The future is in the numbers: The power of predictive analysis in the biomedical educational environment

Abstract

Biomedical programs have a potential treasure trove of data they can mine to assist admissions committees in identification of students who are likely to do well and help educational committees in the identification of students who are likely to do poorly on standardized national exams and who may need remediation. In this article, we provide a step-by-step approach that schools can utilize to generate data that are useful when predicting the future performance of current students in any given program. We discuss the use of linear regression analysis as the means of generating that data and highlight some of the limitations. Finally, we lament on how the combination of these institution-specific data sets are not being fully utilized at the national level where these data could greatly assist programs at large.