Modeling the effect of resident learning curve in the emergency department



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Kansas State University


The University of Kansas Medical Center’s Emergency Department is adopting a new residency program. In the past, generalized Residents have supported attending physicians during a required three month rotation in the Emergency Department. As of July 2010, the University of Kansas Medical Center’s Emergency Department has switched to a dedicated Emergency Medicine Residency program that allows recently graduated physicians the opportunity enter the field of Emergency Medicine. This thesis shows that although not initially a dedicated residency program provides an advantage to the Emergency Department. Discrete Event Simulations have been used to predict changes in processes, policies, and practices in many different fields. The models run quickly, and can provide a basis for future actions without the cost of actually implementing changes in policies or procedures. This thesis applies a learning curve in a Simulation Model in order to provide data that the University of Kansas Medical Center’s Emergency Department can utilize to make decisions about their new Residency Program. A generalized learning curve was used for the base model and compared to all alternatives. When it was compared with an alternative curve following a Sigmoid Function (Logistic Function), there were no significant differences. Ultimately, a Gompertz Curve is suggested for hospitals attempting to develop or improve their residency programs using learning curves because it is easily fitted to their desired shape. This thesis shows the effect that Residents have on the performance of the Emergency Department as a whole. The two major components examined for the generalized learning curve were the initial position for first year residents determined by the variable [alpha], and the shape of the curve determined by the variable [beta]. Individual changes the value of [alpha] had little effect. Varying values of [beta] have shown that smaller values elongate the shape of the curve, prolonging the amount of time it takes for a resident to perform at the level of the attending physician. Each resident’s personal value of [beta] can be used to evaluate the performance in the emergency department. Resident’s who’s [beta] value are smaller the emergency department’s expected value might have trouble performing.



Learning curve, Emergency department, Residency program, Discrete event simulation

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Master of Science


Department of Industrial and Manufacturing Systems Engineering

Major Professor

Chih-Hang Wu