Introduction and Objective: 

Robot-Assisted Laparoscopic Prostatectomy (RALP) is a technically demanding procedure and difficult for novice trainees. Current simulation methods are expensive and have yet to achieve widespread adoption. Dry lab is a simple and affordable alternative. Our objective was to design and evaluate a novel low-cost, low-fidelity dry lab model for training and assessing proficiency in three specific RALP steps.

Materials and Methods: 

We created three standardized inanimate tasks to simulate the following radical prostatectomy steps: posterior dissection, neurovascular bundle release, and urethrovesical anastomosis (Fig 1).  Each task was completed by, medical students (MS, N = 5), junior residents (JR, N = 5), senior residents (SR, N = 5) and urology attendings (N = 7) at a single institution.  Task completion time was recorded, and task performance was evaluated by blinded graders using the Global Evaluative Assessment of Robotic Skills (GEARS) scores (Fig 2).  Surveys were distributed following the task to evaluate user experience (Fig 3). 

Results: The dry lab cost under US $25 to produce. There was a significant difference in task completion times and GEARS scores when comparing all participant cohorts.  Individual cohort differences are presented in Fig 2. The model was rated favorably in terms of technique replication and acceptability for use in residency training. However, most felt the model was dissimilar to human tissue (Fig 3).

Conclusions:

This low cost, procedure-specific dry lab is simple to produce and simulates RALP technique. It is and option to augment robotic surgery education.

Funding:

None