EP1022 - Development of an Analytics Platform for Robotic Surgery Education: Addressing the Gap in Data-Driven Resident Training

Purpose/Background: Robotic-assisted colorectal surgery has become essential to modern surgical training, yet standardized tools for longitudinal performance tracking and feedback remain sparse. Existing simulators provide task-level metrics but do not integrate individualized analytics, credential tracking, or PGY-specific progress visualization. We developed a novel mobile and web-based platform to address this gap and surveyed residents and faculty to assess perceived educational need and feasibility.

Methods/Interventions: A multidisciplinary team from Henry Ford Health and Michigan State University developed a secure, cloud-based system featuring a dashboard for trainees and a web interface for instructors. Core functionalities include real-time visualization of simulator performance, PGY-specific progress tracking, certificate management, and discussion-based feedback. To evaluate perceived utility, structured surveys were distributed to general surgery residents (PGY1-5) and attending surgeons. Questions assessed attitudes toward the importance of analytics-based feedback, progress visualization, and credential management.

Results/Outcomes: A mobile and web-based analytics platform was successfully developed to centralize robotic surgery education across the Henry Ford Health residency program. The system integrates real-time performance visualization, PGY-specific progress dashboards, credential tracking, and discussion-based feedback to streamline resident evaluation (Figure 1). Survey feedback from attendings (n = 10) and residents (n = 8) demonstrated strong enthusiasm for the platform's utility. Ninety percent of attendings rated a centralized tracking system as very or extremely valuable, and 80% agreed that analytics-based feedback would enhance trainee assessment and reduce administrative workload. Among residents, all respondents (100%) viewed the platform as valuable to their training, with 100% agreeing that automated performance summaries and milestone dashboards would improve self-directed learning. Both groups endorsed the platform's feasibility (≥90%), ease of integration into existing workflows, and its potential to enhance transparency and engagement in robotic training.

Conclusion/Discussion: Development of this platform represents a feasible and scalable approach to modernizing robotic surgery education. The high level of faculty and resident support underscores the educational value of integrating data-driven analytics into competency-based curricula. Future directions will incorporate multi-institutional validation and longitudinal performance tracking to assess impact on skill acquisition and training efficiency.

Overview of the robotic surgery analytics platform demonstrating integrated dashboards for performance tracking, credential management, and resident-faculty feedback.