Identifying curriculum content for a cross-specialty robotic-assisted surgery training program: a Delphi study

Transfer of skills between laparoscopic and robot-assisted surgery: a systematic review

BACKGROUND

Robot-assisted surgery is today well-implemented in many surgical specialties, but requires another skill set than laparoscopy. Most often, robot-assisted surgery is considered add-on to laparoscopic skills but very little is known about the transfer of skills. The aim of the study was to examine to what extent surgical skills are transferable between laparoscopic and robot-assisted surgery.

METHODS

A systematic search was conducted in three databases (Ovid Medline, Embase, and Web of Science). Studies investigating transfer of skills between laparoscopy and robot-assisted surgery in either a phantom-based, simulation-based, animal model, or clinical setting were eligible for inclusion. Quality assessment was done using the Medical education research study quality instrument and educational New Ottawa Scale.

RESULTS

Of 15,610 studies identified, 89 studies continued to full-text reading, and 37 studies were included. Four studies were found non-comparable and were left out of the results for the primary outcome. All 33 studies explored transfer from laparoscopy to robot-assisted surgery and 17 found a positive transfer whereas 15 did not. Only 11 studies explored transfer from robot-assisted surgery to laparoscopy, of which only three found a positive transfer.

CONCLUSION

An almost equal number of publications found a positive transfer and no transfer from laparoscopic to robot-assisted surgery. Fewer studies explored the transfer from robot-assisted surgery to laparoscopy. Very little evidence supports that surgeons trained solely in robot-assisted surgery can perform laparoscopy. This must be considered in future training programs as robot-assisted surgery is expected to become the first-in-line modality for many future surgeons.

The importance of robotic surgery training in HPB fellowship: a survey of the 2022 AHPBA fellows

BACKGROUND

The 2022 incoming fellows' expectations for their robotics training, as well as their perceptions of the utility of the surgical robot, are not well defined.

METHODS

Cross-sectional survey of 24 AHPBA fellows in 2022, analyzed with descriptive statistics and Spearman's rho.

RESULTS

Of 33 current AHPBA fellows, 22 completed the survey (66.7%). Study participants had limited-to-moderate experience with robotics prior to fellowship (mean 2.5 ± SD 1.1; range 1-4). Most participants agreed that robotics influenced their fellowship choice (mean 4.14 ± SD 0.87; range 1-5), would make then more marketable (mean 4.77 ± SD 0.52; range 1-5) and improve job prospects (mean 4.68 ± SD 0.87; range 1-5). Of the study participants, 55% responded that robotics training is "essential" in fellowship, while 64% responded that it is "essential" for their careers. Fellows were only slightly satisfied with overall robotics training within their respective programs (mean 3.44 ± SD 1.17; range 1-5) The majority (73.7%) expect that robotics will comprise <25% of their training. Notably, the majority (75%) have no formal robotics training curriculum.

DISCUSSION

This survey identifies potential gaps where robotics training could be improved for future incoming AHPBA fellows.

Utilising an accelerated Delphi process to develop consensus on the requirement and components of a pre-procedural core robotic surgery curriculum

Robot-assisted surgery (RAS) continues to grow globally. Despite this, in the UK and Ireland, it is estimated that over 70% of surgical trainees across all specialities have no access to robot-assisted surgical training (RAST). This study aimed to provide educational stakeholders guidance on a pre-procedural core robotic surgery curriculum (PPCRC) from the perspective of the end user; the surgical trainee. The study was conducted in four Phases: P1: a steering group was formed to review current literature and summarise the evidence, P2: Pan-Specialty Trainee Panel Virtual Classroom Discussion, P3: Accelerated Delphi Process and P4: Formulation of Recommendations. Forty-three surgeons in training representing all surgical specialties and training levels contributed to the three round Delphi process. Additions to the second- and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement. There was 100% response from all three rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of > 0.8. There was 97.7% agreement that a standardised PPCRC would be advantageous to training and that, independent of speciality, there should be a common approach (95.5% agreement). Consensus was reached in multiple areas: 1. Experience and Exposure, 2. Access and context, 3. Curriculum Components, 4 Target Groups and Delivery, 5. Objective Metrics, Benchmarking and Assessment. Using the Delphi methodology, we achieved multispecialty consensus among trainees to develop and reach content validation for the requirements and components of a PPCRC. This guidance will benefit from further validation following implementation.

Identifying curriculum content for operating room nurses involved in robotic-assisted surgery: a Delphi study

BACKGROUND

Currently, no generally accepted curriculum for operating room nurses (OR nurses) working with robotic-assisted surgery (RAS) exists. OR nurses working with RAS require different competencies than regular OR nurses, e.g. knowledge of the robotic system and equipment and specific emergency undocking procedures. The objective of this study was to identify learning goals for a curriculum for OR nurses working with RAS and to investigate which learning methods should be used.

METHODS

A three-round Delphi approach, with an additional survey, was used in this study. Four OR nurses from every department in gynecology, urology, and surgical gastroenterology doing RAS in Denmark were invited to participate.

RESULTS

The response rates were 93%, 81%, and 79%, respectively, in the three rounds of the Delphi survey and 68% in the additional survey. After the processing of data, a list of 57 learning goals, sorted under 11 domains, was produced. 41 learning goals were rated Relevant, Very relevant, or Essential spread over 10 of the 11 domains. The top 3 learning goals rated as Essential: Identify the most common injuries related to patient positioning during robotic-assisted surgery and know how to avoid them, Connect, calibrate and handle the scope, Perform an emergency undocking procedure. The panel rated Supervised training during surgery on patients as the most relevant learning method, followed by Dry lab and Team training.

CONCLUSIONS

The learning goals identified in this study, can be used as the basis for a curriculum for OR nurses working with RAS. During the processing, it became clear that there is a need to further investigate issues such as communication challenges, awareness of emergency procedures, and differences in the skills required depending on the role of the RAS nurse.

Transferability of robotic console skills by early robotic surgeons: a multi-platform crossover trial of simulation training

Robotic surgical training is undergoing a period of transition now that new robotic operating platforms are entering clinical practice. As this occurs, training will need to be adapted to include strategies to train across various consoles. These new consoles differ in multiple ways, with some new vendors using flat screen open source 3D enhanced vision with glasses and differences in design will require surgeons to learn new skills. This process has parallels with aviation credentialling across different aircraft described as type rating. This study was designed to test the hypothesis that technical robotic console operating skills are transferrable across different robotic operating platforms. Ten participants sequentially completed four Mimic®(Surgical Science) simulation exercises on two different robotic operating platforms (DaVinci®, Intuitive Surgical and HUGO™ RAS, Medtronic). Ethical approval and informed consent were obtained for this study. Groups were balanced for key demographics including previous robotic simulator experience. Data for simulation metrics and time to proficiency were collected for each attempt at the simulated exercise and analysed. Qualitative feedback on multi-platform learning was sought via unstructured interviews and a questionnaire. Participants were divided into two groups of 5. Group 1 completed the simulation exercises on console A first then repeated these exercises on console B. Group 2 completed the simulated exercises on console B first then repeated these exercises on console A. Group 1 candidates adapted quicker to the second console and Group 2 candidates reached proficiency faster on the first console. Participants were slower on the second attempt of the final exercise regardless of their allocated group. Quality and efficiency metrics and risk and safety metrics were equivalent across consoles. The data from this investigation suggests that console operating skills are transferrable across different platforms. Overall risk and safety metrics are within acceptable limits regardless of the order of progression of console indicating that training can safely occur across multiple consoles contemporaneously. This data has implications for the design of training and certification as new platforms progress to market and supports a proficiency-based approach.