Developing a Predictive Model to Assess Applicants to an Internal Medicine Residency

Abstract

Background Residency programs strive to accurately assess applicants' qualifications and predict future performance. However, there is little evidence-based guidance on how to do this. The aim of this study was to design an algorithm for ranking applicants to an internal medicine residency program. Methods Ratings of overall performance in residency were compared to application characteristics of 230 graduating residents from 2000–2005. We analyzed 5 characteristics of the application: medical school, overall medical school performance, performance in junior medicine clerkship, United States Medical Licensing Examination (USMLE) Step 1 score, and interview ratings. Using bivariate correlations and multiple regression analysis, we calculated the association of each characteristic with mean performance ratings during residency. Results In multiple regression analysis, the most significant application factors (r² = 0.22) were the quality of the medical school and the applicant's overall performance in medical school (P < .001). Conclusion This data has allowed the creation of a weighted algorithm to rank applicants that uses 4 application factors—school quality, overall medical school performance, medicine performance, and USMLE Step 1 score.