Medical schools in the United States serve to train the next generation of physicians, admitting students who will continue to advance each school’s mission. Admission committees are tasked with identifying those candidates who will be successful academically and who promote the objectives of the school with respect to mission. The Quillen College of Medicine at East Tennessee State University in northeast Tennessee seeks to attract and retain physicians with an interest in rural and primary care medicine. A total of 630 students were included in this study representing classes from 2001 to 2011. This study examined admissions data including MCAT scores, undergraduate GPAs, admission interview scores, and admission committee rating scores along with USMLE Step 1 scores to determine if there is any correlation of these variables with graduates selecting a primary care career or a rural practice location. With respect to data available at admission, only MCAT scores were shown to have a significant correlation to specialty choice. None of the admission data significantly correlated with practice location. USMLE Step 1 scores had a weak negative relationship with specialty choice and a negligible relationship with practice location. This study provides the admission committee information that these variables are insufficient by themselves to predict whether a medical student applicant will select a primary care specialty or practice in a rural location. Other data, perhaps even subjective data, would need to be analyzed to predict how well the admissions committee is addressing the college’s mission with its selection of medical students.

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Identifying the factors associated with medical students who fail Step 1 of the United States Medical Licensing Examination (USMLE) has been a focus of investigation for many years. Some researchers believe lower scores on the Medical Colleges Admissions Test (MCAT) are the sole factor used to identify failure. Other researchers believe lower course outcomes during the first two years of medical training are better indicators of failure. Yet, there are medical students who fail Step 1 of the USMLE who enter medical school with high MCAT scores, and conversely medical students with lower academic credentials who are expected to have difficulty passing Step 1 but pass on the first attempt. Researchers have attempted to find the factors associated with Step 1 outcomes; however, there are two problems associated with their methods used. First is the small sample size due to the high national pass rate of Step 1. And second, research using multivariate regression models indicate correlates of Step 1 but does not predict individual student performance. This study used data mining methods to create models which predict medical students at risk of failing Step 1 of the USMLE. Predictor variables include those available to admissions committees at application time, and final grades in courses taken during the preclinical years of medical education. Models were trained, tested, and validated using a stepwise approach, adding predictor variables in the order of courses taken to identify the point during the medical education continuum which best predicts students who will fail Step 1. Oversampling techniques were employed to resolve the problem of small sample sizes. Results of this study suggest at risk medical students can be identified as early as the end of the first term during the first year. The approach used in this study can serve as a framework which if implemented at other U.S. allopathic medical schools can identify students in time for appropriate interventions to impact Step 1 outcomes