White paper on effective preacquisition evaluation of soft tissue robotic surgery platforms for healthcare institutions

Abstract

Background: With 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. Methods: Based 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. Results: Eight 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. Conclusion: The 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.