The learning curves between team and individual surgeons
This study demonstrates the learning curve of both the team as a whole as well as individual surgeons. With an established-team and standardized teamwork, the learning curve of a newly joined but qualified member of the surgical team may be parallel with the other experienced members in the team. To the best of our knowledge, this could be the first study to show the learning curves of robotic spine surgery with quantificational and nonlinear analysis.
The power law of practice is well described quantitatively for human learning curve study in psychology (15). This law states that the logarithm of the reaction time for a particular task decreases linearly with the logarithm of the number of practices. Therefore, the time per screw is viewed as an important element of the learning curve during robotic spine surgery (4, 8). Our results, as the first study, demonstrated a similar power law of practice only on the learning curve of the first surgeon, who initiated the robotic spine surgery for the team. In the series by Urakov et al. (8), the initial academic experiences of residents/fellows and their learning curve with robotic spine instrumentation were explored. No significance was noted regarding the speed of pedicle instrumentation under senior surgeons’ guidance. According to the theory, results and observations, a well-established team, teamwork, and supervision are important factors for the learning curve of robotic spine surgery.
However, the characteristics of the smooth power law may potentially mask more complex dynamics underpinning individual learning curves. Therefore, using a single power law to predict or analyze individual performance may obscure more complex learning dynamics (16).
The accuracy of robotic spine surgery
The accuracy of spinal instrumentation is another important issue in robotic spine surgery. Previous studies have shown that the rate of successful robotic–assisted pedicle screw placement became consistent after 20 or 30 cases (6, 10). In our study, the rate of accuracy did not change significantly between the 3 groups of 50 cases during 3 specific time intervals. In a meta-analysis of robotic spine surgery by Joseph et al. (17), including 22 retrospective case series and prospective randomized trials, the consistency and high accuracy rates of robotic spine surgery were also recognized. Ringel et al. (3) also stated that accuracy did not improve through the course in their study. Obesity, osteoporosis, and congenital scoliosis have been recognized as risk factors for screw malposition and surgeons in the initial stage of using a robot are suggested to avoid performing surgery on patients with these risk factors (1).
One of our previous studies developed a secondary registration protocol that increased the success rate and intraoperative accuracy by the same robotic system (13). In the studies we published in 2016 and 2017 (13, 14) showed that the K-wire needed to be repositioned manually is 1.26% (4/317 K-wires, with secondary registration) and 0.15% (1/662 K-wires, with third registration). Factors influencing accuracy can be errors in preoperative planning, mounting, registration, drilling, or robot assembly (14). All of these factors could be eliminated or minimized by a well-established team and teamwork, according to the results of this study.
Potential roles of teamwork
Effective teamwork can be measured by examining the quality of output, the process and the members' performance (18). Team dynamics are important for efficient teamwork. Team dynamics include open communication to avoid conflicts, effective coordination to avoid confusion, efficient cooperation to perform the tasks in a timely manner and produce the required results, and high levels of interdependence to maintain high levels of trust, risk-taking, and performance (19). The smooth and parallel learning curves of the team and individuals imply the potential roles of teamwork in robotic spine surgery.
Communication and workplace culture are thought to be important factors in the human-robot team interaction (12), but the evaluation and measurements are usually difficult in complex operation rooms. In the surgery with the Renaissance robot-guided system, a potential error could take place during registration due to inconsistencies between the numbers of station number set on the computer and on the mounting platform. The “triple-check process” applied in robotic spine surgery, i.e. pointing, reading, and confirming all parameters of settings and instruments with team members in every step, prevents our team from errors. There is little literature related with human-robot interaction in the field of robotic spine surgery. Though we did not propose new experimental study design about the human-robot interaction in this study, the high accuracy of transpedicular screw implantation in this study demonstrates not only the efficiency of surgical robot but also the efficiency of the good human-robot interaction.
KMUH has been an academic medical center in southern Taiwan since the 1970s. It has received accreditation every three to five years by the official accreditation organizations in Taiwan. Furthermore, the partnership with the international accreditation organization also encourages and regulates the team to follow rules and guidelines more strictly.
We believed this is the first article emphasizing the importance of team building, human-robot interaction and workplace culture in robotic spine surgery.
Robotic spine Surgery – a system lack of active perception and unmet needs
Modeling and control strategies for perception, defining active perception (20), are missing in the robotic system we used for this study. According to our previous study, skiving over a steep slope of bony surface is the main factor affecting accuracy (14). However, during the process of drilling, there is no passive nor active perception process or mechanism provided. The members of surgical team use their naked eyes and fingers to detect possible skiving of guiding tube before, during, and after the drilling procedure. Contrary to intraoperative image-guided spinal navigation , there is no guiding image or visual feedback provided by the robotic system during the drilling procedure and screw placement (21). These defects are also barriers to surgeons to use or trust this robotic system. Intelligent control strategies according to the data from detecting possible deviation of guiding tube, are apparently the unmet needs for robotic spine surgery.