Using Discrete-Event Simulation to Analyze the Impact of Variation on Surgical Training Programs

In this paper, we use discrete-event simulation in an attempt to highlight the consequences of variability in surgical training. Under the current training model, case volume minimums are being used as a surrogate measure of a surgical trainee's competency for a given operation. However, this assumes that 1) learning is a binary measure, 2) there is no variability in training opportunities, and 3) all trainees learn at the same speed. Our model addresses these variables by allowing the user to manipulate the distribution of continuous learning curves and arrival rates, simulating the competency outcomes of a surgical training model. The results demonstrate that when increasing the variability in learning speeds or decreasing the training opportunities, competency outcomes for common procedures such as appendectomies remain relatively unaffected. However, for rarer procedures like mediastinoscopies, these variabilities result in a greater proportion of decreasingly competent trainees, potentially endangering patient safety.