ArticleSurgical endoscopy2026
White paper on effective preacquisition evaluation of soft tissue robotic surgery platforms for healthcare institutions.
Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Contract architecture and time-to-first public activation of robotic urology programs in Poland: a nationwide open-data linkage study.Journal of robotic surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the increasing popularity of robotic surgery among surgeons, the selection of soft tissue robotic surgery platforms represents a critical strategic decision for healthcare institutions. Following patent expirations in robotic technology, institutions face increasingly complex choices among diverse platforms with varying capabilities, costs, and implementation requirements. The goal of this white paper was to define a framework for evaluating robotic surgical platforms to aid acquisition decisions by healthcare institutions.
methodsBased on expert robotic surgeon opinion informed by the IDEAL Framework (Idea, Development, Exploration, Assessment, and Long-term study) and Multi-Criteria Decision Analysis (MCDA) methodology, and discussions with industry stakeholders, core decision domains for the acquisition of a robotic system were defined and a comprehensive framework for evaluating robotic surgical platforms was developed. The impact of procedure, specialty, and institution-specific variations of the selection criteria was explored.
resultsEight core decision domains were identified: clinical outcomes and patient relevance, hospital-specific considerations, technology-specific attributes, physician needs and preferences, surgeon and trainee education, economic implications, policy and regulatory factors, and long-term sustainability. According to this framework, financial sustainability timelines vary substantially based on case mix, payer composition, and implementation approach, with specialty-focused programs typically achieving positive returns more rapidly than broad multi-specialty implementations. Economic models illustrate different return-on-investment scenarios for academic medical centers, community hospitals, and specialty surgical centers. Educational infrastructure significantly impacts long-term program success, with platforms offering comprehensive simulation, dual-console training, and proficiency-based progression frameworks promoting enhanced adoption and utilization rates.
conclusionThe proposed framework provides institutions with a systematic approach to robotic platform selection designed to maximize clinical outcomes, operational efficiency, educational effectiveness, and financial sustainability, while avoiding common implementation pitfalls that have historically undermined robotic program success.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.