly on differences between cases and these differences are added cumulatively creating the chart. Therefore, when the surgeon is improving chart will show upward slope and when performance stabilizes, it gives negative slope (no changes). Risk-adjusted CUSUM is almost the same yet patient specific risk factors should be considered, which makes analysis more accurate in reflecting surgeon’s true performance ^{1 2 5} .

4.4 Multidimensional methods

This method incorporates multiple indicators, but it is less used due to its complexity.

4.5 Simulation and assessments

Some studies use pre-operative simulation or lab performance as a tool to define learning curve, but it is also not frequently used ^{2 5} .

The variability in thresholds and metrics represents the main obstacle in the route of having a standard learning curve measurement approach which impacts the accuracy and generalization of findings. Also, the interchanging between terms like expertise, proficiency and competency which complicates the identification of actual robotic surgery experts. In addition, the complexity of cases such as risk factors and complicated cases has a significant impact in surgeon's performance leading to inaccurate measurement.

For better medical service and surgical outcomes, many studies are conducted to identify the most effective way to shorten learning curve and develop surgical skills faster. It can be achieved through a combination of structured training and educational programs as it typically follows a continuous and cumulative flow starting with basic foundational knowledge followed by simulation exercises for skills acquisition and then starting live surgeries observation and how to deal with emergencies and finally start to operate by themselves ^{5 1}. Simulation based training such as virtual reality (VR) simulators are highly involved in these programs as it provides a stress- and risk-free environment for surgeons to be able to learn effectively and develop their skills ^{5 1}. Therefore, this integrative approach ensures a smooth transition to a competent surgeon ¹.