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Comprehensive training in robotic surgery

Brook, Nicholas R.a,b; Dell’Oglio, Paolob,c; Barod, Ravid; Collins, Justinb,e; Mottrie, Alexandreb

doi: 10.1097/MOU.0000000000000566
ROBOTIC SURGERY IN UROLOGY: FACTS AND REALITY: Edited by Alexandre Mottrie and Paolo Dell’Oglio

Purpose of review Robotic training in urology can be poorly structured, lack a basic skills foundation, and may not include teaching in important nontechnical human factor skills vital to the safe delivery of robotic care. Assessment of acquired skills is not routine. There is a need for structured and standardized curricular to deliver validated training and final assessment. The present reviews the current literature on training methods for robotic surgery, and examines the evidence for their effect on performance, where available.

Recent findings There is good evidence for the beneficial effect of dry lab simulators on robotic skills acquisition, but less for cadaveric and animal models. Two urological authorities have developed comprehensive curricula for robotic training that take a novice robotic surgeon through the full stages of robotic skills acquisition. These are in the early stages of development and validation but have stimulated the development of curricula in other specialties.

Summary The future landscape for robotic urology training is likely to include structured, mandated, and centralized training, possibly administered by urological organizations. There will be roles for telementoring, advanced education for robotic trainers, and regular revalidation of expert robotic surgeons.

aThe University of Adelaide – Royal Adelaide Hospital, South Australia

bThe Orsi Academy, Melle, and OLV, Department of Urology, Aalst, Belgium

c Division of Oncology/Unit of Urology, Urological Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy

d Specialist Centre for Kidney Cancer, Royal Free Hospital NHS Trust, London, UK.

eDepartment of Molecular Medicine and Surgery (MMK), Karolinska Institutet, Sweden

Correspondence to Nicholas R. Brook, Department of Urology, The University of Adelaide, Royal Adelaide Hospital, 1 Port Road, Adelaide 5000, South Australia. Tel: +61 8 70740000; e-mail:

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Robotic surgery has improved minimally invasive surgery, shortening the learning curve, and conferring increased dexterity for surgeons [1▪]. The platform is very different from other forms of surgery, and if training is not comprehensive and well structured, patients can be put at risk. An independent review from the ECRI Institute on health technology hazards in 2015 identified insufficient training in robotic surgery as one of the top 10 risks to patients [2]. A recent study of over 1.75 million robotic procedures undertaken between 2000 and 2013 in the United States across different specialties demonstrated 10 624 adverse events (0.6% of the total number of procedures performed). These included 144 deaths (1.4%), 1391 patient injuries (13.1%), and 8061 device malfunctions (75.9%) [3]. Although the robotic platform itself may confer this risk, the computer software and high definition video integral to the robot presents opportunities to facilitate training by allowing data collection on surgical technique and performance [4▪]. As the role of robot-assisted surgery continues to expand, the development of standardized and validated training programs will be increasingly important. Consistent surgical curricula are beneficial in training and help identify surgeons who are not yet sufficiently trained [5,6] and will be a crucial step in the standardization of training, accreditation and certification of surgeons for robotic surgical procedures. Vetter et al. [7▪▪] reported that 35.0% of 177 Ob/Gyn residents had no structured robotics training program at their institution. Barriers to preconsole robotic training were a lack of personal time (56.2%) and availability of the virtual reality simulator or access to the robotic equipment (29.2%).

The present review examines the techniques used for training in robotic surgery in a step-wise manner as they are approached by new surgeons. The role of structured training curricula is reviewed. We also identify the aspects of training that are currently in development and look at the future opportunities for robotic training.

Box 1

Box 1

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The Embase, PubMed, and MEDLINE databases were searched for articles published from 2012 to 2018 relating to training and simulation in robotic surgery, using combinations of the search terms: robotic training, robotic surgical training, robotic simulation, robotic simulator, robotic virtual reality, robotic surgery curriculum. Retrieved articles were reviewed for content and relevance by all authors, prior to drafting of text.

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Simulator training

Simulator exposure is the first stage of robotic technical skills training. Simulators are cheap to run, well tolerated, convenient, and efficient [8], and the exercises they present are perfectly reproducible. Patients are not exposed to surgeons during the early learning curve, and simulators can be used at times when operating rooms and robotic platforms are not scheduled for use with patients. The programs range from basic skill acquisition to full procedural virtual reality. Simulators should demonstrate the following features: face validity – the simulator test measures what it is supposed to; this is generally determined by a group of experts

  1. content validity – the skills tested accurately represent the skills required in robotic surgery
  2. construct validity – the simulated task discriminates between operators of different levels of surgical skill
  3. discriminant validity – the simulator is able to differentiate ability levels within a group with similar experience
  4. concurrent validity – the simulator scores and actual robotic scores are comparable for a similar task
  5. predictive validity – performance on the simulator predicts future performance on the robotic platform when used clinically.

Simulators have their limitations. An excellent review by Moglia et al. [9] covered, in detail, what is known about the validity and transferability of skills learned on robotic simulators. Essentially, there is a lack of consensus on those exercises and skills tasks that are transferable to real cases. Predictive validity is hard to demonstrate as there are many variables in human operating. Using time-to-perform a procedure as a metric of competence is not particularly helpful in the clinical setting, as it can be affected by many variables such as case-complexity and patient comorbidity [8]; complications during a case may not be entirely operator dependent. Also, cognitive and human factors make up much of the package of ‘skill’ displayed during a procedure. Variation in anatomy and pathology are possibly the major contributors to the difficult and long-learning period in all surgery, and simulated tasks and procedures can only lay a very early foundation on which to build clinical experience.

The most prevalent simulator in use is the da Vinci TM Skills Simulator, for use with the Si and Xi systems. This program has 30 different exercises to train and test nine robotic skills. Many of the exercises test more than one skill domain. A list of these is available at Instant feedback is given, with an overall score and a breakdown of performance efficiency in time to complete exercise and movement economy. Deducted from this score are penalty metrics that include collisions between endoscope, instruments and environment, use of excessive force, needle/object drops, incorrect targeting, and placing instruments out-of-view. Users can set up recording of their performance to track improvements, and supervisors can log in to view results. More complex procedure specific simulator programme add-ons are available, including one for radical prostatectomy. These are modular, with detailed reports at the completion of each module.

Standalone simulator systems are available, including the widely used DV Trainer from Mimic, and the ROSS system (see Metrics are similar, and include bimanual dexterity, economy, number of critical errors, safety in operative field, and task time. Weightings are given for these and combined into an overall score, in a similar manner to the da Vinci TM. Other systems are available and have been summarized by Bric et al. [8]. Four of the systems have been validated for both content and construct as effective training tools for robotic surgeons. The same authors reviewed the literature for evidence that simulation improved technical skills for robotic naïve surgeons. The outcomes ranged from robotic naïve surgeons demonstrating nonsignificant improvement on post-simulation testing, to the other end of the spectrum where robotic naïve surgeons were able to perform at the same level as experts when undertaking operative hysterectomy [10]. This latter study is the only one that has shown simulator experience leads to improvements in live robotic surgery, indicating skills transfer, although the fact the findings were so extreme does raise into question the results.

There is clearly an important relationship between the simulator package and how it is used in a curriculum; one that is well constructed in both content and mode of delivery will likely yield the best results from simulation. Bric et al. [8] discuss this and highlight the arbitrary use of limits on the number of attempts at a given task in some curricula. Kang et al. [11] described the learning curve of the ‘tube 2’ task on the dV-Trainer, whereby a mean of 74 attempts was needed to reach a performance plateau. Bric suggests that based on this observation… ‘proficiency-based training with the goal of obtaining expert skill levels benefits from […] unlimited […] attempts during basic skills training curriculum on VR simulators.’

The cost-effectiveness (in 2013) of the ROSS simulator platform was demonstrated by Rehman et al. [12]. By converting the time spent on the simulator to equivalent time spent on animal lab sessions and human radical proctectomies, they demonstrated cost savings of US$78 943.40 for the former and US$623 000 for the latter, taking into account simulator costs. The study does not conclude (and it is probably impossible to calculate) if one hour on the simulator is equivalent to one hour spent on animal or human operating in terms of training value.

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Dry-lab training

Dry-lab training refers to the use of nonhuman, nonanimal, table top models for learning, and skill acquisition. Dry lab tasks are often presented with simulator exercises in robotic surgery courses. In urological robotic simulation, dry labs mostly involve knot tying and veisco-urethral anastomosis practice with tissue-like synthetic material. More recently, advanced models of operative procedures have become available, and these include the use of more complex models for partial nephrectomy [13], and those that incorporate simulated blood supply and bleeding using ex-vivo animal tissue (see

Typically, dry-lab exposure follows simulator experience. Interestingly, there is limited evidence that virtual reality (VR) simulators improve dry-lab skills. Phe et al. [14▪] reported that there was no improvement in the performance of dry-lab skills by either junior or experienced surgeons after a program of VR simulation. Ramos et al. [15] demonstrated that robotic dry lab exercises had face, content, construct, and concurrent validity with the corresponding simulator tasks in a study of novice (zero robotic cases) and experts (mean 200 cases, range 30–2000). They used the Global Evaluative Assessment of Robotic Skills (GEARS) assessment tool [16]. This tool examines seven areas of technical performance (depth perception, bimanual dexterity, efficiency, force sensitivity, autonomy, robotic control, and use of third arm) in robotic surgery and scores on each on a linear score from 1 to 5. This tool has been tested and validated both internally and externally in dry-lab, animal and human work. However, GEARS uses a Likert scale and is a subjective score, with individual interpretation and associated variability. Likert scales correlate poorly with patient outcomes, suggesting that the completion of key procedural steps is a critical element that they do not measure [17]. The strength of assessment of performance by VR simulation software is that objective metrics are reported.

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Wet-lab – cadaveric and live animal model training

Use of cadaveric models and live animals for surgical procedures imparts realism to the training experience before a surgeon starts real cases. It is easy to replicate complex tasks that have direct clinical relevance, but the process is costly and requires the use of dedicated lab facilities. Because of the considerable cost involved, it is usual for there to have been simulator and or dry-lab experience prior to cadaveric animal/human or live animal exposure. There are some situations where the models are not good replicas of the human procedure – an example in radical prostatectomy in the porcine model.

There are a few studies that have reported on robotic procedural performance after programs that have included wet-lab exposure [18,19]. These and others were reviewed by Lovegrove et al. [20] but there is little, if any, firm evidence that specific wet-lab exposure results in improved outcomes. It is likely to have a place, and because of the considerable cost and limitations, may be best reserved for practice of specific complex procedural tasks.

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Modular operative training

A modular approach to operative training breaks down complete procedures into constituent steps. These steps follow the order of the operation, but the trainee need not perform the first step before moving onto second step, and so on. The idea is that different steps require different skill levels, and the programme ensures graduated exposure to progressively more complex steps. The EAU/ERUS educational board have defined a structured modular training framework for robotic-assisted radical prostatectomy, with a specified number of times that each step should be performed (see

Examples are:

  1. Bladder detachment (at least 20 cases)
  2. Endopelvic fascia incision (at least 15 cases)
  3. Bladder neck incision (at least 15 cases)
  4. Section of vasa and preparation of seminal vesicles (at least 15 cases)
  5. Dissection of the posterior plane (at least 10 cases)
  6. Dissection of prostatic pedicles (at least 10 cases)
  7. Dissection of neurovascular bundles (at least 5 cases)
  8. Ligation of the Santorini plexus (at least 10 cases)
  9. Apical dissection (at least 5 cases)

A similar scheme has been proposed by EAU/ERUS for robot-assisted partial nephrectomy, with number of repetitions needed for each step, along with an expert-panel agreed level of complexity for each step (Table 1) [21▪]. The required number of cases may not apply to all individuals, but these figures are based on wide experience of training experts.

Table 1

Table 1

The British Association of Urological Surgeons has produced a curriculum document for modular training, covering robotic radical prostatectomy, pyeloplasty, partial nephrectomy, and cystectomy [22]. This is discussed in more detail below but is mentioned here because of the recognition in the document of the central importance of modular training (Fig. 1).



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Robotic training curricula

The acquisition of expert robotic skills is a lengthy process, which has been poorly structured in the past. For contemporary trainees and surgeons acquiring skills, consideration now needs to be given to a stepwise process of education, which encompasses the totality of robotic training, from lab-based novice to proficiency.

Various curricula have been proposed and are undergoing development; these have been comprehensively reviewed by Fisher et al. [23]. These have been rather limited in their scope; one included only simulator-based training [24], and this was shown to improve performance in simulator based robotic skills. Another both defined steps that should be taught and recommended outcome measures for robotic surgery training [25] based on expert recommendation, but these were not tested. To date, the only validated robotic training curriculum in urology to address all stages of training from lab to ‘expert’ is the EAU/ERUS curriculum. There are other established curricula such as the BAUS curriculum which require validation. If accepted as relevant to robotic training, there may be widespread implementation of these programmes, which may in turn reduce variability in training.

A full training curriculum should start with baseline evaluation to confirm the current understanding and skillsets of the surgeon. Theory and safety aspects are covered, usually as e-learning. The next steps are VR simulation, dry-labs, then cadaveric models and live animal operating. As the surgeon moves towards in vivo human operating, he/she should gain considerable experience at the bedside as an assistant. This allows a full understanding of the procedural steps, and gives an in-depth understanding of patient positioning, robotic platform set-up and intraoperative trouble-shooting. Next, the surgeon should be exposed to operative procedures in a modular manner, being exposed to progressively more complex elements of operations, with close teaching and mentoring (this is possibly facilitated with a dual console system). Assessment of competence should be undertaken, before independent operating. One of the clear advantages of structured comprehensive curricula is the potential to provide continuous assessment and feedback during training. In one study, as many as 50% of residents undergoing clinical robotic training did not have formal assessments [26]. This is clearly an important area to allow identification of strengths and weakness, and to allow dynamic intervention and tailored development. Use of items such as the RARP Assessment Score [20] address this issue with its detailed breakdown of the procedure into multiple steps.

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The EAU/ERUS curriculum

Volpe et al. [27] published the first validated, standardized robotic training curriculum (Fig. 2). This highly structured programme involves observation of cases, simulation dry, and wet lab exposure, followed by a short period of mentored operative experience.



Importantly, the curriculum concludes with a video performance analysis and marking of a full RARP procedure. Test individuals had been previously exposed to bedside assisting and some had limited console experience of robotic surgery. The median number of RARPs they were involved in as console surgeons during modular training was 18 (range 14–36). Following completion of the modular training, surgical mentors evaluated the RARP procedural skills of the individuals using the GEARS score. The mentors also assessed the quality of the surgical skills for each surgical step using a RARP procedure-specific scoring scale ranging from 1 to 5, for which 3 was considered well tolerated.

Videos of the final RARP procedures performed by each fellow were assessed by blinded, expert robotic surgeons using a generic dedicated linear scoring criterion for each procedural step, which ranged from 4 to 16, with 10 considered well tolerated. It is not clear from this study whether individuals had an opportunity to select a case for video analysis. Only 10 individuals were evaluated, and eight of these reached a well tolerated level for the operative steps, with two failing to do so.

This pyramidal form of training, with graduated exposure, may accelerate learning. Although many questions remain about the level at which surgeons can be credentialed, this is an excellent foundation for the development of an acceptable, universal approach to training, and assessment of competence. A similar programme is under development for robotic partial nephrectomy (vide supra). Encouragingly, the ERUS curriculum has been adapted and tested in gynecological [28▪▪] and thoracic [29▪▪] robotic surgery indicating it has utility in, and portability to, other specialties.

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BAUS curriculum

The BAUS curriculum is largely based on the EAU/ERUS curriculum, with five recognized stages: online theoretical training/e-learning, observation of procedure, simulation-based training, a mentorship/fellowship period, and sign-off for independent surgery.

The modular training approach of the BAUS curriculum for each of the four procedures is outlined in Fig. 3. There are suggested numbers of cases (with a large range, which allows for individual variation) and also procedure-specific quality indicators, based on evidence from a systematic review [30] on learning curves in urologic surgery.



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Unmet needs and future developments in robotic training

Training in nontechnical and cognitive skills

Robotic training generally focuses on technical skill acquisition, and there is a scarcity of teaching in the area of nontechnical skills (NTS). NTS include the cognitive and interpersonal skills that augment a surgeon's clinical knowledge and enable effective delivery of surgical care (Table 2). These skills include decision making, leadership, team work, and situation awareness. NTS in open/laparoscopic surgery translate to robotic surgery, but the physical displacement of the surgeon from the patient, assistant, and scrub staff place extra demands on communication. Nontechnical skills and team training can be learnt in simulation training, which replicates common and emergency scenarios in robotic surgery, supporting structured assessments within robotic surgical curricula [31▪▪].

Table 2

Table 2

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Train the trainer courses

These courses teach trainers the skills needed to educate. This is a further opportunity to standardize robotic surgery education by using a ‘top-down’ approach. The English National Training Programme have developed, validated, and implemented an assessment tool for laparoscopic colorectal surgery [32]. This study identified four key areas for trainer assessment: training structure, training behavior, characteristics demonstrated during training, and demonstration of technical and nontechnical skills. Train the trainer (TTT) courses for robotic surgery, based on the work done in laparoscopic surgery, are in development, but there are no publications available at the current time.

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The training process can cease abruptly when the trainee returns to his/her home institution. Traveling proctors can help at this stage of development but may not always be available when the trainee is presented with unfamiliar anatomy or pathology at the time of surgery. There are several telementoring initiatives that connect a remote mentor to the robotic operating room [33]. As an educational tool, this has potential to increase availability of expertise, reduce training costs and confer higher rates of well tolerated procedure adoption. However, there are important medico-legal issues about shared responsibility of care that need to be addressed [34▪▪] and these may be region specific.

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Revalidation and ongoing assessment

Learning and skills acquisition does not cease when competence is achieved; lifelong learning is imperative in surgery. Consideration should be given to regular reassessment / revalidation in robotic surgery. Simulators, dry labs, cadaveric work, video assessment, and telementoring may have a role in revalidation of experienced surgeons, but there are no published data to suggest they have utility in the advanced setting of revalidation for established robotic surgeons.

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Ordered and well organized training in robotics and associated skills is becoming more prevalent, and the need to mold this into structured curricula is increasingly recognized by professional bodies. Several curricula are in development and further work is needed in the validation and implementation of these programmes before robotic training can be further standardized. It would be beneficial if these curricula are developed and implemented in a co-ordinated manner, with co-operation across professional societies, accredited training centers and institutions to reach a consensus on standardized education.

A possible model is that of centralized training institutions providing standardized and comprehensive programmes for robotic training involving theoretical work and advanced simulation facilities for the early stages of learning, with accredited host, or ‘home’ hospitals supporting validated curricula, incorporating modular programmes for trainee surgeons, supported by tele-mentoring services. Training would then be completed with centralized final assessments. A similar model could be used for revalidation of surgeons.

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Financial support and sponsorship

J.C. has received research grants from Intuitive Surgical Inc., and research grants and consultancy fees from Medtronic Inc.

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Conflicts of interest

N.B.: No conflict of interest or relevant source of funding declared.

P.D.: No conflict of interest or relevant source of funding declared.

R.B.: Consultancy fees from Medtronic.

J.C.: Research grants from Intuitive Surgical. Research grants and Consultancy fees from Medtronic.

A.M.: Proctor for Intuitive Surgical and CEO of ORSI Academy.

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Papers of particular interest, published within the annual period of review, have been highlighted as:

  • ▪ of special interest
  • ▪▪ of outstanding interest
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1▪. Choussein S, Srouji SS, Farland LV, et al. Robotic assistance confers ambidexterity to laparoscopic surgeons. J Minim Invasive Gynecol 2018; 25:76–83.

Robot-assisted laparoscopy may eliminate the operative handedness observed in conventional laparoscopy, allowing for virtual ambidexterity. This ergonomic advantage is particularly evident in surgical trainees.

2. ECRI Institute. Top 10 Health Technology Hazards for 2015. A Report from Health Devices. November 2014.
3. Alemzadeh H, Raman J, Leveson N, et al. Adverse events in robotic surgery: a retrospective study of 14 years of FDA data. PLoS One 2016; 11:e0151470.
4▪. Hung AJ, Chen J, Jarc A, et al. Development and validation of objective performance metrics for robot-assisted radical prostatectomy: a pilot study. J Urol 2018; 199:296–304.

Objective metrics were recorded for experts and novices, and compared to GEARS scores. Experts performed better than novices in objective measures, There was poor correlation between GEARS and objective measures. These findings lay the foundation for developing standardized metrics for surgeon training and assessment.

5. Ahmed K, Khan R, Mottrie A, et al. Development of a standardised training curriculum for robotic surgery: a consensus statement from an international multidisciplinary group of experts. BJU Int 2015; 116:93–101.
6. Brunckhorst O, Volpe A, van der Poel H, et al. Training, simulation, the learning curve, and how to reduce complications in urology. Eur Urol Focus 2016; 2:10–18.
7▪▪. Vetter MH, Palettas M, Hade E, et al. Time to consider integration of a formal robotic-assisted surgical training program into obstetrics/gynecology residency curricula. J Robot Surg 2018; 12 3:517–521. doi:10.1007/s11701-017-0775-0.

This study of 177 Ob/Gyn residents found 35% do not have structured robotics training programs. Barriers to training were a lack of personal time (56.2%) and availability of the virtual reality simulator or access to the robotic equipment (29.2%). OB/Gyn residents desire robotics training and are exposed to a wide variety of training modalities. The authors conclude that standardized formal robotics training programs should be incorporated into OB/Gyn residency curricula.

8. Bric JD, Lumbard DC, Frelich MJ, Gould JC. Current state of virtual reality simulation in robotic surgery training: a review. Surg Endosc 2016; 30:2169–2178.
9. Moglia A, Ferrari V, Morelli L, et al. A systematic review of virtual reality simulators for robot-assisted surgery. Eur Urol 2016; 69:1065–1080.
10. Culligan P, Gurshumov E, Lewis C, et al. Predictive validity of a training protocol using a robotic surgery simulator. Female Pelvic Med Reconstr Surg 2014; 20:48–51.
11. Kang SG, Ryu BJ, Yang KS, et al. An effective repetitive training schedule to achieve skill proficiency using a novel robotic virtual reality simulator. J Surg Educ 2015; 72:369–376.
12. Rehman S, Raza SJ, Stegemann AP, et al. Simulation-based robot-assisted surgical training: a health economic evaluation. Int J Surg 2013; 11:841–846.
13. Hung AJ, Ng CK, Patil MB, et al. Validation of a novel robotic-assisted partial nephrectomy surgical training model. BJU Int 2012; 110:870–874.
14▪. Phe V, Cattarino S, Parra J, et al. Outcomes of a virtual-reality simulator-training programme on basic surgical skills in robot-assisted laparoscopic surgery. Int J Med Robotics 2017; 13.

Virtual-reality simulator training had no benefit for subsequent dry lab tasks performance in robotic surgery.

15. Leslie S, Abreu AL, Chopra S, et al. Transvesical robotic simple prostatectomy: initial clinical experience. Eur Urol 2014; 66:321–329.
16. Goh AC, Goldfarb DW, Sander JC, et al. Global evaluative assessment of robotic skills: validation of a clinical assessment tool to measure robotic surgical skills. J Urol 2012; 187:247–252.
17. Scally CP, Varban OA, Carlin AM, et al. Video ratings of surgical skill and late outcomes of bariatric surgery. JAMA Surg 2016; 151:e160428.
18. Moreno Sierra J, Fernandez Perez C, Ortiz Oshiro E, et al. Key areas in the learning curve for robotic urological surgery: a Spanish multicentre survey. Urol Int 2011; 87:64–69.
19. Thompson JE, Egger S, Bohm M, et al. Superior quality of life and improved surgical margins are achievable with robotic radical prostatectomy after a long learning curve: a prospective single-surgeon study of 1552 consecutive cases. Eur Urol 2014; 65:521–531.
20. Lovegrove C, Novara G, Mottrie A, et al. Structured and modular training pathway for robot-assisted radical prostatectomy (RARP): validation of the RARP assessment score and learning curve assessment. Eur Urol 2016; 69:526–535.
21▪. Larcher A, Turri F, Collins J, et al. MP01-17 definition of a structured training curriculum for robot-assisted partial nephrectomy: a Delphi-consensus study from the ERUS Educational Board. J Urol 2018; 199:e9.

This published abstract is the first to describe a structured training programme in robotic partial nephrectomy. It is based on opinion from international experts.

22. BAUS. Robotic Surgery Curriculum - Guidelines for Training.
23. Fisher RA, Dasgupta P, Mottrie A, et al. An over-view of robot assisted surgery curricula and the status of their validation. Int J Surg 2015; 13:115–123.
24. Stegemann AP, Ahmed K, Syed JR, et al. Fundamental skills of robotic surgery: a multiinstitutional randomized controlled trial for validation of a simulation-based curriculum. Urology 2013; 81:767–774.
25. Smith R, Patel V, Satava R. Fundamentals of robotic surgery: a course of basic robotic surgery skills based upon a 14- society consensus template of outcomes measures and curriculum development. Int J Med Robot 2014; 10:379–384.
26. Canvasser NE, Gahan J, Sorokin I. Re: Time to consider integration of a formal robotic-assisted surgical training program. J Robot Surg 2018.
27. Volpe A, Ahmed K, Dasgupta P, et al. Pilot validation study of the european association of urology robotic training curriculum. Eur Urol 2015; 68:292–299.
28▪▪. Rusch P, Kimmig R, Lecuru F, et al. The Society of European Robotic Gynaecological Surgery (SERGS) Pilot Curriculum for robot assisted gynecological surgery. Arch Gynecol Obstetr 2018; 297:415–420.

The Society of European Robotic Gynecological Surgery (SERGS) describe a pilot curriculum for standardized robotic training and assessment of performance, which boosts the learning curve. This is the first nonurological curriculum to be described.

29▪▪. Veronesi G, Dorn P, Dunning J, et al. Outcomes from the Delphi process of the Thoracic Robotic Curriculum Development Committee. Eur J Cardiothorac Surg 2018; 53:1173–1179.

This article reports a Delphi process to define procedures to optimize robotic training of thoracic surgeons. Agreement was reached on a comprehensive standardized curriculum from lab top bedside. This closely follows the EAU/ERUS curriculum and is a further development into a nonurological specialty.

30. Abboudi H, Khan MS, Guru KA, et al. Learning curves for urological procedures: a systematic review. BJU Int 2014; 114:617–629.
31▪▪. Raison N, Wood T, Brunckhorst O, et al. Development and validation of a tool for nontechnical skills evaluation in robotic surgery-the ICARS system. Surg Endosc 2017; 31:5403–5410.

ICARS is the first nontechnical skills behavioral rating system developed for robotic surgery, developed, and tested by expert robotic surgeons. Initial validation in this study shows it is an effective and reliable tool. Implementation of ICARS may support structured training and assessment of NTS within the robotic surgical curriculum.

32. Wyles SM, Miskovic D, Ni Z, et al. Development and implementation of the Structured Training Trainer Assessment Report (STTAR) in the English National Training Programme for laparoscopic colorectal surgery. Surg Endosc 2016; 30:993–1003.
33. Schlachta CM, Nguyen NT, Ponsky T, Dunkin B. Project 6 Summit: SAGES telementoring initiative. Surg Endosc 2016; 30:3665–3672.
34▪▪. Augestad KM, Han H, Paige J, et al. Educational implications for surgical telementoring: a current review with recommendations for future practice, policy, and research. Surg Endosc 2017; 31:3836–3846.

This review describes the critical components of a structured telementoring curriculum, including prerequisites, teaching modalities, and key curricular components.


nontechnical skills; robotic curricula; robotic simulation; robotic surgery; robotic training; surgical training curriculum

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