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Png, Chan, and Ng: Has robotic surgery finally found robotic surgeons? The confluence of technology and the perfect user

Abstract

Objective:

Our study aims to identify traits of a surgeon that might determine facility with robotic surgery.

Methods

Several robotics lab sessions were organised for 129 medical students, where they were introduced to robotics surgery. Their demographic and self-reported data regarding their experience was collected.

Results

Our data suggests that traits commonly believed to facilitate robotic surgery skill acquisition, such as technological proficiency and playing musical instruments do not confer an advantage in robotic surgery.

Conclusion

One compelling explanation is the ubiquitous familiarity with technology that characterises this generation of surgeons. This ability to easily interface with digital technology to perform tasks is a more significant contributor to facility in robotic surgery than any trait of dexterity. These findings are interesting and inform the need for further investigation into the relationship between technology, the acquisition of dexterity, and ultimately the evolution of how surgeons are trained.

INTRODUCTION

When the idea of robotic surgery was first introduced in the late 1980s, it was felt to be a fad embraced by enthusiasts rather than serious surgeons [1]. Robotic surgery has since become a standard of care option with its use increasing exponentially from 1.8% in 2012 to 15.1% in 2018 across numerous surgical disciplines [2]. As with all technology, innovation continues to drive the evolution of robotic surgery promising broader access and better outcomes [3]. Much has been written about the learning curve of robotic surgery [4-8], but few studies have investigated whether any specific generational characteristics might contribute to differences in the learning curve [9,10]. As robotic surgery becomes ubiquitous, understanding what an ideal candidate for robotic surgery training might be would be important in optimising limited training resources and streamlining the medical workforce. Our study aims to investigate if any specific demographic traits confer an advantage in robotic surgery skill acquisition.

MATERIALS AND METHODS

We organised several robotics lab sessions in which 129 medical students were introduced to robotics surgery. We then collected their demographic data and self-reported data regarding their experience. The participants self-reported demographic data such as age, experience playing musical instruments, use of electronic devices, preferred referencing method, writing method, and surgery method. The participants self-reported their experience with the robotics vision system and controls, which we used as a proxy for measuring their aptitude for robotics surgery. The participants also provided feedback on the duration and utility of these sessions.
The frequency and percentages of the categorical data were tabulated and then analysed using the chi-square test with the significance level set at P<0.05. A logistics regression model was used to calculate an odds ratio for how various traits can predict the ease of navigating robotic controls.

RESULTS

The majority of the participants were aged 21–25 years old (n=109, 84.5%), played 1 or more musical instruments (n=94, 72.9%), and were digitally proficient (i.e., frequent users of electronic devices [n=75, 58.1%], comfortable with using digital platforms for referencing [n=119, 92.2%] and writing [n=119, 92.2%]) (Table 1). Over half of the participants preferred robotics surgery (n=67, 52.0%) (Table 1).
Most of the participants reported a positive experience with the robotics lab session (n=109, 84.5%) and had little difficulty navigating the vision system (n=97, 75.2%) and hand controls (n=100, 77.5%) (Table 2). More than half of the participants initially struggled with coordinating their hands and feet to control the robot but could do so after minor adjustments (n=78, 60.5%) (Table 2). The majority of participants rated the lab as useful (n=124, 96.1%) and that they would like more sessions in the future (n=122, 94.6%) (Table 2).
Overall, our results found no significant correlation between qualities such as technological proficiency (Table 3) or musical inclination (Table 4), and aptitude towards robotic surgery. Preference for online learning did not significantly influence participants’ ability to adapt to hand and feet controls (χ2=1.06, P=0.30) or the vision system (χ2=3.69, P=0.05) (Table 5). The only significant finding (P<0.05) showed that participants who preferred online learning were more adept at finger and hand controls (χ2=4.71, P=0.03) (Table 5). This is further evidenced by the logistics regression model demonstrating a strong relationship between preference for online learning and superior hand and finger control, with a statistically significant odds ratio (odds ratio, 3.96; 95% confidence interval, 1.03–15.3; P=0.041).

DISCUSSION

Our study supports the hypothesis that the current cohort of junior medical trainees (recent medical graduates) has a native advantage in robotic surgery compared to previous generations likely due to their general facility with technology. It is interesting to note that within this cohort, traits that intuitively might have been assumed to confer an advantage in acquiring robotic surgical skill sets have not been shown to confer any obvious advantage. It is more than likely that the ubiquity of familiarity and comfort with digital tools and environments contributes so significantly to robotic surgical skills acquisition that any contribution by traits of dexterity are small in comparison. These “digital natives” see technology as an organic and integral part of basic functioning, and are the result of having grown up in a society where smart devices connected to the Internet are taken as much for granted as electricity and running water were by their predecessors. Their reliance on technology to accomplish tasks is demonstrated by the significant association between a preference for online learning and better task performance with the robotic surgical controls. We therefore concluded that there is no particular trait or characteristic that significantly impacts aptitude toward robotics surgery.
The mindset of having instantaneous access to information and the ability to process multiple streams of live information is another defining trait of this generation of surgical learners. This ability to process and absorb multiple streams of information simultaneously, makes them the perfect candidates to lead the future of robotic surgery as more data is made available to the robotic surgeon, and artificial intelligence-based clinical decision support systems (CDSS) augment surgical decision making in real time. At no time in the history of surgery has more information been available to the surgeon and more data generated by each surgical procedure than now [11]. This generation of surgeons is particularly equipped to take on the challenge where surgery becomes the act of coordination rather than just a dextrous act, more so than previous generations of surgeons.
At the time of its introduction in the 1980s, some surgeons and medical professionals were wary of the steep learning curve associated with mastering the new technology that was robotic surgery. A research paper published in the Journal of Surgical Education in 2015 showed that less than 40% of the surgical residents who took part in the study intended to use surgical robotics in their future practice [12]. In comparison, 52% of our participants reported that robotic surgery would be their preferred surgical modality, which is concordant with our findings of the current generation being more technological savvy and thus being more comfortable with robotic surgery.
The physical aspect of surgery has always revolved around manual dexterity. The open approach to surgery was borne out of the necessity to create an operative field to accommodate the use of one’s hands. This concept was modified with the introduction of laparoscopy to avoid the morbidity of the open wound whilst attempting to adhere to the principles of tissue handling and anatomy from open surgery. Robotic surgical technology has combined the dexterity of open surgery with the ability to reproduce the physical vectoring of open surgery with the low physiologic impact of minimally invasive surgery (MIS). This ability to “do your open procedure minimally invasively” is largely responsible for the rapid adoption of robotic surgery [13]. The convergence of this format of surgery with this generation’s “digital nativeness” will help drive surgery toward precision, efficiency, access, and economy. The ultimate winner of this marriage parfait is the patient. Patients will have unprecedented access to the benefits of MIS.
A separate conclusion we drew from our study was that medical students may benefit from early exposure to robotic surgery. There is a steep learning curve associated with MIS [14], less so with contemporary robotic surgery where the technology compensates for the demanding operating environment of laparoscopy [10]. A multi-institutional study found that surgical residents who had previously completed a surgical boot camp felt more adequately equipped with surgical and technical skills than those who did not [15]. Currently, medical students have close to zero exposure to robotic surgery in their clinical years, with most graduating medical school without having ever seen a robotic surgery case [16]. Just as we provide exposure for all medical students to basic surgical technique with a forceps and needle driver, we need to look to the future where the surgical robot is a basic tool for these surgeons of the future. Early exposure makes the learning curve even more gradual and more accessible. There is certainly interest as our data suggests, where 96.1% of participants found the robotic surgery practice session useful (Table 2), and 94.6% of participants were keen on having more sessions of robotic surgery practice.

Limitations and suggestions

Our study is limited largely in part to the small sample size which could have led to the increased risk of sampling bias. This small sample size could be due in part to the limited robotics surgery resources available for medical students. Replicating this study with a larger sample size can reduce the sampling bias, and allow us to analyse more behavioural and demographic data that could confer an advantage in robotics surgery.
We acknowledge that our study relies heavily on the participants’ subjective, self-reported data regarding their ease of navigating robotic controls. Future studies can use standardised training exercises and objective measurements including time taken to complete a task, number of repetitions and established assessment scores to reduce information bias. This would better define what exactly aptitude for robotics surgery entails.

Conclusion

Robotic surgery is here to stay. The rise of robotic surgery can be traced to the evolution of the modern surgeon [13]. Data from our survey supports the view that the surgeon has evolved to become the perfect user and the ubiquity of this ease in interacting with technology has resulted in MIS being more accessible than ever to patients than any time in the history of medicine.
Our data also suggests that it is not only reasonable but important for medical students to be given early exposure to robotic surgery. In the context of contemporary medical education, early exposure to robotic surgery provides a key component of the holistic development of medical students. Inclusion of robotic surgery in medical curricula not only hones students' technical skills, enhancing proficiency with the robotic console, but also cultivates cognitive adaptability and a forward-thinking mindset essential for navigating the rapidly evolving landscape of modern healthcare. We are thankful for this opportunity to share an example of how Singapore continues to lead in the use of technology and how a small country’s healthcare continues to punch well above its weight in the world.

Notes

Conflict of interest

No potential conflict of interest relevant to this article was reported.

Table 1.
Participant demographic data (n=129)
Responses Value
Age (yr)
 15–20 20 (15.5)
 21–25 109 (84.5)
Number of musical instruments played
 0 35 (27.1)
 ≥1 94 (72.9)
Number of times electronic devices (i.e., smartphone, tablet, laptop, or desktop) is used
 1–10 15 (11.6)
 10–20 39 (30.2)
 >20 75 (58.1)
Preferred method of referencing
 Online (i.e., Google, PubMed, etc.) 119 (92.2)
 Computer-based library databases 7 (5.4)
 Library books, encyclopaedias, or magazines 3 (2.3)
Preferred method of writing
 Computer 119 (92.2)
 Writing by hand 8 (6.2)
 Typewriter 2 (1.6)
Statement: robotics surgery is a preferred method of performing surgery
 No (1–4) 62 (48.0)
 Yes (5–9) 67 (52.0)

Values are presented as number (%).

Table 2.
Participant self-reported experience (n=129)
Responses Value
Did the vision system seem natural to you?
 Very natural, no adjustment needed. 97 (75.2)
 Eyes needed time to adjust. 29 (22.5)
 Unnatural, could not adjust. 3 (2.3)
Did the finger and hand controls seem natural to you?
 Natural – could almost immediately make the instruments do what I wanted. 100 (77.5)
 Clumsy – spent most of my time figuring out how to make the instruments do what I wanted. 27 (20.9)
 Unnatural – could not figure out how to make the instruments do what I wanted. 2 (1.6)
Did you have any problems with using your hands and feet to control the robot?
 No problems – it was natural and easy. 45 (34.9)
 Some minor adjustments – after which it was easy. 78 (60.5)
 Could not get used to using hand and foot controls. 6 (4.7)
How did you feel about completing the assigned tasks with the robot?
 Positive 109 (84.5)
 Mixed 19 (14.7)
 Negative 1 (0.8)
Rate this lab on overall usefulness and utility to you.
 Not useful (1–4) 5 (3.9)
 Useful (5–9) 124 (96.1)
Would you like more sessions like these?
 Yes 122 (94.6)
 Unsure 6 (4.7)
 No 1 (0.8)

Values are presented as number (%).

Table 3.
Technological proficiency and self-reported experience with robotic controls crosstabulation (n=129)
Self-reported experience with robotic controls Less technologically proficienta) (n=54) More technologically proficientb) (n=75) χ2 P-value
Finger and hand controls 0.24 0.63
 Unnatural, clumsy 11 18
 Natural 43 57
Vision 1.97 0.16
 Unnatural, eyes needed time to adjust 10 22
 Natural 44 53
Hand and feet controls 0.47 0.49
 Could not get used to it, some minor adjustments needed 37 47
 Natural 17 28

a) Less technologically proficient is defined as using a smartphone, tablet, laptop or desktop less than 20 times a day, and preferring typing or writing out a report over using a computer.

b) More technologically proficient is defined as using a smartphone, tablet, laptop or desktop more than 20 times a day, and preferring using a computer to do a report over typing or writing.

Table 4.
Inclination to musical instruments and self-reported experience with robotic controls crosstabulation (n=129)
Self-reported experience with robotic controls Does not play musical instrumentsa) (n=35) Plays musical instrumentsb) (n=94) χ2 P-value
Finger and hand controls 0.29 0.59
 Unnatural, clumsy 9 20
 Natural 26 74
Vision 0.02 0.89
 Unnatural, eyes needed time to adjust 9 23
 Natural 26 71
Hand and feet controls 0.01 0.93
 Could not get used to it, some minor adjustments needed 23 61
 No problems 12 33

a) Not musically inclined is defined as 0 musical instruments played.

b) Musically inclined is defined as more than or equal to 1 musical instrument played.

Table 5.
Preference for online learning and self-reported experience with robotic controls crosstabulation (n=129)
Self-reported experience with robotic controls Do not prefer online learning (n=10) Prefer online learning (n=119) χ2 P-value
Finger and hand controls 4.71 0.03
 Unnatural, clumsy 5 24
 Natural 5 95
Vision 3.69 0.05
 Unnatural, eyes needed time to adjust 5 27
 Natural 5 92
Hand and feet controls 1.06 0.30
 Could not get used to it, some minor adjustments needed 8 76
 No problems 2 43

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