Selecting a winning team: Management of surgical team composition in robotic surgery

Abstract

Surgical robots can offer higher precision, flexibility, and control during surgeries compared to conventional approaches. Robotic surgeries lead to a decrease in surgical error rates, lengths of hospital stays, and patient recovery times. The effectiveness of robotic surgeries depends on many factors, including the individual performance of team members. Although the experience of every team member makes a difference, the performance in the operating room depends as well on the cohesion among surgical team members. In this study, we address a team composition problem in robotic surgery in which we evaluate the efficiency of an operating room by assessing individual and dependent performances of surgical team members. We build a two-stage stochastic programming model, where the team members’ performance values are stochastic, to decide on team compositions. We propose two easy-to-implement algorithms based on implementations of data analyses and a stochastic programming model to identify surgical team compositions where the resulting computational difficulties are addressed through the Lagrangian decomposition procedure. We also describe computational results based on actual historical data, which indicate that the operating room time and surgical team performances can be improved if the proposed policies are implemented. With the model developed, surgical team composition decisions can be made more systematically and effectively. We also highlight the importance of considering individual and dependent performances of all surgical team members on operating room time and overall team performance.