Proficiency Based Progression Training for Learning Robotic Basic Surgical Skills

Proficiency Based Progression (PBP) training has already demonstrated its scientific added value in different fields of modern medicine. It leads to a reduction of 'performance errors' during surgical or non-surgical tasks. This doctoral thesis deals with the implementation of this new training methodology in teaching basic robotic surgery skills, comparing PBP training with the current standard of care training 'See one, do one, teach one' or 'Apprenticeship model'. By following a specific flowchart, in which the trainee has to demonstrate his 'Proficiency level' in a quantitative way at each step of the flowchart, the ultimate aim is that the learning curve is performed on simulation models and not on living patients. This could potentially lead to a reduction in perioperative morbidity by not exposing patients to untrained surgeons. Central to this methodology are the 'operative metrics'. This is an exact, validated description of the procedure characterization in which the procedural steps are defined ('What to do'), but in which, above all, the possible errors are also defined ('What not to do'). These metrics are used to teach a specific surgical task in a systematic way. They are also used for an objective, binary and quantitative means of performance assessment. Identifying the predefined errors/errors made facilitates retraining and remediation.