Hugo RAS versus da Vinci Xi for robot-assisted simple hysterectomy in endometrial cancer: a concurrent-period comparison of operative efficiency, cost structure, and the cost of early adoption
In a concurrent-period analysis of robot-assisted simple hysterectomy for endometrial cancer, the Hugo RAS system demonstrated shorter operative times and lower consumable costs compared to the da Vinci Xi, though the performance gap narrowed significantly when early-adoption learning cases were excluded, highlighting the challenge of disentangling platform-specific efficiency from initial learning curves.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the operating room, the removal of the uterus is a cornerstone procedure for treating early-stage cancer of the womb. For decades, surgeons have moved from large, open incisions to minimally invasive techniques that use small holes and long instruments, a shift that generally helps patients recover faster. More recently, surgeons have begun using robotic systems to assist with these delicate tasks. These machines do not operate on their own; instead, a surgeon sits at a console, controlling robotic arms that hold the instruments inside the patient's body. The robots offer a magnified, three-dimensional view and instruments that can bend and rotate in ways human hands cannot, theoretically making complex movements easier and more precise. However, these machines are expensive to buy and maintain, and hospitals must decide if the benefits in speed and safety are worth the high price tag. When a new robotic system enters the market, it is difficult to judge its true performance because the first time a team uses it, they are also learning how to use it. This "learning curve" often makes a new machine look slower or less efficient simply because the team is inexperienced, not because the machine is flawed.
A team of researchers at Yamanashi Central Hospital in Japan set out to untangle this confusion by comparing two different robotic systems used for the same type of surgery. They looked at a specific group of patients with early-stage uterine cancer who underwent a simple removal of the uterus without the removal of lymph nodes, a procedure that keeps the surgery time relatively consistent. The hospital had been using the established da Vinci Xi system for several years before introducing a newer competitor called the Hugo RAS system in late 2023. To get a fair comparison, the researchers focused their main analysis on a specific window of time when both machines were being used at the same time by the same surgical teams. This approach allowed them to see how the two systems performed side-by-side, rather than comparing a new machine used by a learning team against an old machine used by a veteran team.
The study included 67 surgeries performed during this overlapping period, with 30 done on the new Hugo system and 37 on the established da Vinci system. The researchers found that, on average, the surgeries performed with the Hugo system took about 16 minutes longer than those performed with the da Vinci system. This difference was not just a small fluctuation; it was a consistent finding across the data. When the researchers looked closer at the timing, they saw that the extra time was spread across the entire process, from the moment the incision was made until the patient left the room, with the surgeon spending more time at the console and the team taking longer to prepare the patient. However, the researchers also noticed that the very first surgeries on the new machine seemed to take the most time. When they removed the first ten Hugo surgeries from their calculation to account for this initial learning phase, the time difference shrank to about six minutes, a gap that was no longer statistically significant. This suggests that part of the delay was indeed due to the team getting used to the new equipment, but a small difference in speed remained even after that initial adjustment.
Despite the longer operating time, the financial picture told a different story. The researchers analyzed the actual costs incurred by the hospital for each surgery, including the price of the disposable tools and instruments used during the operation. They discovered that the total cost to perform the surgery was lower with the Hugo system. Specifically, the cost of the disposable consumables was significantly cheaper, saving the hospital roughly 50,000 Japanese yen per case compared to the da Vinci system. Even though the surgery took longer, which usually means more staff time and higher labor costs, the savings on the instruments were large enough to make the overall procedure cheaper. The amount the hospital was reimbursed by the insurance system was essentially the same for both machines, meaning the hospital kept more profit when using the Hugo system, despite the longer time in the operating room.
The study also looked at whether the patients fared differently in terms of their cancer outcomes, but the data was too limited to draw firm conclusions. Because the new system had only been in use for a short time, the patients treated with it had not been followed for as long as those treated with the older system, making it impossible to compare long-term survival rates or recurrence of the disease with certainty. The researchers also noted that they could not fully assess safety complications like bleeding or infection because the hospital records did not capture those details in a way that allowed for a direct comparison.
Ultimately, the research highlights a complex trade-off that hospitals face when adopting new technology. The newer robotic system appeared to be slightly slower to use, at least in the hands of a team that was still gaining experience, but it was significantly cheaper to run because the tools cost less. The findings suggest that when evaluating a new surgical platform, it is not enough to look at just the speed of the surgery or just the price of the tools; one must look at how the learning curve affects the timeline and how the cost of the equipment interacts with the time spent in the operating room. The study concludes that while the new system may take a little longer to use initially, its lower cost structure could make it a viable alternative, provided that the surgical team continues to build experience and the hospital can manage the slightly longer time the patient spends in the operating room.
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