Identifying Barriers to Resident Robotic Console Time in a General Surgery Residency Through a Targeted Needs Assessment

doi:10.21203/rs.3.rs-3137063/v1

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Identifying Barriers to Resident Robotic Console Time in a General Surgery Residency Through a Targeted Needs Assessment

https://doi.org/10.21203/rs.3.rs-3137063/v1

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published 17 Sep, 2023

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Robotic-assisted general surgery is experiencing exponential growth. Despite our institution’s high volume, residents often graduate with inadequate console experience. Our aim was to identify the educational needs of residents and perceived barriers to residents’ console time from both attendings and residents. Separate surveys were created and distributed to robotic surgery faculty and general surgery residents at our institution. Questions were a variety of modalities and focused on the robotic surgery experience at our institution, including barriers to resident console time from both attending surgeon and resident perspectives. Although residents' interest in robotic surgery exceeded that of open and laparoscopic surgery, confidence in their robotic skills was low compared to the other modalities. The top barriers to participating in robotic cases according to residents included minimal or no previous console time with the attending, lack of simulator time, and being required to perform bedside assistant duties. Faculty reported resident preparedness, prior robotic skill demonstration, simulator time, case complexity, and their own confidence as significant factors influencing resident console time. Using these results, we concluded that the design and implementation of a formal robotic surgery curriculum should incorporate simulation-based opportunities for residents to practice their skills, improve confidence, and increase console experience. In addition, simulation opportunities for faculty should also be considered to allow for improvement and maintenance of robotic surgical skills.

Robotic surgery

resident training

needs assessment

curriculum development

robotic simulation

Since the first robotic surgical system was implemented at Corewell Health East (formerly Beaumont Health) in 2002, the number and complexity of robotic cases performed have drastically increased. Our 8-hospital healthcare system has 24 da Vinci® (Intuitive Surgical, Sunnyvale, CA) surgical robots, and Corewell Health East - William Beaumont University Hospital (formerly Beaumont Health - Royal Oak) has recently surpassed the 20,000th robotic case, making it one of fewer than 20 hospitals in the United States to reach this milestone^1,2. Despite the high volume and breadth of complexity, our general surgery residents have had a subjectively limited and varied experience as robotic console surgeons during training. The purpose of this needs assessment is to assess barriers to robotic console time for general surgery residents from the resident and attending perspectives.

With an eye toward solutions, we employed Kern’s 6-step model for curriculum development to guide the development and implementation of this needs assessment³. For step 1, problem identification and general needs assessment, it was noted by residents that robotic console time varied during their training. For step 2, targeted needs assessment, separate surveys were distributed to general surgery residents and robotic surgery faculty to identify barriers to residents’ robotic console time.

We hypothesized that residents’ and attending surgeons’ perceptions of barriers to robotic console time differ. More specifically, we believed that residents equate less console time due to their faculty still gaining experience with robotic surgery themselves. Conversely, it was hypothesized that attending surgeons are less likely to hand over the role of console surgeon due to their perception that the resident does not have enough experience with the robot, either in the operating room or on a simulator console.

This study was approved by the Corewell Health Institutional Review Board (#2022 − 195).

Survey Instruments

Two surveys were developed by attending surgeons (NNo, AI, and FI), a medical education specialist (NNg), and a single surgery resident (KH). Surveys were anonymous, voluntary, and web-based. A 14-question survey was distributed to general surgery residency attending faculty who perform robotic surgery at our institution. A 13-question survey was distributed to general surgery residents of all PGY levels. Responses were collected from January-February of 2022. Reminder emails were sent periodically to improve response rate. No response data contained identifiers. The surveys utilized a combination of question modalities. Two versions of the instruments were created – one for attending surgeons and one for residents – to target specific barriers and perspectives for those groups.

The attending survey used binary (yes/no), multiple choice, Likert scale, and free text questions. Questions queried robotic operative volume, years in total practice and robotic practice, frequency of residents performing bedside assistant role in their cases, number of cases residents should bedside prior to being allowed console time, number of cases to achieve a feeling of ‘plateauing’ in robotic operative skills and teaching residents to operate robotically, importance of resident-modifiable and resident non-modifiable factors when deciding to give residents console time, and importance of verifiable simulator practice in allowing residents console time.

The resident survey used a similar distribution of question modalities to the faculty survey. Questions queried confidence in operative skills (open, laparoscopic, robotic), factors influencing decision not to participate in robotic cases, frequency of “fill-in” bedside assistant coverage for the robotic advanced practice provider (APP) team, interest in different surgical approaches (open, laparoscopic/thoracoscopic, robotic), time spent on a robotic simulator, frequency of participation in robotic cases, anticipated future practice pattern (open, laparoscopic/thoracoscopic, robotic, endovascular), number of cases required to achieve an acceptable comfort level as a bedside assistant, and typical degree of participation in a robotic inguinal hernia repair.

For both the faculty and resident surveys, additional demographic information (age, gender, PGY) were also collected.

Statistical Analysis

Statistical analysis was performed using the Qualtrics software and SPSS to calculate mean, median, mode, and standard deviation (where applicable).

Resident Survey

Surveys were sent to 37 residents in the six-year general surgery program at Corewell Health East - William Beaumont University Hospital, including two preliminary undesignated residents and two preliminary urology residents. Preliminary residents designated for interventional radiology, as well as other transitional year or rotating residents, were not included in the resident survey.

Demographics. A total of 20 (54%) residents completed the survey. The average age of responding residents was 30.9 years (range = 27–34 years). Fifteen (75%) of responding residents identified as male, and five (25%) identified as female. The response breakdown by PGY level (and percentage of total respondents) was as follows: PGY-1 = 3(15%), PGY-2 = 3 (15%), PGY-3 = 3 (15%), PGY-4 = 3 (15%), PGY-5 = 4 (20%), and PGY-6 = 4 (20%).

Interests in operative modalities. Residents were asked to rate their interest in various operative modalities (open, laparoscopic, and robotic) on a Likert scale of 1 to 5. The mean levels of interest for open, laparoscopic/thoracoscopic, and robotic surgery were 4.20 ± 0.87, 3.85 ± 0.57, and 4.30 ± 0.64 out of 5, respectively, shown in Fig. 1.

Confidence in operative skills. Residents rated their confidence in the various operative modalities on a Likert scale of 1 to 5. The mean levels of confidence in their open, laparoscopic, and robotic surgical skills were 2.95 ± 1.07, 2.65 ± 0.91, and 2.05 ± 0.86 out of 5, respectively, also shown in Fig. 1.

Robotic case - time spent and degree of participation. When asked the number of times per month they participated as console surgeon, seven (35%) residents reported four or more times, five (25%) residents spent between 1 to 3 times per month, and eight (40%) residents spent less than one time per month.

Barriers to scrubbing robotic cases. Residents were queried about different barriers to participating in robotic cases and were asked to rate the importance of the barrier on a Likert scale of 1 to 4. The respondent was also allowed to pick ‘not applicable’ to any barrier. According to residents, the top three barriers to scrubbing robotic cases were: ‘minimal or no console time with the attending’ (2.9 ± 1.09), ‘lack of simulator time’ (2.7 ± 1.05) and ‘being required to perform bedside assistant duties’ (2.6 ± 0.73), as shown in Fig. 2.

Robotic simulator time. Residents were given the opportunity to input a free text response detailing the number of hours weekly they would or do spend on a robotic simulator. Nine residents (45%) of residents reported spending no time on the simulator, while the remaining 11 (55%) residents estimated spending less than two hours per week (average time = 1.73 hrs; range = 1 to 3 hrs) practicing on the simulator.

Attending Survey

Demographics. Surveys were sent to 32 attending surgeons credentialed to perform robotic surgery at our institution. A total of ten (31%) attendings completed the survey. Seven (70%) attendings identified as male and three (30%) identified as female. The average years of practice for attendings = 10.3 ± 7.80 (range = 2–28 years), and the average years they have been operating robotically = 4.3 ± 2.0 (range = 1–7 years). The average number of robotic cases performed by attendings = 428.44 ± 669.39 (range = 6–2000; one respondent did not provide an estimate). Five (50%) attendings perform 0–2 robotic cases/week, three (30%) perform 3–5 robotic cases/week, one (10%) perform 6–10 cases/week, and one (10%) perform 11–15 robotic cases/week.

Learning/teaching robotic surgery. Attendings were asked to estimate the number of robotic cases it took them to reach the “plateau” phase on a skill curve for robotically operating on a moderately difficult case. One (10%) attending reported plateauing between 0–10 cases, one (10%) between 11–20 cases, three (30%) between 41–60 cases, and one (10%) between 61–100 cases. Two attending (20%) reported that they were not at the plateau phase, and one (10%) did not answer the question.

Similarly, attendings were asked to estimate the number of robotic cases it took them to reach the “plateau” phase for teaching residents how to operate robotically. Four (40%) attending reported plateauing between 11–20 cases, one (10%) between 41–60 cases, and three (30%) between 61–100 cases. One attending (10%) reported that they were not at the plateau phase, and one (10%) did not answer the question.

Factors influencing attendings’ decision to give residents console time. Attendings were polled about resident-modifiable and resident non-modifiable factors that influenced their decision to give residents time on the console during robotic cases. For both questions, attendings were asked to rate the importance of each factor’s influence on a Likert scale of 1–5.

For resident-modifiable factors, the most important factor was ‘resident perceived preparedness for the case’ (4.3 ± 1.0), followed closely by ‘prior demonstrated good robotic skills’ (4.2 ± 1.4), and ‘time spent on the Mimic/daVinci simulator’ (4.1 ± 0.83), as shown in Fig. 3.

For resident non-modifiable factors, the most impactful factor was ‘case complexity’ (4.5 ± 0.92) followed by ‘confidence in own robotic skills’ (4.4 ± 1.36), as shown in Fig. 4.

Access to Robotic Simulator. Attendings were asked to rate the likelihood of allowing residents more time on the console if they were able to track their simulator time. Specifically, they were queried on if a resident had verifiable increased time on a robotic simulator, how likely would it be that the faculty member would give a resident increased console time on a Likert scale of 1 to 5. One (10%) faculty member responded ‘extremely unlikely’, two (20%) responded ‘neutral’, seven (70%) responded ‘likely’ or ‘extremely likely’.

It is undeniable that robotic-assisted surgery will continue to grow in the field of general surgery. With both the increasing number of robotic systems and rapidly increasing volume of robotic-assisted cases being performed in the United States, general surgery residents’ interest in gaining proficiency in this operative modality has also increased. In 2003, Patel, et al. indicated that 57% of residents were interested in robotic surgery⁴. This number increased to 81% in 2015⁵. Our study shows that residents’ interest in robotic surgery exceeds both laparoscopic and open surgery. However, confidence in their robotic-assisted skills scored low compared to other operative modalities. This result suggests that more formal opportunities are needed to increase residents’ exposure to robotic surgery and subsequently improve proficiency and confidence in their robotic skills.

One significant barrier to robotic console time reported by both residents and attendings is insufficient robotic simulation time. Approximately half of our residents reported spending no time on the robotic simulator while the other half reported spending less than 2 hours per week on the simulator. At the time of conducting this needs assessment, the Mimic dVTrainer (dV-Trainer®) (Mimic Technologies, Inc., Seattle, WA) was the only robotic simulator available in our hospital system. The simulator, however, was often non-operational and required frequent maintenance and repair. As a result, our residents had limited access to a working simulator to practice their robotic skills. This major barrier to resident robotic console time could be addressed by adopting a working simulator and increasing residents' robotic simulation time. A recent study conducted by Imai et al. found that nearly all faculty and residents agreed that console simulation time should be an integral part of resident robotic skills training^6,7. We predict that increasing resident robotic simulation time will boost residents' confidence in their own robotic-assisted skills and increase attendings’ confidence that residents are able to practice skills outside of the OR. This, in turn, will increase resident console time during robotic cases.

When a new surgical modality is introduced into clinical practice, an attending surgeon’s confidence in performing the operation using the new modality is likely a barrier to resident education. This was first seen with the advent of laparoscopic surgery, where a previously junior-level case became one with a much steeper learning curve, leading to decreased resident experience^8,9. Similarly, an attending surgeon’s confidence in their own robotic skills is likely a barrier to resident console time^10,11. Our study confirmed that attending surgeons' own confidence in their robotic skills was a significant barrier to resident console time, second only to the complexity of the case itself. Thus, in addition to increasing robotic simulation time for residents, we recommend increasing robotic simulation time for attending surgeons, as well. A robust simulation program designed specifically to improve attending surgeons’ confidence in higher-complexity cases could be an excellent way to flatten the learning curve for faculty, with the added benefit for residents being increased console surgeon opportunities.

Several of the barriers investigated in this TNA could, to varying degrees, be addressed with the implementation of a simulation-based robotic surgery curriculum. Although curricula such as the Fundamentals of Robotic Surgery were created in the spirit of standardizing training, individual institutions are still responsible for creating and implementing their own robotic curricula^{6, 12–17}. Thus, we plan to use the barriers identified in our survey to tailor a curriculum to our institution’s greatest needs. While multiple aspects of a robotic curriculum such as an industry-sponsored didactic curriculum and graduated levels of responsibility during robotic cases, are important, we believe that the incorporation of simulation-based training opportunities should be a cornerstone of a future robotic curriculum. The simulation experience (or lack thereof) at our institution was highlighted by both our residents and attending surgeons in our needs assessment. This desire for a strong simulation component is also reflected in the sentiments of residents and attendings polled by Imai, et al. as well as other investigations related to robotic surgery curriculum development^{6,15, 18–20}.

While there are several relatively minor limitations to be addressed in our study, we feel the most significant limitation was that of sample size. Although we did receive quality responses from those attendings and residents that answered the survey, the total number of responses was a relatively low proportion of the population at our institution.

We used the Kern model to identify the needs of our residents and attendings in the context of our institution’s robotic surgery experience. We initially hypothesized that our TNA would successfully identify one or more barriers to resident console experience from the perspective of both residents and attendings, and several resident modifiable and non-modifiable barriers were demonstrated. These barriers centered principally on (the lack of) simulation experience. Residents’ high interest and low confidence in the robotic surgery experience, along with the aforementioned barriers, validate the need for the implementation of a formal robotic curriculum at our institution. This curriculum should incorporate simulation opportunities for residents, but there are potential opportunities for faculty to improve their skills as well. Moving forward, we hope to use this TNA and the Kern model to develop a high-quality, multi-faceted robotic curriculum that will improve the training of general surgery residents and the overall robotic experience at our institution.

Acknowledgments

The work reported in this publication represents part of the first author’s scholarly activities during his tenure as a Healthcare Simulation Fellow, Applebaum Simulation Learning Institute, Corewell Health East William Beaumont University Hospital. The authors would like to acknowledge the Consortium of American College of Surgeons Accredited Education Institutes for supporting the Healthcare Simulation Fellowship Program.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflicts of interest

All authors have no conflicts of interest to disclose.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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No competing interests reported.

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You are reading this latest preprint version

Identifying Barriers to Resident Robotic Console Time in a General Surgery Residency Through a Targeted Needs Assessment

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Abstract

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Introduction

Materials and Methods

Survey Instruments

Statistical Analysis

Results

Resident Survey

Attending Survey

Discussion

Conclusion

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References

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