During early adoption of robotic surgeries, evidence is primarily descriptive, and how such evidence emerges and accumulates over time remains poorly understood. This study introduces a framework for modeling the temporal dynamics of descriptive evidence on device-level utilities and operational issues accumulated during early adoption of the da Vinci system. We employed a three-step approach comprising systematic dataset acquisition, thematic coding of device utilities and operational issues, and quantitative temporal modeling. A PRISMA-guided search of PubMed and Web of Science identified early clinical studies of the da Vinci system from FDA clearance (July 2000) to the first published randomized controlled trial. Two reviewers independently coded descriptive themes, and cumulative occurrence proportions were modeled over time using exponential and logistic regression to characterize the emergence and saturation patterns. Nineteen studies met inclusion criteria, yielding 16 themes (7 utilities, 9 issues) with high inter-rater reliability (agreement rate 93.1%, Cohen's κ = 0.85). Utilities, particularly instrument dexterity and stereoscopic depth perception, were reported early and reached saturation rapidly, whereas operational issues, including loss of haptic feedback and workflow-related constraints, emerged more gradually and required greater cumulative clinical experience. Time-series modeling demonstrated a clear saturating pattern, with utilities reaching 80% cumulative occurrence at 13.2 months versus 26.0 months for issues. This study presents a framework for modeling how descriptive evidence emerges and matures in early-stage medical device adoption. The observed asymmetry, rapid recognition of utilities versus delayed emergence of operational issues, highlights the importance of structured, continuous synthesis of early clinical evidence.
Suzuki et al. (Wed,) studied this question.