BUILDING AN ANALYTICAL MODEL TO PREDICT WORKFORCE OUTCOMES IN MEDICAL EDUCATION

Abstract

Across the United States, there is a shortage of physicians providing care in rural areas. This shortage means patients living in rural communities must travel further with fewer care options. The purpose of this study is to ultimately fill the gap in rural workforce outcomes by identifying students that are likely to practice in rural areas once they complete medical school and residency/fellowship programs. These students may be identified through use of predictive analytics techniques. By identifying these students, we can provide informational material and optional programs to further foster interest in rural care. Through techniques such as feature extraction, resampling, and data imputation, we prepare data for various machine learning classifiers. These models allow us to identify features common to urban providers and rural providers. Seventy percent of rural providers were correctly identified as practicing in rural areas, while 25% of their urban counterparts were classified as rural. One characteristic difference between the groups shows rural providers have high average scores through medical school courses, while urban providers have higher standardized test scores.