338 Chapter 13 certain pre-defined standard.34 Recent studies into this method of training have shown it is possible to implement this method of training in novice surgeons courses and in train the trainer courses.34–37 The addition of a proficiency-based training aspect to a multi-step training program should ensure that all trained surgeons meet the same quality standards and are able to perform the same tasks at the end of their training curriculum. This proficiency-based multi-step training program could be the start of a lifelong learning program for RAS surgeons. The multi-step training program described above is based on the Dreyfus Five-Stage Model of Adult Skills Acquisition.38 It contains the novice (basic RAS training) and advanced beginner (advanced RAS training) stages. In order to reach the proficient, expert and master stages further training should be provided in the form of a fellowship or as a lifelong learning program as described by Jones et al.39,40 With the introduction of new RAS systems this format of training could also be the basis for the training curricula for these new robots assisted surgery systems.41 How can the performance of robotic surgeon’s best be assessed? A challenge for the implementation of lifelong learning programs is the assessment of experts. Where surgical skills of novice surgeons are commonly assessed using virtual reality simulators, experts’ surgical skills assessment is commonly based on the analysis of surgical videos or postoperative patient outcome analysis. Although postoperative patient outcome analysis gives a crude overview of the outcomes of the surgeries performed by the surgeon, there are a lot of confounders to consider in this type of analysis. The postoperative patient outcomes are not entirely determined by skills of the surgeons since patient characteristics such as prostate weight, age of the patient and the BMI of the patient are known to influence the postoperative outcome.15 Analysis of the intracorporal video does give insight into the technical or surgical skills of a surgeon. However, most assessment methods are highly subjective since they are observer based templated assessments (i.e., GEARS12, GERT13, and PACE14). Furthermore, the structural assessment of skills using observer based templated assessments methods is impractical due to the level of training needed for accurate assessments. Novel assessment methods need to be developed to allow for objective and consistent assessment of expert’s surgeons’ skills. The group of Hung et al has made the first steps in using Artificial Intelligence (AI) as a novel method of surgical skills analysis.42–44 Their research shows that it is possible to identify differences in skill levels among surgeons and correlate these results to postoperative outcome.42,44 Which is a
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