In his dissertation, Matthew Beane investigates the concept of productive deviance within surgical practices, focusing specifically on the integration of robotic surgery compared to traditional methods. Through a mixed-method study spanning 2.5 years, Beane demonstrates that practices challenging established norms—though tolerated—often lead to superior outcomes, especially in the early phases of significant technological transformations like robotic surgery. He highlights how surgical residents who engage in "shadow learning"—a set of deviant practices—can achieve proficiency with robotic techniques, albeit with unintended negative consequences, and he reveals the critical, yet undervalued, compensatory work that ensures patient safety when using unreliable robotic equipment.