Impact of non-surgical OR time on efficiency and costs with Hugo™ RAS

Abstract

Robot-assisted surgery is increasingly preferred. New systems such as the Hugo™RAS enter the market, offering different pricing and modular architecture. While daVinci systems dominate U.S. practice, the HugoRAS system is undergoing early implementation. Understanding non-surgical, patient-independent time (NonSPIT)-a major driver of cost and efficiency-is crucial for institutions considering adoption. We conducted a prospective, single-center study in Germany, comparing perioperative times of robotic pelvic procedures (RPP) using newly introduced HugoRAS and preestablished daVinci Xi, and retrospectively of open pelvic procedures (OPP). NonSPIT, defined as setup, docking, undocking, and system cleanup, was recorded and analyzed with respect to personnel experience. The resulting costs were estimated. 167 RPP using HugoRAS (n = 144) and Xi (n = 23) were included and supplemented with 20 historical OPP. NonSPIT accounted for 40.0% (HugoRAS) and 36.7% (Xi) of total procedure time, compared to 31% in OPP. HugoRAS required 13 more minutes of NonSPIT than Xi (94.3 vs. 81.6 min; p < 0.05-OPP: 68.4 min). This overall disadvantage was nullified after 10-15 cases per scrub nurse. 77.1% of NonSPIT difference was caused by setup, where RPP showed longer preparation times than OPP (45.1 and 33.5 min vs. 27.4 min). HugoRAS setup was slower than Xi for unexperienced (48.0 vs. 33.5 min; p < 0.01) and experienced teams (44.5 vs. 33.5 min; p < 0.01). The projected additional time costs of NonSPIT in HugoRAS RPP amounts to $865.55 and $1325.95 per procedure compared to Xi and OPP, respectively. HugoRAS' cost of learning is estimated at $7017.87 per nurse until efficiency of an established Xi system is achieved. In the U.S. context, HugoRAS may initially be less time-efficient than daVinci Xi. However, this gap closes after approximately 15 procedures, leaving behind only a small disadvantage in system preparation and making HugoRAS an economical alternative-particularly given its lower per-case instrumentation costs.