The findings are based on two studies conducted by Matthew Beane, PhD, project scientist from the University of California, Santa Barbara. The first was a two-year study comparing learning in robotic and traditional surgical practices. These findings were combined with a blinded interview-based study conducted at 13 teaching hospitals in the U.S.
Dr. Beane found traditional open surgery training methods were effective in teaching trainees how to become surgeons. However, robotic surgery techniques limited opportunities for trainees to help during surgery and get supervised experience.
As a result, trainees used what Dr. Beane called “shadow learning” practices, which included premature specialization in robotic surgery without competence in general surgery, among others. These practices kept all but “a minority of surgical trainees to come to competence.”
“Shadow learning practices were neither punished nor forbidden, and they contributed to significant and troubling outcomes for the cadre of initiate surgeons and the profession, including hyperspecialization and a decreasing supply of experts relative to demand,” Dr. Beane wrote.
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