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.
The copy of record is available from the publisher at http://dx.doi.org/10.3402/meo.v21.32516. Copyright © 2016 Charles A. Gullo. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.
I would like to acknowledge Mathew Crutchfield our creative arts designer for assistance with Fig. 1. I would also like to thank Brian Dzwonek EdD, Director of US clinical placements, INTO University Partnerships for his editorial assistance with this article.