Whilst live animal, cadaveric and animal tissue models have all been used in robotic surgical training, validation has been limited. Tissue models have been developed for both RARP and RAPN using porcine specimens. Animal models (porcine and chicken) are used as part of the EAU robotic training curriculum however specific data on their effectiveness has not been published. Likewise cadaveric models have undergone initial assessment showing face, content and construct validity however data is limited by low participant numbers.

In 1936, T.P Wright described the practical application of learning curve theory to aeronautical manufacturing in the USA [10]. By comparing trainees’ learning with productivity he identified a relationship with reduced production cost.

In surgery, historically the assumption has been that training-time and larger caseloads translate to improved surgical skill, eventually reaching a level of “competence” with improved outcomes. At this point the learning curve plateaus, indicating consistency in surgical practice [4]. The aim of improved training techniques, is to bring forward this point of plateau in the learning curve. Knowing when trainees can be expected to operate with the necessary level of proficiency is important for both planning surgical education programmes and for maintaining patient safety. Different parameters can be used in learning curve analysis. These can include surgical variables, patient variables or variables measured in a simulated environment (Table 65.1).

Through analysis of learning curves for a procedure or the sub-steps within an operation it becomes possible to subsequently plot the stage of a surgeon in their training. Trainees can be compared to experts, demonstrating construct validity for the learning curves. Consequently these can indicate when proficiency has been attained that the trainee is likely to be safe to move from operating in a simulated environment to operating on a patient in theatre. Furthermore this may allow accreditation at a given skill level to be awarded. It is also possible to utilise learning curves to indicate the minimum training hours or experience required prior to undertaking specific procedures.

Stolzenburg first proposed the use of modular training to shorten the learning curve associated with urological procedures [8]. This involves a defined sequence whereby surgeons progress through training in surgical steps requiring increasing levels of technical skill. A modular approach to training enables surgeons to participate in operations up to the level at which they are proficient with their seniors intervening for the more challenging steps where they are yet to attain competence.

Training pathways have been formulated incorporating this modular design. The European Association of Urology (EAU) Robotic Urology Section (ERUS) developed a robotic curriculum incorporating a modular design for robot assisted radical prostatectomy (RARP) [9]. This begins with theory-based training through online e-learning modules followed by virtual reality, dry-lab and wet-lab simulator experience. After establishing the foundations of training, a week of intensive lab-based training is undertaken including use of a dual console to facilitate mentoring of surgeons while operating in real-time. Trainees’ technical and non-technical skills are assessed at baseline and after 28, 35 and 180 days to assess progression along the procedural learning curve. Finally, fellows submit a recorded case of RARP that they have performed independently. It is assessed by blinded reviewers using the Global Evaluative Assessment of Robotic Skills (GEARS) score and a generic dedicated scoring system. Successful completion of the curriculum results in certification as an ERUS Robotic Fellow.