Impact of non-surgical OR time on efficiency and costs with Hugo™ RAS
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".