**5. Learning curve**

In 1885, German psychologist Herman Ebbinghaus described the concept of the learning curve, saying, "By a sufficient number of repetitions their final mastery is ensured [35]". In 1936, Wright endorsed the concept of the learning curve by hypothesizing that by increasing production one achieves perfection and, consequently, requires less time to produce aircrafts. Over 1,200 robotic programs have been established across the United States, with over 1,500 gynecologic surgeons being trained in the technology. Along with this training, there obviously comes a learning curve. This phenomenon is well-established with robotic surgery in all specialties, and multiple studies have been published to discuss the learning curve and minimum cases require to surpass the learning curve [36, 37]. The learning curve could be different for surgeons with advanced surgical skills [38] and variable for different portions of the same surgical procedure [39]. Acquisition and maintenance of a robotic program is a costly venture [16]. Not including initial acquisition, robotic hysterectomies cost roughly \$2000 more than laparoscopic hysterectomies. This increased cost difference is attributed to the cost of instruments (Intuitive surgical has restricted the number of instruments in use), the costs of operating room time, costs of staffing, costs of training, and costs of personal egos. Out of these, the learning curve certainly accounts for the costs of increased operating room time, costs of personal egos, costs of the number of instruments used, costs associated with complications, etc. Therefore, before adopting a robotic program, surgeons and hospital administration should have proper understanding of the phenomenon of 'the learning curve," and its implications on the balance sheet of the hospitals. Typically, the learning curve has been described as an S-curve or sigmoid shape (**Figure 2A**). The Y-axis represents learning, and the x-axis represents experience. Classical sigmoid behavior represents an initially slow, then rapid, and subsequently slow improvement [40]. In most medical studies of learning curves, the statistical approach discretizes cases into groups and uses standard statistical methods to compare the variables. This methodology provides the statistical significance values, but it is not always the optimal way to assess the learning curve which is a dynamic process in which improvement occurs on a case-to-case basis.

A sensitive way to portray surgical failures that are indicative of both the early learning curve and the post-learning curve is the cumulative sum failure analysis (CUSUM) [41–43]. This technique not only recognizes time as an important, hidden

**113**

a robotic program [44].

**Figure 2.**

**6. Business perspective and return on investment (ROI)**

The most important step in acquiring technology is the financial willingness of administration to invest in advanced technology. Therefore, understanding the business model associated with a robotic program is critical. Unlike other industries, the healthcare industry has not experienced a paradigm shift from long-term strategies to transient gain primarily due to the lengthy process that new medical and surgical advancements must undergo to be accepted as a new standard of care. To keep steady profits, companies employ many strategies. One of the strategies is to reduce costs by increasing production and providing the most cost-effective products to market. They often use the theory of "planned obsolescence" [45] by

*Present Challenges of Robotics in Gynecology DOI: http://dx.doi.org/10.5772/intechopen.96780*

variable in these studies, but it also prevents the decreased statistical significance that can sometimes accompany repeat testing. For these reasons, both the standard statistical method and cumulative sum analysis are recommended to fully assess new teams with accurate and objective feedback. The following formula is used to plot the cumulative sum curve: Sn = Σ(Xi–Xo) where Xi = 0 means success and Xi = 1 means failure. Xo represents the predicted risk of major adverse events. The X-axis portrays the number of cases, while the Y-axis represents the sum of failure. This is shown in the figure (**Figure 2B**). The line that trends above the baseline portrays the learning curve or a performance that does not meet expectations. Contrarily, the line trending toward or below the baseline portrays the performance that is improving or the post-learning curve, respectively. The line trending below the baseline and away from the baseline shows adequate experience or performance that is either better or equivocal. Examples of these graphs are represented in **Figures 2B**–**D**. **Figure 2A** shows the analysis of a hypothetical CUSUM analysis of any successful procedure as explained above. **Figure 2C** has a curve above and moving away from the baseline. This could represent an example of either an unsuccessful procedure or a surgeon not passing the learning curve. **Figure 2D** shows the curve representing either a surgeon with excellent skills from the beginning or having escaped the learning curve that happens when skillful laparoscopic surgeons start performing robotic cases. The assessment of learning not only plays a critical role in development of an effective robotics program to assess the initial learning curve, but it also provides continued monitoring by assessing the state of the learning curve of the entire division from time-to-time which is a critical part of

*Learning curve. A - S curve; B, C, D - different type of hypothetical CUSUM curve.*

*Present Challenges of Robotics in Gynecology DOI: http://dx.doi.org/10.5772/intechopen.96780*

*Latest Developments in Medical Robotics Systems*

with a strong sense of culture of safety.

**5. Learning curve**

nurses that error is a systemic problem and not an individual one. Their minds need to be trained not to think any less of their colleagues when they make errors. Second is 'litigation and regulatory barriers.' Unfortunately, regulatory boards and the court of law or peer review processes at hospitals again reinforce the idea of clinical perfection. Therefore, it is very difficult for nursing staff to deviate from the routine practice and adopt changes that come with new technology. The culture of safety will play a large role in the outcomes of robotic-assisted surgeries, and therefore, it is both necessary and vital to address the changes that come with the implementation of novel technology. To develop a successful robotic program, it is important to implement frequent reviews of outcomes, multidisciplinary discussions, development of parameter-based new postoperative care protocols, and consideration of recommendations and management strategies from all the team members. This is a crucial part of the process of building a gynecologic surgical robotic program, and it requires commitment from members at all levels in the health care delivery system

In 1885, German psychologist Herman Ebbinghaus described the concept of the learning curve, saying, "By a sufficient number of repetitions their final mastery is ensured [35]". In 1936, Wright endorsed the concept of the learning curve by hypothesizing that by increasing production one achieves perfection and, consequently, requires less time to produce aircrafts. Over 1,200 robotic programs have been established across the United States, with over 1,500 gynecologic surgeons being trained in the technology. Along with this training, there obviously comes a learning curve. This phenomenon is well-established with robotic surgery in all specialties, and multiple studies have been published to discuss the learning curve and minimum cases require to surpass the learning curve [36, 37]. The learning curve could be different for surgeons with advanced surgical skills [38] and variable for different portions of the same surgical procedure [39]. Acquisition and maintenance of a robotic program is a costly venture [16]. Not including initial acquisition, robotic hysterectomies cost roughly \$2000 more than laparoscopic hysterectomies. This increased cost difference is attributed to the cost of instruments (Intuitive surgical has restricted the number of instruments in use), the costs of operating room time, costs of staffing, costs of training, and costs of personal egos. Out of these, the learning curve certainly accounts for the costs of increased operating room time, costs of personal egos, costs of the number of instruments used, costs associated with complications, etc. Therefore, before adopting a robotic program, surgeons and hospital administration should have proper understanding of the phenomenon of 'the learning curve," and its implications on the balance sheet of the hospitals. Typically, the learning curve has been described as an S-curve or sigmoid shape (**Figure 2A**). The Y-axis represents learning, and the x-axis represents experience. Classical sigmoid behavior represents an initially slow, then rapid, and subsequently slow improvement [40]. In most medical studies of learning curves, the statistical approach discretizes cases into groups and uses standard statistical methods to compare the variables. This methodology provides the statistical significance values, but it is not always the optimal way to assess the learning curve which

is a dynamic process in which improvement occurs on a case-to-case basis.

A sensitive way to portray surgical failures that are indicative of both the early learning curve and the post-learning curve is the cumulative sum failure analysis (CUSUM) [41–43]. This technique not only recognizes time as an important, hidden

**112**

**Figure 2.** *Learning curve. A - S curve; B, C, D - different type of hypothetical CUSUM curve.*

variable in these studies, but it also prevents the decreased statistical significance that can sometimes accompany repeat testing. For these reasons, both the standard statistical method and cumulative sum analysis are recommended to fully assess new teams with accurate and objective feedback. The following formula is used to plot the cumulative sum curve: Sn = Σ(Xi–Xo) where Xi = 0 means success and Xi = 1 means failure. Xo represents the predicted risk of major adverse events. The X-axis portrays the number of cases, while the Y-axis represents the sum of failure. This is shown in the figure (**Figure 2B**). The line that trends above the baseline portrays the learning curve or a performance that does not meet expectations. Contrarily, the line trending toward or below the baseline portrays the performance that is improving or the post-learning curve, respectively. The line trending below the baseline and away from the baseline shows adequate experience or performance that is either better or equivocal. Examples of these graphs are represented in **Figures 2B**–**D**. **Figure 2A** shows the analysis of a hypothetical CUSUM analysis of any successful procedure as explained above. **Figure 2C** has a curve above and moving away from the baseline. This could represent an example of either an unsuccessful procedure or a surgeon not passing the learning curve. **Figure 2D** shows the curve representing either a surgeon with excellent skills from the beginning or having escaped the learning curve that happens when skillful laparoscopic surgeons start performing robotic cases. The assessment of learning not only plays a critical role in development of an effective robotics program to assess the initial learning curve, but it also provides continued monitoring by assessing the state of the learning curve of the entire division from time-to-time which is a critical part of a robotic program [44].
